Sano A, Chen W, Lopez-Martinez D, Taylor S, Picard R.
Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks. Journal of Biomedical and Health Informatics. 2018.
AbstractUnobtrusive and accurate ambulatory methods are needed to monitor long-term sleep patterns for improving health. Previously developed ambulatory sleep detection methods rely either in whole or in part on self-reported diary data as ground truth, which is a problem since people often do not fill them out accurately. This paper presents an algorithm that uses multimodal data from smartphones and wearable technologies to detect sleep/wake state and sleep onset/offset using a type of recurrent neural network with long-short-term memory (LSTM) cells for synthesizing temporal information. We collected 5580 days of multimodal data from 186 participants and compared the new method for sleep/wake classification and sleep onset/offset detection to (1) non-temporal machine learning methods and (2) a state-of-the-art actigraphy software. The new LSTM method achieved a sleep/wake classification accuracy of 96.5%, and sleep onset/offset detection F1 scores of 0.86 and 0.84 respectively, with mean absolute errors of 5.0 and 5.5 min, respectively, when compared with sleep/wake state and sleep onset/offset assessed using actigraphy and sleep diaries. The LSTM results were statistically superior to those from non-temporal machine learning algorithms and the actigraphy software. We show good generalization of the new algorithm by comparing participant-dependent and participant-independent models, and we show how to make the model nearly realtime with slightly reduced performance.
Lopez-Martinez D, Peng K, Steele S, Lee A, Borsook D, Picard R.
Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals (BEST STUDENT PAPER AWARD), in
International Conference on Pattern Recognition. Beijing, China ; 2018.
AbstractCurrently there is no validated objective measure of pain. Recent neuroimaging studies have explored the feasibility of using functional near-infrared spectroscopy (fNIRS) to measure alterations in brain function in evoked and ongoing pain. In this study, we applied multi-task machine learning methods to derive a practical algorithm for pain detection derived from fNIRS signals in healthy volunteers exposed to a painful stimulus. Especially, we employed multi-task multikernel learning to account for the inter-subject variability in pain response. Our results support the use of fNIRS and machine learning techniques in developing objective pain detection, and also highlight the importance of adopting personalized analysis in the process.
Sano A, Taylor S, Jaques N, Chen W, Lopez-Martinez D, Nosakhare E, Rudovic O, Umematsu T, Picard R.
Mood, Stress and Sleep Sensing with Wearable Sensors and Mobile Phone, in
International Engineering in Medicine and Biology Conference (EMBC). Hawaii ; 2018.
AbstractThis paper highlights lessons learned from a four-year ambulatory study, developed to measure Sleep, Networks, Affect, Performance, Stress, and Health using Objective Techniques (SNAPSHOT), which was run in seven cohorts of college students (N=321), collecting continuous wearable and mobile phone data, typically for a month each. This paper overviews the objectives of this study, challenges faced, and some key findings focused on detecting sleep patterns and detecting and forecasting mood changes.
Lopez-Martinez D, Picard R.
Continuous pain intensity estimation from autonomic signals with recurrent neural networks, in
IEEE Engineering in Medicine and Biology Society (EMBC). Hawaii ; 2018.
AbstractPain is usually measured by patient’s self-report, which requires patient collaboration. Hence, the development of an objective automatic pain detection method would be useful in many clinical applications and patient populations. Previous studies have explored the feasibility of using physiological autonomic signals to detect the presence of pain. In this study, we focused on continuously estimating experimental heat pain intensity with high temporal resolution from autonomic signals. Specifically, we employed skin conductance deconvolution and point process heart rate variability analysis to continuously evaluate time-varying autonomic parameters, and presented a regression algorithm based on recurrent neural networks.
Lopez-Martinez D, Picard R.
Skin conductance deconvolution for pain estimation, in
Biomedical and Health Informatics (BHI). Las Vegas, USA ; 2018.
AbstractPain is usually measured by patient’s self-report. While self-report is viewed as the gold standard of pain assessment, this approach fails when patients cannot communicate pain intensity or lack normal mental abilities. Here, we present a method for the automatic estimation of pain intensity from skin conductance data, and test it in a dataset containing physiological responses to nociceptive heat pain.