Publications

@article{pmid35687390,

title = {Feedback on Trunk Movements From an Electronic Game to Improve Postural Balance in People With Nonspecific Low Back Pain: Pilot Randomized Controlled Trial},

author = {Anita Meinke and Rick Peters and Ruud H Knols and Jaap Swanenburg and Walter Karlen},

doi = {10.2196/31685},

issn = {2291-9279},

year  = {2022},

date = {2022-06-01},

journal = {JMIR Serious Games},

volume = {10},

number = {2},

pages = {e31685},

abstract = {BACKGROUND: Postural balance is compromised in people with low back pain, possibly by changes in motor control of the trunk. Augmenting exercising interventions with sensor-based feedback on trunk posture and movements might improve postural balance in people with low back pain.



OBJECTIVE: We hypothesized that exercising with feedback on trunk movements reduces sway in anterior-posterior direction during quiet standing in people with low back pain. Secondary outcomes were lumbar spine and hip movement assessed during box lift and waiter bow tasks, as well as participant-reported outcomes. Adherence to the exercising intervention was also examined.



METHODS: A randomized controlled trial was conducted with the intervention group receiving unsupervised home exercises with visual feedback using the Valedo Home, an exergame based on 2 inertial measurement units. The control group received no intervention. Outcomes were recorded by blinded staff during 4 visits (T1-T4) at University Hospital Zurich. The intervention group performed 9 sessions of 20 minutes in the 3 weeks between T2 and T3 and were instructed to exercise at their own convenience between T3 and T4. Postural balance was assessed on a force platform. Lumbar spine and hip angles were obtained from 3 inertial measurement units. The assessments included pain intensity, disability, quality of life, and fear of movement questionnaires.



RESULTS: A total of 32 participants with nonspecific low back pain completed the first assessment T1, and 27 (84%) participants were randomized at T2 (n=14, 52% control and n=13, 48% intervention). Intention-to-treat analysis revealed no significant difference in change in anterior-posterior sway direction during the intervention period with a specified schedule (T2-T3) between the groups (W=99; P=.36; r=0.07). None of the outcomes showed significant change in accordance with our hypotheses. The intervention group completed a median of 61% (55/90; range 2%-99%) of the exercises in the predefined training program. Adherence was higher in the first intervention period with a specified schedule.



CONCLUSIONS: The intervention had no significant effect on postural balance or other outcomes, but the wide range of adherence and a limited sample size challenged the robustness of these conclusions. Future work should increase focus on improving adherence to digital interventions.



TRIAL REGISTRATION: ClinicalTrials.gov NCT04364243; https://clinicaltrials.gov/ct2/show/NCT04364243.



INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/26982.},

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@article{pmid35332406,

title = {Assessment of neonatal respiratory rate variability},

author = {Jesse Coleman and Amy Sarah Ginsburg and William M Macharia and Roseline Ochieng and Dorothy Chomba and Guohai Zhou and Dustin Dunsmuir and Walter Karlen and J Mark Ansermino},

doi = {10.1007/s10877-022-00840-2},

issn = {1573-2614},

year  = {2022},

date = {2022-03-01},

journal = {J Clin Monit Comput},

abstract = {Accurate measurement of respiratory rate (RR) in neonates is challenging due to high neonatal RR variability (RRV). There is growing evidence that RRV measurement could inform and guide neonatal care. We sought to quantify neonatal RRV during a clinical study in which we compared multiparameter continuous physiological monitoring (MCPM) devices. Measurements of capnography-recorded exhaled carbon dioxide across 60-s epochs were collected from neonates admitted to the neonatal unit at Aga Khan University-Nairobi hospital. Breaths were manually counted from capnograms and using an automated signal detection algorithm which also calculated mean and median RR for each epoch. Outcome measures were between- and within-neonate RRV, between- and within-epoch RRV, and 95% limits of agreement, bias, and root-mean-square deviation. Twenty-seven neonates were included, with 130 epochs analysed. Mean manual breath count (MBC) was 48 breaths per minute. Median RRV ranged from 11.5% (interquartile range (IQR) 6.8-18.9%) to 28.1% (IQR 23.5-36.7%). Bias and limits of agreement for MBC vs algorithm-derived breath count, MBC vs algorithm-derived median breath rate, MBC vs algorithm-derived mean breath rate were - 0.5 (- 2.7, 1.66), - 3.16 (- 12.12, 5.8), and - 3.99 (- 11.3, 3.32), respectively. The marked RRV highlights the challenge of performing accurate RR measurements in neonates. More research is required to optimize the use of RRV to improve care. When evaluating MCPM devices, accuracy thresholds should be less stringent in newborns due to increased RRV. Lastly, median RR, which discounts the impact of extreme outliers, may be more reflective of the underlying physiological control of breathing.},

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@article{pmid35185455,

title = {Boosting Recovery During Sleep by Means of Auditory Stimulation},

author = {Elena Krugliakova and Jelena Skorucak and Georgia Sousouri and Sven Leach and Sophia Snipes and Maria Laura Ferster and Giulia Da Poian and Walter Karlen and Reto Huber},

doi = {10.3389/fnins.2022.755958},

issn = {1662-4548},

year  = {2022},

date = {2022-01-01},

journal = {Front Neurosci},

volume = {16},

pages = {755958},

abstract = {Sufficient recovery during sleep is the basis of physical and psychological well-being. Understanding the physiological mechanisms underlying this restorative function is essential for developing novel approaches to promote recovery during sleep. Phase-targeted auditory stimulation (PTAS) is an increasingly popular technique for boosting the key electrophysiological marker of recovery during sleep, slow-wave activity (SWA, 1-4 Hz EEG power). However, it is unknown whether PTAS induces physiological sleep. In this study, we demonstrate that, when applied during deep sleep, PTAS accelerates SWA decline across the night which is associated with an overnight improvement in attentional performance. Thus, we provide evidence that PTAS enhances physiological sleep and demonstrate under which conditions this occurs most efficiently. These findings will be important for future translation into clinical populations suffering from insufficient recovery during sleep.},

keywords = {},

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@article{pmid35259094,

title = {Benchmarking Real-Time Algorithms for In-Phase Auditory Stimulation of Low Amplitude Slow Waves With Wearable EEG Devices During Sleep},

author = {Maria Laura Ferster and Giulia Da Poian and Kiran Menachery and Simon J Schreiner and Caroline Lustenberger and Angelina Maric and Reto Huber and Christian R Baumann and Walter Karlen},

doi = {10.1109/TBME.2022.3157468},

issn = {1558-2531},

year  = {2022},

date = {2022-01-01},

journal = {IEEE Trans Biomed Eng},

volume = {69},

number = {9},

pages = {2916--2925},

abstract = {OBJECTIVE: In-phase stimulation of EEG slow waves (SW) during deep sleep has shown to improve cognitive function. SW enhancement is particularly desirable in subjects with low-amplitude SW such as older adults or patients suffering from neurodegeneration. However, existing algorithms to estimate the up-phase of EEG suffer from a poor phase accuracy at low amplitudes and when SW frequencies are not constant.



METHODS: We introduce two novel algorithms for real-time EEG phase estimation on autonomous wearable devices, a phase-locked loop (PLL) and, for the first time, a phase vocoder (PV). We compared these phase tracking algorithms with a simple amplitude threshold approach. The optimized algorithms were benchmarked for phase accuracy, the capacity to estimate phase at SW amplitudes between 20 and 60 μV, and SW frequencies above 1 Hz on 324 home-based recordings from healthy older adults and Parkinson disease (PD) patients. Furthermore, the algorithms were implemented on a wearable device and the computational efficiency and the performance was evaluated in simulation and with a PD patient.



RESULTS: All three algorithms delivered more than 70% of the stimulation triggers during the SW up-phase. The PV showed the highest capacity on targeting low-amplitude SW and SW with frequencies above 1 Hz. The hardware testing revealed that both PV and PLL have marginal impact on microcontroller load, while the efficiency of the PV was 4% lower. Active stimulation did not influence the phase tracking.



CONCLUSION: This work demonstrated that phase-accurate auditory stimulation can also be delivered during fully remote sleep interventions in populations with low-amplitude SW.},

keywords = {},

pubstate = {published},

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}

@article{pmid35603302,

title = {Auditory deep sleep stimulation in older adults at home: a randomized crossover trial},

author = {Caroline Lustenberger and M Laura Ferster and Stephanie Huwiler and Luzius Brogli and Esther Werth and Reto Huber and Walter Karlen},

doi = {10.1038/s43856-022-00096-6},

issn = {2730-664X},

year  = {2022},

date = {2022-01-01},

journal = {Commun Med (Lond)},

volume = {2},

pages = {30},

abstract = {Background: Auditory stimulation has emerged as a promising tool to enhance non-invasively sleep slow waves, deep sleep brain oscillations that are tightly linked to sleep restoration and are diminished with age. While auditory stimulation showed a beneficial effect in lab-based studies, it remains unclear whether this stimulation approach could translate to real-life settings.



Methods: We present a fully remote, randomized, cross-over trial in healthy adults aged 62-78 years (clinicaltrials.gov: NCT03420677). We assessed slow wave activity as the primary outcome and sleep architecture and daily functions, e.g., vigilance and mood as secondary outcomes, after a two-week mobile auditory slow wave stimulation period and a two-week Sham period, interleaved with a two-week washout period. Participants were randomized in terms of which intervention condition will take place first using a blocked design to guarantee balance. Participants and experimenters performing the assessments were blinded to the condition.



Results: Out of 33 enrolled and screened participants, we report data of 16 participants that received identical intervention. We demonstrate a robust and significant enhancement of slow wave activity on the group-level based on two different auditory stimulation approaches with minor effects on sleep architecture and daily functions. We further highlight the existence of pronounced inter- and intra-individual differences in the slow wave response to auditory stimulation and establish predictions thereof.



Conclusions: While slow wave enhancement in healthy older adults is possible in fully remote settings, pronounced inter-individual differences in the response to auditory stimulation exist. Novel personalization solutions are needed to address these differences and our findings will guide future designs to effectively deliver auditory sleep stimulations using wearable technology.},

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@article{Frohlich2020,

title = {Injury risks among elite competitive alpine skiers are underestimated if not registered prospectively, over the entire season and regardless of whether requiring medical attention},

author = {Stefan Fröhlich and Moritz Helbling and Sandro F Fucentese and Walter Karlen and Walter O Frey and Jörg Spörri},

url = {https://doi.org/10.1007/s00167-020-06110-5 http://link.springer.com/10.1007/s00167-020-06110-5 https://link.springer.com/10.1007/s00167-020-06110-5},

doi = {10.1007/s00167-020-06110-5},

issn = {0942-2056},

year  = {2021},

date = {2021-05-01},

journal = {Knee Surgery, Sports Traumatology, Arthroscopy},

volume = {29},

number = {5},

pages = {1635--1643},

publisher = {Springer Berlin Heidelberg},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Schwab2020a,

title = {A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data},

author = {Patrick Schwab and Walter Karlen},

url = {http://arxiv.org/abs/2001.09748 https://ieeexplore.ieee.org/document/9184949/},

doi = {10.1109/JBHI.2020.3021143},

issn = {2168-2194},

year  = {2021},

date = {2021-04-01},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {25},

number = {4},

pages = {1284--1291},

abstract = {Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involves a combination of complex clinical assessments and tests to rule out other diseases with similar symptoms. New technologies, such as smartphone monitoring in free-living conditions, could potentially aid in objectively assessing the symptoms of MS by quantifying symptom presence and intensity over long periods of time. Here, we present a deep-learning approach to diagnosing MS from smartphone-derived digital biomarkers that uses a novel combination of a multilayer perceptron with neural soft attention to improve learning of patterns in long-term smartphone monitoring data. Using data from a cohort of 774 participants, we demonstrate that our deep-learning models are able to distinguish between people with and without MS with an area under the receiver operating characteristic curve of 0.88 (95% CI: 0.70, 0.88). Our experimental results indicate that digital biomarkers derived from smartphone data could in the future be used as additional diagnostic criteria for MS.},

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}

@article{pmid33931049,

title = {Ethics review of big data research: What should stay and what should be reformed?},

author = {Agata Ferretti and Marcello Ienca and Mark Sheehan and Alessandro Blasimme and Edward S Dove and Bobbie Farsides and Phoebe Friesen and Jeff Kahn and Walter Karlen and Peter Kleist and S Matthew Liao and Camille Nebeker and Gabrielle Samuel and Mahsa Shabani and Minerva Rivas Velarde and Effy Vayena},

doi = {10.1186/s12910-021-00616-4},

issn = {1472-6939},

year  = {2021},

date = {2021-01-01},

journal = {BMC Med Ethics},

volume = {22},

number = {1},

pages = {51},

abstract = {BACKGROUND: Ethics review is the process of assessing the ethics of research involving humans. The Ethics Review Committee (ERC) is the key oversight mechanism designated to ensure ethics review. Whether or not this governance mechanism is still fit for purpose in the data-driven research context remains a debated issue among research ethics experts.



MAIN TEXT: In this article, we seek to address this issue in a twofold manner. First, we review the strengths and weaknesses of ERCs in ensuring ethical oversight. Second, we map these strengths and weaknesses onto specific challenges raised by big data research. We distinguish two categories of potential weakness. The first category concerns persistent weaknesses, i.e., those which are not specific to big data research, but may be exacerbated by it. The second category concerns novel weaknesses, i.e., those which are created by and inherent to big data projects. Within this second category, we further distinguish between purview weaknesses related to the ERC's scope (e.g., how big data projects may evade ERC review) and functional weaknesses, related to the ERC's way of operating. Based on this analysis, we propose reforms aimed at improving the oversight capacity of ERCs in the era of big data science.



CONCLUSIONS: We believe the oversight mechanism could benefit from these reforms because they will help to overcome data-intensive research challenges and consequently benefit research at large.},

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pubstate = {published},

tppubtype = {article}

}

@article{Scebba2020,

title = {Multispectral Video Fusion for Non-Contact Monitoring of Respiratory Rate and Apnea},

author = {Gaetano Scebba and Giulia {Da Poian} and Walter Karlen},

url = {https://ieeexplore.ieee.org/document/9091089/},

doi = {10.1109/TBME.2020.2993649},

issn = {0018-9294},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Transactions on Biomedical Engineering},

volume = {68},

number = {1},

pages = {350--9},

abstract = {Continuous monitoring of respiratory activity is desirable in many clinical applications to detect respiratory events. Non-contact monitoring of respiration can be achieved with near- and far-infrared spectrum cameras. However, current technologies are not sufficiently robust to be used in clinical applications. For example, they fail to estimate an accurate respiratory rate (RR) during apnea. We present a novel algorithm based on multispectral data fusion that aims at estimating RR also during apnea. The algorithm independently addresses the RR estimation and apnea detection tasks. Respiratory information is extracted from multiple sources and fed into an RR estimator and an apnea detector whose results are fused into a final respiratory activity estimation. We evaluated the system retrospectively using data from 30 healthy adults who performed diverse controlled breathing tasks while lying supine in a dark room and reproduced central and obstructive apneic events. Combining multiple respiratory information from multispectral cameras improved the root mean square error (RMSE) accuracy of the RR estimation from up to 4.64 monospectral data down to 1.60 breaths/min. The median F1 scores for classifying obstructive (0.75 to 0.86) and central apnea (0.75 to 0.93) also improved. Furthermore, the independent consideration of apnea detection led to a more robust system (RMSE of 4.44 vs. 7.96 breaths/min). Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications.},

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tppubtype = {article}

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@article{Leach2020,

title = {A Protocol for Comparing Dry and Wet EEG Electrodes During Sleep},

author = {Sven Leach and Ku-young Chung and Laura Tüshaus and Reto Huber and Walter Karlen},

url = {https://www.frontiersin.org/article/10.3389/fnins.2020.00586/full},

doi = {10.3389/fnins.2020.00586},

issn = {1662-453X},

year  = {2020},

date = {2020-06-01},

journal = {Frontiers in Neuroscience},

volume = {14},

pages = {586},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Schwab2020b,

title = {Learning Counterfactual Representations for Estimating Individual Dose-Response Curves},

author = {Patrick Schwab and Lorenz Linhardt and Stefan Bauer and Joachim M Buhmann and Walter Karlen},

url = {http://arxiv.org/abs/1902.00981 https://aaai.org/ojs/index.php/AAAI/article/view/6014},

doi = {10.1609/aaai.v34i04.6014},

issn = {2374-3468},

year  = {2020},

date = {2020-04-01},

journal = {Proceedings of the AAAI Conference on Artificial Intelligence},

volume = {34},

number = {04},

pages = {5612--5619},

publisher = {AAAI Press},

address = {New York},

abstract = {Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are either focused on estimating average dose-response curves, or limited to settings with only two treatments that do not have an associated dosage parameter. Here, we present a novel machine-learning approach towards learning counterfactual representations for estimating individual dose-response curves for any number of treatments with continuous dosage parameters with neural networks. Building on the established potential outcomes framework, we introduce performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual dose-response curves. Our experiments show that the methods developed in this work set a new state-of-the-art in estimating individual dose-response.},

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@article{Schwab2020,

title = {Learning Counterfactual Representations for Estimating Individual Dose-Response Curves},

author = {Patrick Schwab and Lorenz Linhardt and Stefan Bauer and Joachim M Buhmann and Walter Karlen},

url = {http://arxiv.org/abs/1902.00981 https://aaai.org/ojs/index.php/AAAI/article/view/6014},

doi = {10.1609/aaai.v34i04.6014},

issn = {2374-3468},

year  = {2020},

date = {2020-04-01},

journal = {Proceedings of the AAAI Conference on Artificial Intelligence},

volume = {34},

number = {04},

pages = {5612--5619},

publisher = {AAAI Press},

address = {New York},

abstract = {Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are either focused on estimating average dose-response curves, or limited to settings with only two treatments that do not have an associated dosage parameter. Here, we present a novel machine-learning approach towards learning counterfactual representations for estimating individual dose-response curves for any number of treatments with continuous dosage parameters with neural networks. Building on the established potential outcomes framework, we introduce performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual dose-response curves. Our experiments show that the methods developed in this work set a new state-of-the-art in estimating individual dose-response.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Behar2020,

title = {Remote health diagnosis and monitoring in the time of COVID-19},

author = {Joachim A Behar and Chengyu Liu and Kevin Kotzen and Kenta Tsutsui and Valentina D A Corino and Janmajay Singh and Marco A F Pimentel and Philip Warrick and Sebastian Zaunseder and Fernando Andreotti and David Sebag and Georgy Kopanitsa and Patrick E McSharry and Walter Karlen and Chandan Karmakar and Gari D Clifford},

url = {https://iopscience.iop.org/article/10.1088/1361-6579/abba0a},

doi = {10.1088/1361-6579/abba0a},

issn = {1361-6579},

year  = {2020},

date = {2020-01-01},

journal = {Physiological Measurement},

volume = {41},

number = {10},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ding2020,

title = {Pulse transit time based respiratory rate estimation with singular spectrum analysis},

author = {Xiao-Rong Ding and Bryan P Yan and Walter Karlen and Yuan-Ting Zhang and Hon Ki Tsang},

url = {http://link.springer.com/10.1007/s11517-019-02088-6},

doi = {10.1007/s11517-019-02088-6},

issn = {0140-0118},

year  = {2020},

date = {2020-01-01},

journal = {Medical & Biological Engineering & Computing},

volume = {58},

number = {2},

pages = {257--266},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Huser2019,

title = {Forecasting intracranial hypertension using multi-scale waveform metrics},

author = {Matthias Hüser and Adrian Kündig and Walter Karlen and Valeria {De Luca} and Martin Jaggi},

url = {http://arxiv.org/abs/1902.09499 https://iopscience.iop.org/article/10.1088/1361-6579/ab6360},

doi = {10.1088/1361-6579/ab6360},

issn = {1361-6579},

year  = {2020},

date = {2020-01-01},

journal = {Physiological Measurement},

volume = {41},

number = {1},

pages = {014001},

abstract = {Objective: Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively and time is lost before counteractive measures are taken. A pro-active approach that predicts critical events several hours ahead of time could be beneficial for the patient. Methods: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours. It jointly uses cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state of the patient. One-minute base windows were compressed by computing signal metrics, and then stored in a multi-scale history, from which physiological features were derived. Results: Our model predicted events up to 8 hours in advance with alarm recall rates of 90% at a precision of 36% in the MIMIC-II waveform database, improving upon two baselines from the literature. We found that features derived from high-frequency waveforms substantially improved the prediction performance over simple statistical summaries of low-frequency time series, and each of the three feature classes contributed to the performance gain. The inclusion of long-term history up to 8 hours was especially important. Conclusion: Our approach showed promising performance and enabled us to gain insights about the critical components of the prediction model. Significance: Our results highlight the importance of information contained in high-frequency waveforms in the neurological intensive care unit. They could motivate future studies on pre-hypertensive patterns and the design of new alarm algorithms for critical events in the injured brain.},

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}

@article{Muroi2019,

title = {Automated False Alarm Reduction in a Real-Life Intensive Care Setting Using Motion Detection},

author = {Carl Muroi and Sando Meier and Valeria {De Luca} and David J Mack and Christian Strässle and Patrick Schwab and Walter Karlen and Emanuela Keller},

url = {http://link.springer.com/10.1007/s12028-019-00711-w},

doi = {10.1007/s12028-019-00711-w},

issn = {1541-6933},

year  = {2020},

date = {2020-01-01},

journal = {Neurocritical Care},

volume = {32},

number = {2},

pages = {419--426},

abstract = {Background: Contemporary monitoring systems are sensitive to motion artifacts and cause an excess of false alarms. This results in alarm fatigue and hazardous alarm desensitization. To reduce the number of false alarms, we developed and validated a novel algorithm to classify alarms, based on automatic motion detection in videos. Methods: We considered alarms generated by the following continuously measured parameters: arterial oxygen saturation, systolic blood pressure, mean blood pressure, heart rate, and mean intracranial pressure. The movements of the patient and in his/her surroundings were monitored by a camera situated at the ceiling. Using the algorithm, alarms were classified into RED (true), ORANGE (possibly false), and GREEN alarms (false, i.e., artifact). Alarms were reclassified by blinded clinicians. The performance was evaluated using confusion matrices. Results: A total of 2349 alarms from 45 patients were reclassified. For RED alarms, sensitivity was high (87.0%) and specificity was low (29.6%) for all parameters. As the sensitivities and specificities for RED and GREEN alarms are interrelated, the opposite was observed for GREEN alarms, i.e., low sensitivity (30.2%) and high specificity (87.2%). As RED alarms should not be missed, even at the expense of false positives, the performance was acceptable. The low sensitivity for GREEN alarms is acceptable, as it is not harmful to tag a GREEN alarm as RED/ORANGE. It still contributes to alarm reduction. However, a 12.8% false-positive rate for GREEN alarms is critical. Conclusions: The proposed system is a step forward toward alarm reduction; however, implementation of additional layers, such as signal curve analysis, multiple parameter correlation analysis and/or more sophisticated video-based analytics are needed for improvement.},

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pubstate = {published},

tppubtype = {article}

}

@article{StellaMHartinger2020a,

title = {A factorial cluster-randomised controlled trial combining home-environmental and early child development interventions to improve child health and development: rationale, trial design and baseline findings.},

author = {Stella M Hartinger and Nestor Nu{ñ}o and Jan Hattendorf and Hector Verastegui and Walter Karlen and Mariela Ortiz and Daniel Mäusezahl},

url = {http://www.ncbi.nlm.nih.gov/pubmed/32241260 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC7115072},

doi = {10.1186/s12874-020-00950-y},

issn = {1471-2288},

year  = {2020},

date = {2020-01-01},

journal = {BMC medical research methodology},

volume = {20},

number = {1},

pages = {73},

publisher = {BMC Medical Research Methodology},

abstract = {BACKGROUND Exposure to unhealthy environments and inadequate child stimulation are main risk factors that affect children's health and wellbeing in low- and middle-income countries. Interventions that simultaneously address several risk factors at the household level have great potential to reduce these negative effects. We present the design and baseline findings of a cluster-randomised controlled trial to evaluate the impact of an integrated home-environmental intervention package and an early child development programme to improve diarrhoea, acute respiratory infections and childhood developmental outcomes in children under 36 months of age living in resource-limited rural Andean Peru. METHODS We collected baseline data on children's developmental performance, health status and demography as well as microbial contamination in drinking water. In a sub-sample of households, we measured indoor kitchen 24-h air concentration levels of carbon monoxide (CO) and fine particulate matter (PM2.5) and CO for personal exposure. RESULTS We recruited and randomised 317 children from 40 community-clusters to four study arms. At baseline, all arms had similar health and demographic characteristics, and the developmental status of children was comparable between arms. The analysis revealed that more than 25% of mothers completed primary education, a large proportion of children were stunted and diarrhoea prevalence was above 18%. Fifty-two percent of drinking water samples tested positive for thermo-tolerant coliforms and the occurrence of E.coli was evenly distributed between arms. The mean levels of kitchen PM2.5 and CO concentrations were 213 $mu$g/m3 and 4.8 ppm, respectively. CONCLUSIONS The trial arms are balanced with respect to most baseline characteristics, such as household air and water pollution, and child development. These results ensure the possible estimation of the trial effectiveness. This trial will yield valuable information for assessing synergic, rational and cost-effective benefits of the combination of home-based interventions. TRIAL REGISTRY ISRCTN-26548981.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhou2018,

title = {A transformation of oxygen saturation (the saturation virtual shunt) to improve clinical prediction model calibration and interpretation},

author = {Guohai Zhou and Walter Karlen and Rollin Brant and Matthew Wiens and Niranjan Kissoon and Mark J Ansermino},

url = {http://www.nature.com/articles/s41390-019-0525-2 https://www.biorxiv.org/content/10.1101/391292v3},

doi = {10.1038/s41390-019-0525-2},

issn = {0031-3998},

year  = {2019},

date = {2019-08-01},

journal = {Pediatric Research},

pages = {3--10},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Schwab2018e,

title = {PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data},

author = {Patrick Schwab and Walter Karlen},

url = {https://arxiv.org/abs/1810.01485 https://aaai.org/ojs/index.php/AAAI/article/view/3904},

doi = {10.1609/aaai.v33i01.33011118},

issn = {2374-3468},

year  = {2019},

date = {2019-07-01},

journal = {Proceedings of the AAAI Conference on Artificial Intelligence},

volume = {33},

pages = {1118--25},

publisher = {AAAI Press},

address = {Honolulu, HI, USA},

abstract = {Parkinson's disease is a neurodegenerative disease that can affect a person's movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson's disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson's disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson's disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson's disease.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Schwab2018eb,

title = {PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data},

author = {Patrick Schwab and Walter Karlen},

url = {https://arxiv.org/abs/1810.01485 https://aaai.org/ojs/index.php/AAAI/article/view/3904},

doi = {10.1609/aaai.v33i01.33011118},

issn = {2374-3468},

year  = {2019},

date = {2019-07-01},

journal = {Proceedings of the AAAI Conference on Artificial Intelligence},

volume = {33},

pages = {1118--25},

publisher = {AAAI Press},

address = {Honolulu, HI, USA},

abstract = {Parkinson's disease is a neurodegenerative disease that can affect a person's movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson's disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson's disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson's disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson's disease.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Schwab2018,

title = {Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks},

author = {Patrick Schwab and Djordje Miladinovic and Walter Karlen},

url = {http://arxiv.org/abs/1802.02195 https://aaai.org/ojs/index.php/AAAI/article/view/4412},

doi = {10.1609/aaai.v33i01.33014846},

issn = {2374-3468},

year  = {2019},

date = {2019-07-01},

journal = {Proceedings of the AAAI Conference on Artificial Intelligence},

volume = {33},

pages = {4846--53},

publisher = {AAAI Press},

address = {Honolulu, HI, USA},

abstract = {Knowledge of the importance of input features towards decisions made by machine-learning models is essential to increase our understanding of both the models and the underlying data. Here, we present a new approach to estimating feature importance with neural networks based on the idea of distributing the features of interest among experts in an attentive mixture of experts (AME). AMEs use attentive gating networks trained with a Granger-causal objective to learn to jointly produce accurate predictions as well as estimates of feature importance in a single model. Our experiments show (i) that the feature importance estimates provided by AMEs compare favourably to those provided by state-of-theart methods, (ii) that AMEs are significantly faster at estimating feature importance than existing methods, and (iii) that the associations discovered by AMEs are consistent with those reported by domain experts.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ferster2019,

title = {Configurable Mobile System for Autonomous High-Quality Sleep Monitoring and Closed-Loop Acoustic Stimulation},

author = {Maria Laura Ferster and Caroline Lustenberger and Walter Karlen},

url = {https://ieeexplore.ieee.org/document/8703847/},

doi = {10.1109/LSENS.2019.2914425},

issn = {2475-1472},

year  = {2019},

date = {2019-01-01},

journal = {IEEE Sensors Letters},

volume = {3},

number = {5},

pages = {1--4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ferster2019b,

title = {Configurable Mobile System for Autonomous High-Quality Sleep Monitoring and Closed-Loop Acoustic Stimulation},

author = {Maria Laura Ferster and Caroline Lustenberger and Walter Karlen},

url = {https://ieeexplore.ieee.org/document/8703847/},

doi = {10.1109/LSENS.2019.2914425},

issn = {2475-1472},

year  = {2019},

date = {2019-01-01},

journal = {IEEE Sensors Letters},

volume = {3},

number = {5},

pages = {1--4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tushaus2019,

title = {Physiologically driven, altitude-adaptive model for the interpretation of pediatric oxygen saturation at altitudes above 2,000 m a.s.l.},

author = {Laura Tüshaus and Monica Moreo and Jia Zhang and Stella Maria Hartinger and Daniel Mäusezahl and Walter Karlen},

url = {http://www.ncbi.nlm.nih.gov/pubmed/31525318 https://www.physiology.org/doi/10.1152/japplphysiol.00478.2018},

doi = {10.1152/japplphysiol.00478.2018},

issn = {8750-7587},

year  = {2019},

date = {2019-01-01},

journal = {Journal of Applied Physiology},

volume = {127},

number = {3},

pages = {847--57},

abstract = {Measuring peripheral oxygen saturation (SpO2) with pulse oximeters at the point of care is widely established. However, since [Formula: see text] is dependent on ambient atmospheric pressure, the distribution of SpO2 values in populations living above 2000 m a.s.l. is largely unknown. Here, we propose and evaluate a computer model to predict SpO2 values for pediatric permanent residents living between 0 and 4,000 m a.s.l. Based on a sensitivity analysis of oxygen transport parameters, we created an altitude-adaptive SpO2 model that takes physiological adaptation of permanent residents into account. From this model, we derived an altitude-adaptive abnormal SpO2 threshold using patient parameters from literature. We compared the obtained model and threshold against a previously proposed threshold derived statistically from data and two empirical data sets independently recorded from Peruvian children living at altitudes up to 4,100 m a.s.l. Our model followed the trends of empirical data, with the empirical data having a narrower healthy SpO2 range below 2,000 m a.s.l. but the medians never differed more than 2.3% across all altitudes. Our threshold estimated abnormal [Formula: see text] in only 17 out of 5,981 (0.3%) healthy recordings, whereas the statistical threshold returned 95 (1.6%) recordings outside the healthy range. The strength of our parametrized model is that it is rooted in physiology-derived equations and enables customization. Furthermore, as it provides a reference SpO2, it could assist practitioners in interpreting SpO2 values for diagnosis, prognosis, and oxygen administration at higher altitudes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pham2018d,

title = {Immuno-gold silver staining assays on capillary-driven microfluidics for the detection of malaria antigens.},

author = {Ngoc M Pham and Sebastian Rusch and Yuksel Temiz and Hans-Peter Beck and Walter Karlen and Emmanuel Delamarche},

url = {http://www.ncbi.nlm.nih.gov/pubmed/30810808},

doi = {10.1007/s10544-019-0376-y},

issn = {1572-8781},

year  = {2019},

date = {2019-01-01},

journal = {Biomedical microdevices},

volume = {21},

number = {1},

pages = {24},

abstract = {Accurate and affordable rapid diagnostic tests (RDTs) are indispensable but often lacking for many infectious diseases. Specifically, there is a lack of highly sensitive malaria RDTs that can detect low antigen concentration at the onset of infection. Here, we present a strategy to improve the sensitivity of malaria RDTs by using capillary-driven microfluidic chips and combining sandwich immunoassays with electroless silver staining. We used 5 $mu$m fluorescent beads functionalized with capture antibodies (cAbs). These beads are self-assembled by capillary action in recessed "bead lanes", which cross the main flow path of chips microfabricated in Si and SU-8. The binding of analytes to detection antibodies (dAbs) and secondary antibodies (2ndAbs) conjugated to gold nanoparticles (NPs) allows the formation of a silver film on the beads. Such silver film masks the fluorescent core of the bead inversely proportional to the concentration of antigen in a sample. We illustrate this method using the recombinant malaria antigen Plasmodium falciparum histidine-rich-protein 2 (rPfHRP2) spiked in human serum. This antigen was a recombinant HRP2 protein expressed in Escherichia coli, which is also the standard reference material. The limit of detection (LOD) of our immunoassay was found to be less than 6 ng mL-1 of rPfHRP2 within 20 min, which is approaching the desired sensitivity needed in the Target Product Profile (TPP) for malaria elimination settings. The concept presented here is flexible and may also be utilized for implementing fluorescence immunoassays for the parallel detection of biomarkers on capillary-driven microfluidic chips.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Azza2019,

title = {How stress affects sleep and mental health: Nocturnal heartrate increases during prolonged stress and interacts with childhood trauma exposure to predict anxiety.},

author = {Yasmine Azza and Marcus Grueschow and Walter Karlen and Erich Seifritz and Birgit Kleim},

url = {http://www.ncbi.nlm.nih.gov/pubmed/31863098 https://academic.oup.com/sleep/advance-article/doi/10.1093/sleep/zsz310/5682806},

doi = {10.1093/sleep/zsz310},

issn = {1550-9109},

year  = {2019},

date = {2019-01-01},

journal = {Sleep},

abstract = {STUDY OBJECTIVES Stress can adversely impact sleep health by eliciting arousal increase and a cascade of endocrine reactions that may impair sleep. To date, little is known regarding continuous effects of real-world stress on physiological sleep characteristics and potential effects on stress-related psychopathology. We examined effects of stress on heart-rate (HR) during sleep and total sleep time (TST) during prolonged real-world stress exposure in medical interns. Moreover, we investigated the influence of previous stress and childhood trauma exposure on HR during sleep, TST, and its interaction in predicting anxiety. METHODS We examined a sample of 50 medical students prior to and during their first internship, a well described real-world stressor. Heartrate and total sleep time were continuously collected over 12 weeks non-invasively by a wrist-worn activity monitor. Prior to starting the internship, at baseline, participants reported on their sleep, anxiety and childhood trauma exposure. They also tracked stress exposure during internship and reported on their anxiety symptoms after 3 months after this professional stress. RESULTS Mean HR during sleep increased over time, while TST remained unchanged. This effect was more pronounced in interns exposed to childhood trauma exposure. In multilevel models, childhood trauma exposure also moderated the relation between individual HR increase and development of anxiety. CONCLUSIONS Prolonged stress may lead to increased HR during sleep, whereas individuals with childhood trauma exposure are more vulnerable. childhood trauma exposure also moderated the relation between individual HR increase and development of anxiety. These findings may inform prevention and intervention measures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cherbuin2018,

title = {Quantifying cyanide in water and foodstuff using corrin-based CyanoKit technologies and a smartphone},

author = {Mathias Cherbuin and Felix Zelder and Walter Karlen},

url = {http://www.ncbi.nlm.nih.gov/pubmed/30460362 http://xlink.rsc.org/?DOI=C8AN01059E},

doi = {10.1039/C8AN01059E},

issn = {0003-2654},

year  = {2019},

date = {2019-01-01},

journal = {The Analyst},

volume = {144},

number = {1},

pages = {130--136},

abstract = {This paper describes the detection of endogenous cyanide using corrin-based CyanoKit technologies in combination with a smartphone readout device.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhang2018b,

title = {Data Integrity–Based Methodology and Checklist for Identifying Implementation Risks of Physiological Sensing in Mobile Health Projects: Quantitative and Qualitative Analysis},

author = {Jia Zhang and Laura Tüshaus and Néstor {Nu{ñ}o Martínez} and Monica Moreo and Hector Verastegui and Stella M Hartinger and Daniel Mäusezahl and Walter Karlen},

url = {http://mhealth.jmir.org/2018/12/e11896/},

doi = {10.2196/11896},

issn = {2291-5222},

year  = {2018},

date = {2018-12-01},

journal = {JMIR mHealth and uHealth},

volume = {6},

number = {12},

pages = {e11896},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pham2018a,

title = {A bead-based immunogold-silver staining assay on capillary-driven microfluidics.},

author = {Ngoc M Pham and Sebastian Rusch and Yuksel Temiz and Robert D Lovchik and Hans-Peter Beck and Walter Karlen and Emmanuel Delamarche},

url = {http://link.springer.com/10.1007/s10544-018-0284-6 http://www.ncbi.nlm.nih.gov/pubmed/29781041},

doi = {10.1007/s10544-018-0284-6},

issn = {1572-8781},

year  = {2018},

date = {2018-05-01},

journal = {Biomedical microdevices},

volume = {20},

number = {2},

pages = {41},

publisher = {Biomedical Microdevices},

abstract = {Point-of-care (POC) diagnostics are critically needed for the detection of infectious diseases, particularly in remote settings where accurate and appropriate diagnosis can save lives. However, it is difficult to implement immunoassays, and specifically immunoassays relying on signal amplification using silver staining, into POC diagnostic devices. Effective immobilization of antibodies in such devices is another challenge. Here, we present strategies for immobilizing capture antibodies (cAbs) in capillary-driven microfluidic chips and implementing a gold-catalyzed silver staining reaction. We illustrate these strategies using a species/anti-species immunoassay and the capillary assembly of fluorescent microbeads functionalized with cAbs in "bead lanes", which are engraved in microfluidic chips. The microfluidic chips are fabricated in silicon (Si) and sealed with a dry film resist. Rabbit IgG antibodies in samples are captured on the beads and bound by detection antibodies (dAbs) conjugated to gold nanoparticles. The gold nanoparticles catalyze the formation of a metallic film of silver, which attenuates fluorescence from the beads in an analyte-concentration dependent manner. The performance of these immunoassays was found comparable to that of assays performed in 96 well microtiter plates using "classical" enzyme-linked immunosorbent assay (ELISA). The proof-of-concept method developed here can detect 24.6 ng mL-1 of rabbit IgG antibodies in PBS within 20 min, in comparison to 17.1 ng mL-1 of the same antibodies using a ~140-min-long ELISA protocol. Furthermore, the concept presented here is flexible and necessitate volumes of samples and reagents in the range of just a few microliters.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Schwab2018f,

title = {Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks},

author = {Patrick Schwab and Lorenz Linhardt and Walter Karlen},

url = {http://arxiv.org/abs/1810.00656},

year  = {2018},

date = {2018-01-01},

journal = {ArXiv Preprint},

abstract = {Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Counterfactual inference enables one to answer "What if...?" questions, such as "What would be the outcome if we gave this patient treatment $t_1$?". However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatment options, or both. Here, we present Perfect Match (PM), a method for training neural networks for counterfactual inference that is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. PM is based on the idea of augmenting samples within a minibatch with their propensity-matched nearest neighbours. Our experiments demonstrate that PM outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes across several real-world and semi-synthetic datasets.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kammerer2018,

title = {Changes of hemodynamic and cerebral oxygenation after exercise in normobaric and hypobaric hypoxia: associations with acute mountain sickness.},

author = {Tobias Kammerer and Valentina Faihs and Nikolai Hulde and Andreas Bayer and Max Hübner and Florian Brettner and Walter Karlen and Julia Maria Kröpfl and Markus Rehm and Christina Spengler and Simon Thomas Schäfer},

url = {https://aoemj.biomedcentral.com/articles/10.1186/s40557-018-0276-2 http://www.ncbi.nlm.nih.gov/pubmed/30479778 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC6245893},

doi = {10.1186/s40557-018-0276-2},

issn = {2052-4374},

year  = {2018},

date = {2018-01-01},

journal = {Annals of occupational and environmental medicine},

volume = {30},

number = {1},

pages = {66},

publisher = {Annals of Occupational and Environmental Medicine},

abstract = {Objective Normobaric (NH) and hypobaric hypoxia (HH) are associated with acute mountain sickness (AMS) and cognitive dysfunction. Only few variables, like heart-rate-variability, are correlated with AMS. However, prediction of AMS remains difficult. We therefore designed an expedition-study with healthy volunteers in NH/HH to investigate additional non-invasive hemodynamic variables associated with AMS. Methods Eleven healthy subjects were examined in NH (FiO2 13.1%; equivalent of 3.883 m a.s.l; duration 4 h) and HH (3.883 m a.s.l.; duration 24 h) before and after an exercise of 120 min. Changes in parameters of electrical cardiometry (cardiac index (CI), left-ventricular ejection time (LVET), stroke volume (SV), index of contractility (ICON)), near-infrared spectroscopy (cerebral oxygenation, rScO2), Lake-Louise-Score (LLS) and cognitive function tests were assessed. One-Way-ANOVA, Wilcoxon matched-pairs test, Spearman's-correlation-analysis and Student's t-test were performed. Results HH increased heart rate (HR), mean arterial pressure (MAP) and CI and decreased LVET, SV and ICON, whereas NH increased HR and decreased LVET. In both NH and HH cerebral oxygenation decreased and LLS increased significantly. After 24 h in HH, 6 of 11 subjects (54.6%) developed AMS. LLS remained increased until 24 h in HH, whereas cognitive function remained unaltered. In HH, HR and LLS were inversely correlated (r = - 0.692; p textless 0.05). More importantly, the rScO2-decrease after exercise in NH significantly correlated with LLS after 24 h in HH (r = - 0.971; p textless 0.01) and rScO2 correlated significantly with HR (r = 0.802; p textless 0.01), CI (r = 0.682; p textless 0.05) and SV (r = 0.709; p textless 0.05) after exercise in HH. Conclusions Both acute NH and HH altered hemodynamic and cerebral oxygenation and induced AMS. Subjects, who adapted their CI had higher rScO2 and lower LLS. Furthermore, rScO2 after exercise under normobaric conditions was associated with AMS at high altitudes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pham2018c,

title = {Malaria and the ‘last' parasite: how can technology help?},

author = {Ngoc Minh Pham and Walter Karlen and Hans-Peter Beck and Emmanuel Delamarche},

url = {https://malariajournal.biomedcentral.com/articles/10.1186/s12936-018-2408-0},

doi = {10.1186/s12936-018-2408-0},

issn = {1475-2875},

year  = {2018},

date = {2018-01-01},

journal = {Malaria Journal},

volume = {17},

number = {1},

pages = {260},

publisher = {BioMed Central},

abstract = {Malaria, together with HIV/AIDS, tuberculosis and hepatitis are the four most deadly infectious diseases globally. Progress in eliminating malaria has saved millions of lives, but also creates new challenges in detecting the ‘last para‑ site'. Effective and accurate detection of malaria infections, both in symptomatic and asymptomatic individuals are needed. In this review, the current progress in developing new diagnostic tools to fight malaria is presented. An ideal rapid test for malaria elimination is envisioned with examples to demonstrate how innovative technologies can assist the global defeat against this disease. Diagnostic gaps where technology can bring an impact to the elimination cam‑ paign for malaria are identified. Finally, how a combination of microfluidic‑based technologies and smartphone‑based read‑outs could potentially represent the next generation of rapid diagnostic tests is discussed},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Schwab2017,

title = {Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks},

author = {Patrick Schwab and Gaetano Claudio Scebba and Jia Zhang and Marco Delai and Walter Karlen},

url = {http://www.cinc.org/archives/2017/pdf/363-223.pdf},

doi = {10.22489/CinC.2017.363-223},

year  = {2017},

date = {2017-09-01},

journal = {Computing in Cardiology (CinC)},

volume = {44},

pages = {1--4},

address = {Rennes, F},

abstract = {INTRODUCTION: Previous work on detecting arrhythmias in electrocardiogram (ECG) records has predominantly focused on identifying atrial fibrillation (AF) in data obtained from clinical settings or Holter devices, where long-term recordings with multiple leads are the norm. However, the advent of mobile cardiac event recorders increased the importance of being able to differentiate between multiple types of rhythms in noisy short-term recordings with just a single lead. We propose a machine-learning architecture to learn the temporal and morphological patterns of various types of rhythms in order to perform multiclass classification under these more challenging conditions. METHODS: We segment the input ECG signal with a QRS detector into individual heartbeats. From each heartbeat, we extract - among others - morphological features with the encoding side of a stacked denoising autoencoder that was trained in an unsupervised manner. The extracted features are passed in original heartbeat order as input sequences to an ensemble of recurrent neural networks (RNNs). The RNNs were trained on different features, random overlapping subsets of the training data and in various one-versus-all setups in order to increase the model diversity within the ensemble. We blend the individual RNNs' predictions into a final classification solution using a multilayer perceptron (MLP) that was trained on held-out data. RESULTS: Our best ensemble at time of writing achieves an average F1-score over all classes of 0.78 (F1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ettinger2016,

title = {Building quality mHealth for low resource settings},

author = {Kate Michi Ettinger and Hamilton Pharaoh and Reymound Yaw Buckman and Hoffie Conradie and Walter Karlen},

url = {https://www.tandfonline.com/doi/full/10.1080/03091902.2016.1213906},

doi = {10.1080/03091902.2016.1213906},

issn = {0309-1902},

year  = {2016},

date = {2016-01-01},

journal = {Journal of Medical Engineering & Technology},

volume = {40},

number = {7-8},

pages = {431--43},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Garde2016d,

title = {Respiratory rate and pulse oximetry derived information as predictors of hospital admission in young children in Bangladesh: a prospective observational study},

author = {Ainara Garde and Guohai Zhou and Shahreen Raihana and Dustin Dunsmuir and Walter Karlen and Parastoo Dekhordi and Tanvir Huda and Shams El Arifeen and Charles Larson and Niranjan Kissoon and Guy A Dumont and Mark J Ansermino},

url = {http://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2016-011094},

doi = {10.1136/bmjopen-2016-011094},

issn = {2044-6055},

year  = {2016},

date = {2016-01-01},

journal = {BMJ Open},

volume = {6},

number = {8},

pages = {e011094},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Dehkordi2015,

title = {Evaluation of cardiac modulation in children in response to apnea/hypopnea using the Phone Oximeter™},

author = {Parastoo Dehkordi and Ainara Garde and Walter Karlen and Christian L Petersen and David Wensley and Guy A Dumont and J {Mark Ansermino}},

url = {http://stacks.iop.org/0967-3334/37/i=2/a=187?key=crossref.90bafa387933f4fb238552e83edf9c42},

doi = {10.1088/0967-3334/37/2/187},

issn = {0967-3334},

year  = {2016},

date = {2016-01-01},

journal = {Physiological Measurement},

volume = {37},

number = {2},

pages = {187--202},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Karlen2015,

title = {Estimation of Respiratory Rate From Photoplethysmographic Imaging Videos Compared to Pulse Oximetry},

author = {Walter Karlen and Ainara Garde and Dorothy Myers and Cornie Scheffer and Mark J Ansermino and Guy A Dumont},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7101812},

doi = {10.1109/JBHI.2015.2429746},

issn = {2168-2194},

year  = {2015},

date = {2015-01-01},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {19},

number = {4},

pages = {1331--8},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wiens2014a,

title = {A cohort study of morbidity, mortality and health seeking behavior following rural health center visits by children under 12 in Southwestern Uganda},

author = {Matthew O Wiens and Heng Gan and Celestine Barigye and Guohai Zhou and Elias Kumbakumba and Jerome Kabakyenga and Niranjan Kissoon and Mark J Ansermino and Walter Karlen and Charles P Larson and Stuart M MacLeod},

doi = {10.1371/journal.pone.0118055},

year  = {2015},

date = {2015-01-01},

journal = {PLoS ONE},

volume = {10},

number = {1},

pages = {e0118055},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Raihana2015,

title = {Development and Internal Validation of a Predictive Model Including Pulse Oximetry for Hospitalization of Under-Five Children in Bangladesh},

author = {Shahreen Raihana and Dustin Dunsmuir and Tanvir Huda and Guohai Zhou and Qazi Sadeq-ur Rahman and Ainara Garde and Md Moinuddin and Walter Karlen and Guy A Dumont and Niranjan Kissoon and Shams {El Arifeen} and Charles Larson and Mark J Ansermino},

editor = {Martin Chalumeau},

url = {http://dx.plos.org/10.1371/journal.pone.0143213},

doi = {10.1371/journal.pone.0143213},

issn = {1932-6203},

year  = {2015},

date = {2015-01-01},

journal = {PLoS ONE},

volume = {10},

number = {11},

pages = {e0143213},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gan2015,

title = {The Performance of a Mobile Phone Respiratory Rate Counter Compared to the WHO ARI Timer},

author = {Heng Gan and Walter Karlen and Dustin Dunsmuir and Guohai Zhou and Michelle Chiu and Guy A Dumont and Mark J Ansermino},

url = {http://multi-science.atypon.com/doi/10.1260/2040-2295.6.4.691},

doi = {10.1260/2040-2295.6.4.691},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Healthcare Engineering},

volume = {6},

number = {4},

pages = {691--704},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Karlen2015c,

title = {Variability in estimating shunt from single pulse oximetry measurements},

author = {Walter Karlen and Christian L Petersen and Guy A Dumont and Mark J Ansermino},

url = {http://stacks.iop.org/0967-3334/36/i=5/a=967?key=crossref.8c2a59105fefc7e1db14fdff4a81fb49},

doi = {10.1088/0967-3334/36/5/967},

issn = {0967-3334},

year  = {2015},

date = {2015-01-01},

journal = {Physiological Measurement},

volume = {36},

number = {5},

pages = {967--981},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Garde2014b,

title = {Development of a screening tool for sleep disordered breathing in children using the phone oximeter™.},

author = {Ainara Garde and Parastoo Dehkordi and Walter Karlen and David Wensley and Mark J Ansermino and Guy A Dumont},

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4234680&tool=pmcentrez&rendertype=abstract},

doi = {10.1371/journal.pone.0112959},

issn = {1932-6203},

year  = {2014},

date = {2014-01-01},

journal = {PLoS ONE},

volume = {9},

number = {11},

pages = {e112959},

abstract = {BACKGROUND: Sleep disordered breathing (SDB) can lead to daytime sleepiness, growth failure and developmental delay in children. Polysomnography (PSG), the gold standard to diagnose SDB, is a highly resource-intensive test, confined to the sleep laboratory. AIM: To combine the blood oxygen saturation (SpO2) characterization and cardiac modulation, quantified by pulse rate variability (PRV), to identify children with SDB using the Phone Oximeter, a device integrating a pulse oximeter with a smartphone. METHODS: Following ethics approval and informed consent, 160 children referred to British Columbia Children's Hospital for overnight PSG were recruited. A second pulse oximeter sensor applied to the finger adjacent to the one used for standard PSG was attached to the Phone Oximeter to record overnight pulse oximetry (SpO2 and photoplethysmogram (PPG)) alongside the PSG. RESULTS: We studied 146 children through the analysis of the SpO2 pattern, and PRV as an estimate of heart rate variability calculated from the PPG. SpO2 variability and SpO2 spectral power at low frequency, was significantly higher in children with SDB due to the modulation provoked by airway obstruction during sleep (p-value [Formula: see text]). PRV analysis reflected a significant augmentation of sympathetic activity provoked by intermittent hypoxia in SDB children. A linear classifier was trained with the most discriminating features to identify children with SDB. The classifier was validated with internal and external cross-validation, providing a high negative predictive value (92.6%) and a good balance between sensitivity (88.4%) and specificity (83.6%). Combining SpO2 and PRV analysis improved the classification performance, providing an area under the receiver operating characteristic curve of 88%, beyond the 82% achieved using SpO2 analysis alone. CONCLUSIONS: These results demonstrate that the implementation of this algorithm in the Phone Oximeter will provide an improved portable, at-home screening tool, with the capability of monitoring patients over multiple nights.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Crede2014,

title = {Where do pulse oximeter probes break?},

author = {Sarah Crede and G {Van der Merwe} and J Hutchinson and David Woods and Walter Karlen and Joy Lawn},

url = {http://www.ncbi.nlm.nih.gov/pubmed/24420339},

doi = {10.1007/s10877-013-9538-2},

year  = {2014},

date = {2014-01-01},

journal = {Journal of clinical monitoring and computing},

volume = {28},

number = {3},

pages = {309--14},

abstract = {Pulse oximetry, a non-invasive method for accurate assessment of blood oxygen saturation (SPO2), is an important monitoring tool in health care facilities. However, it is often not available in many low-resource settings, due to expense, overly sophisticated design, a lack of organised procurement systems and inadequate medical device management and maintenance structures. Furthermore medical devices are often fragile and not designed to withstand the conditions of low-resource settings. In order to design a probe, better suited to the needs of health care facilities in low-resource settings this study aimed to document the site and nature of pulse oximeter probe breakages in a range of different probe designs in a low to middle income country. A retrospective review of job cards relating to the assessment and repair of damaged or faulty pulse oximeter probes was conducted at a medical device repair company based in Cape Town, South Africa, specializing in pulse oximeter probe repairs. 1,840 job cards relating to the assessment and repair of pulse oximeter probes were reviewed. 60.2 % of probes sent for assessment were finger-clip probes. For all probes, excluding the neonatal wrap probes, the most common point of failure was the probe wiring (textgreater50 %). The neonatal wrap most commonly failed at the strap (51.5 %). The total cost for quoting on the broken pulse oximeter probes and for the subsequent repair of devices, excluding replacement components, amounted to an estimated ZAR 738,810 (USD $98,508). Improving the probe wiring would increase the life span of pulse oximeter probes. Increasing the life span of probes will make pulse oximetry more affordable and accessible. This is of high priority in low-resource settings where frequent repair or replacement of probes is unaffordable or impossible.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Karlen2014,

title = {Improving the accuracy and efficiency of respiratory rate measurements in children using mobile devices.},

author = {Walter Karlen and Heng Gan and Michelle Chiu and Dustin Dunsmuir and Guohai Zhou and Guy A Dumont and Mark J Ansermino},

url = {http://www.ncbi.nlm.nih.gov/pubmed/24919062 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4053345&tool=pmcentrez&rendertype=abstract},

doi = {10.1371/journal.pone.0099266},

issn = {1932-6203},

year  = {2014},

date = {2014-01-01},

journal = {PLoS ONE},

volume = {9},

number = {6},

pages = {e99266},

abstract = {The recommended method for measuring respiratory rate (RR) is counting breaths for 60 s using a timer. This method is not efficient in a busy clinical setting. There is an urgent need for a robust, low-cost method that can help front-line health care workers to measure RR quickly and accurately. Our aim was to develop a more efficient RR assessment method. RR was estimated by measuring the median time interval between breaths obtained from tapping on the touch screen of a mobile device. The estimation was continuously validated by measuring consistency (% deviation from the median) of each interval. Data from 30 subjects estimating RR from 10 standard videos with a mobile phone application were collected. A sensitivity analysis and an optimization experiment were performed to verify that a RR could be obtained in less than 60 s; that the accuracy improves when more taps are included into the calculation; and that accuracy improves when inconsistent taps are excluded. The sensitivity analysis showed that excluding inconsistent tapping and increasing the number of tap intervals improved the RR estimation. Efficiency (time to complete measurement) was significantly improved compared to traditional methods that require counting for 60 s. There was a trade-off between accuracy and efficiency. The most balanced optimization result provided a mean efficiency of 9.9 s and a normalized root mean square error of 5.6%, corresponding to 2.2 breaths/min at a respiratory rate of 40 breaths/min. The obtained 6-fold increase in mean efficiency combined with a clinically acceptable error makes this approach a viable solution for further clinical testing. The sensitivity analysis illustrating the trade-off between accuracy and efficiency will be a useful tool to define a target product profile for any novel RR estimation device.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Garde2014,

title = {Estimating respiratory and heart rates from the correntropy spectral density of the photoplethysmogram.},

author = {Ainara Garde and Walter Karlen and Mark J Ansermino and Guy A Dumont},

url = {http://dx.plos.org/10.1371/journal.pone.0086427 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3899260&tool=pmcentrez&rendertype=abstract},

doi = {10.1371/journal.pone.0086427},

issn = {1932-6203},

year  = {2014},

date = {2014-01-01},

journal = {PLoS ONE},

volume = {9},

number = {1},

pages = {e86427},

abstract = {The photoplethysmogram (PPG) obtained from pulse oximetry measures local variations of blood volume in tissues, reflecting the peripheral pulse modulated by heart activity, respiration and other physiological effects. We propose an algorithm based on the correntropy spectral density (CSD) as a novel way to estimate respiratory rate (RR) and heart rate (HR) from the PPG. Time-varying CSD, a technique particularly well-suited for modulated signal patterns, is applied to the PPG. The respiratory and cardiac frequency peaks detected at extended respiratory (8 to 60 breaths/min) and cardiac (30 to 180 beats/min) frequency bands provide RR and HR estimations. The CSD-based algorithm was tested against the Capnobase benchmark dataset, a dataset from 42 subjects containing PPG and capnometric signals and expert labeled reference RR and HR. The RR and HR estimation accuracy was assessed using the unnormalized root mean square (RMS) error. We investigated two window sizes (60 and 120 s) on the Capnobase calibration dataset to explore the time resolution of the CSD-based algorithm. A longer window decreases the RR error, for 120-s windows, the median RMS error (quartiles) obtained for RR was 0.95 (0.27, 6.20) breaths/min and for HR was 0.76 (0.34, 1.45) beats/min. Our experiments show that in addition to a high degree of accuracy and robustness, the CSD facilitates simultaneous and efficient estimation of RR and HR. Providing RR every minute, expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Karlen2013a,

title = {Multiparameter respiratory rate estimation from the photoplethysmogram.},

author = {Walter Karlen and Srinivas Raman and Mark J Ansermino and Guy A Dumont},

url = {http://www.ncbi.nlm.nih.gov/pubmed/23399950 https://www.researchgate.net/publication/235521997_Multiparameter_Respiratory_Rate_Estimation_From_the_Photoplethysmogram},

doi = {10.1109/TBME.2013.2246160},

issn = {1558-2531},

year  = {2013},

date = {2013-01-01},

journal = {IEEE Transactions on Biomedical Engineering},

volume = {60},

number = {7},

pages = {1946--53},

abstract = {We present a novel method for estimating respiratory rate in real time from the photoplethysmogram (PPG) obtained from pulse oximetry. Three respiratory-induced variations (frequency, intensity, and amplitude) are extracted from the PPG using the Incremental-Merge Segmentation algorithm. Frequency content of each respiratory-induced variation is analyzed using fast Fourier transforms. The proposed Smart Fusion method then combines the results of the three respiratory-induced variations using a transparent mean calculation. It automatically eliminates estimations considered to be unreliable because of detected presence of artifacts in the PPG or disagreement between the different individual respiratory rate estimations. The algorithm has been tested on data obtained from 29 children and 13 adults. Results show that it is important to combine the three respiratory-induced variations for robust estimation of respiratory rate. The Smart Fusion showed trends of improved estimation (mean root mean square error 3.0 breaths/min) compared to the individual estimation methods (5.8, 6.2, and 3.9 breaths/min). The Smart Fusion algorithm is being implemented in a mobile phone pulse oximeter device to facilitate the diagnosis of severe childhood pneumonia in remote areas.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Karlen2013ab,

title = {Multiparameter respiratory rate estimation from the photoplethysmogram.},

author = {Walter Karlen and Srinivas Raman and Mark J Ansermino and Guy A Dumont},

url = {http://www.ncbi.nlm.nih.gov/pubmed/23399950 https://www.researchgate.net/publication/235521997_Multiparameter_Respiratory_Rate_Estimation_From_the_Photoplethysmogram},

doi = {10.1109/TBME.2013.2246160},

issn = {1558-2531},

year  = {2013},

date = {2013-01-01},

journal = {IEEE Transactions on Biomedical Engineering},

volume = {60},

number = {7},

pages = {1946--53},

abstract = {We present a novel method for estimating respiratory rate in real time from the photoplethysmogram (PPG) obtained from pulse oximetry. Three respiratory-induced variations (frequency, intensity, and amplitude) are extracted from the PPG using the Incremental-Merge Segmentation algorithm. Frequency content of each respiratory-induced variation is analyzed using fast Fourier transforms. The proposed Smart Fusion method then combines the results of the three respiratory-induced variations using a transparent mean calculation. It automatically eliminates estimations considered to be unreliable because of detected presence of artifacts in the PPG or disagreement between the different individual respiratory rate estimations. The algorithm has been tested on data obtained from 29 children and 13 adults. Results show that it is important to combine the three respiratory-induced variations for robust estimation of respiratory rate. The Smart Fusion showed trends of improved estimation (mean root mean square error 3.0 breaths/min) compared to the individual estimation methods (5.8, 6.2, and 3.9 breaths/min). The Smart Fusion algorithm is being implemented in a mobile phone pulse oximeter device to facilitate the diagnosis of severe childhood pneumonia in remote areas.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Brouse2013a,

title = {Monitoring nociception during general anesthesia with cardiorespiratory coherence.},

author = {Chris J Brouse and Walter Karlen and Guy A Dumont and Dorothy Myers and Erin Cooke and Jonathan Stinson and Joanne Lim and Mark J Ansermino},

url = {http://www.ncbi.nlm.nih.gov/pubmed/23568315},

doi = {10.1007/s10877-013-9463-4},

issn = {1573-2614},

year  = {2013},

date = {2013-01-01},

journal = {Journal of clinical monitoring and computing},

volume = {27},

number = {5},

pages = {551--60},

abstract = {A novel wavelet transform cardiorespiratory coherence (WTCRC) algorithm has been developed to measure the autonomic state. WTCRC may be used as a nociception index, ranging from 0 (no nociception, strong coherence) to 100 (strong nociception, low coherence). The aim of this study is to estimate the sensitivity of the algorithm to nociception (dental dam insertions) and antinociception (bolus doses of anesthetic drugs). WTCRC's sensitivity is compared to mean heart rate (HRmean) and mean non-invasive blood pressure (NIBPmean), which are commonly used clinical signs. Data were collected from 48 children receiving general anesthesia during dental surgery. The times of dental dam insertion and anesthetic bolus events were noted in real-time during surgeries. 42 dental dam insertion and 57 anesthetic bolus events were analyzed. The change in average WTCRC, HRmean, and NIBPmean was calculated between a baseline period before each event and a response period after. A Wilcoxon rank-sum test was used to compare changes. Dental dam insertion changed the WTCRC nociception index by an average of 14 (82 %) [95 % CI from 7.4 to 19], HRmean by 7.3 beats/min (8.1 %) [5.6-9.6], and NIBPmean by 8.3 mmHg (12 %) [4.9-13]. A bolus dose of anesthetics changed the WTCRC by -15 (-50 %) [-21 to -9.3], HRmean by -4.8 beats/min (4.6 %) [-6.6 to -2.9], and NIBPmean by -2.6 mmHg (3.4 %) [-4.7 to -0.50]. A nociception index based on cardiorespiratory coherence is more sensitive to nociception and antinociception than are HRmean or NIBPmean. The WTCRC algorithm shows promise for noninvasively monitoring nociception during general anesthesia.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Karlen2012c,

title = {Photoplethysmogram signal quality estimation using repeated Gaussian filters and cross-correlation.},

author = {Walter Karlen and K Kobayashi and Mark J Ansermino and Guy A Dumont},

url = {http://www.ncbi.nlm.nih.gov/pubmed/22986287},

doi = {10.1088/0967-3334/33/10/1617},

issn = {1361-6579},

year  = {2012},

date = {2012-09-01},

journal = {Physiological Measurement},

volume = {33},

number = {10},

pages = {1617--29},

abstract = {Pulse oximeters are monitors that noninvasively measure heart rate and blood oxygen saturation (SpO(2)). Unfortunately, pulse oximetry is prone to artifacts which negatively impact the accuracy of the measurement and can cause a significant number of false alarms. We have developed an algorithm to segment pulse oximetry signals into pulses and estimate the signal quality in real time. The algorithm iteratively calculates a signal quality index (SQI) ranging from 0 to 100. In the presence of artifacts and irregular signal morphology, the algorithm outputs a low SQI number. The pulse segmentation algorithm uses the derivative of the signal to find pulse slopes and an adaptive set of repeated Gaussian filters to select the correct slopes. Cross-correlation of consecutive pulse segments is used to estimate signal quality. Experimental results using two different benchmark data sets showed a good pulse detection rate with a sensitivity of 96.21% and a positive predictive value of 99.22%, which was equivalent to the available reference algorithm. The novel SQI algorithm was effective and produced significantly lower SQI values in the presence of artifacts compared to SQI values during clean signals. The SQI algorithm may help to guide untrained pulse oximeter users and also help in the design of advanced algorithms for generating smart alarms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chandler2012,

title = {Pulse oximeter plethysmograph variation and its relationship to the arterial waveform in mechanically ventilated children.},

author = {John R Chandler and Erin Cooke and Chris Petersen and Walter Karlen and N Froese and Joanne Lim and Mark J Ansermino},

url = {http://www.ncbi.nlm.nih.gov/pubmed/22407178},

doi = {10.1007/s10877-012-9347-z},

issn = {1573-2614},

year  = {2012},

date = {2012-06-01},

journal = {Journal of clinical monitoring and computing},

volume = {26},

number = {3},

pages = {145--51},

abstract = {The variations induced by mechanical ventilation in the arterial pulse pressure and pulse oximeter plethysmograph waveforms have been shown to correlate closely and be effective in adults as markers of volume responsiveness. The aims of our study were to investigate: (1) the feasibility of recording plethysmograph indices; and (2) the relationship between pulse pressure variation ($Delta$PP), plethysmograph variation ($Delta$POP) and plethysmograph variability index (PVI) in a diverse group of mechanically ventilated children. A prospective, observational study was performed. Mechanically ventilated children less than 11 years of age, with arterial catheters, were enrolled during the course of their clinical care in the operating room or in the pediatric intensive care unit. Real time monitor waveforms and trend data were recorded. $Delta$PP and $Delta$POP were manually calculated and the relationships between $Delta$PP, $Delta$POP and PVI were compared using Bland-Altman analysis and Pearson correlations. Forty-nine children were recruited; four (8%) subjects were excluded due to poor quality of the plethysmograph waveforms. $Delta$PP and $Delta$POP demonstrated a strong correlation (r = 0.8439, P textless 0.0001) and close agreement (Bias = 1.44 ± 6.4%). PVI was found to correlate strongly with $Delta$PP (r = 0.7049, P textless 0.0001) and $Delta$POP (r = 0.715, P textless 0.0001). This study demonstrates the feasibility of obtaining plethysmographic variability indices in children under various physiological stresses. These data show a similarly strong correlation to that described in adults, between the variations induced by mechanical ventilation in arterial pulse pressure and the pulse oximeter plethysmograph.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}