Using Dynamic Time Warping for Sleep and Wake Discrimination

1. Oral Session “Assisted Living Technology and Smart homes” IEEE-EMBS BHI Conference (Shenzhen, China, Jan. 07 2012) Using Dynamic Time Warping for Sleep and Wake Discrimination Xi LongPhD Candidate (Philips Research and TU/e, NL), IEEE Member Pedro FonsecaSenior Scientist (Philips Research,NL) Jerome FoussierPhD Candidate (RWTH University, DE), IEEE Member Dr. Reinder Haakma Senior Scientist and Director (Philips Research , NL) Prof. Ronald AartsFellow (Philips Research, NL) and Professor (TU/e, NL), IEEE Fellow

2. Background of our “Big” Project Unobtrusive sleep monitoring • Monitoring of people’s sleep • at home • for non-clinical purposes • Requirements • Sleep information • Understandable for non-experts • Comfort • No cables, Contactless or easy to wear • Ease of installation • Consumer can set it up • … TU/e and Philips Research, Xi Long

3. Flow sensing classification sensor data acquisition physiological signals feature extraction sleep stage classification • Unobtrusive sensing • Signal processing • e.g., body movement, respiration, • cardiac activity (heart rate variability), etc. • Good features • Optimal feature selector • Reliable and accurate classifier • sleep-wake • REM-NREM • deep-light this paper TU/e and Philips Research, Xi Long

4. This study: Sleep-Wake Discrimination • Data acquisition Respiratory effort signal (10 Hz) • Polysonmography (PSG) chest belt, Philips Respironics Actigraphy • Actiwatch, Philips Respironics • A wrist-worn device with a built-in accelerometer • Subject • 9 subjects obtained in Sleep Health Center, Boston, USA (Sleep Lab) • Age: 31.9 ± 12.8 (Adults) • Sleep efficiency: 91.5 ± 3.7 % (Healthy) • Ground truth of sleep and wake states • Expert scoring based on PSG measurements according to AASM guideline www.aasmnet.org “Gold Standard” A. Rechtschaffen et al. 1968 TU/e and Philips Research, Xi Long

5. Existing Features • Epoch-based (each epoch 30 seconds) • Existing (original) feature set D. Sandrine et al. 2010, S.J. Redmond et al. 2007, etc. • Actigraphic feature • Sum of activity counts over an epoch • Respiratory features • Standard deviation • Respiration rate • VLF and HF respiration contents • … We want to further improve discrimination performance Typical examples of wake and sleep respiratory effort signal segments TU/e and Philips Research, Xi Long

6. New Feature? • Dynamic Time Warping (DTW) technique • DTW has been widely used in • speech processing e.g. L. Rabiner et al. 1978 • Bioinformatics e.g. D. Berndt et al. 2001 • Biometrics e.g. Z. M. Kovacs-Vajna 2000 • etc. • It focuses on the signal shape in time domain What is DTW? Does DTW work? … TU/e and Philips Research, Xi Long

7. DTW (-distance) Y X • Given two time series (signals) X = {x1, x2, …, xi, …, xn}, length n Y = {y1, y2, …, yj, …, ym}, length m • Form an n-by-m matrix • each element of the matrix (i, j) corresponds to a distance function D: D(i, j) = (xi - yj) 2 (Here n = m) Y • Construct a warping path W = {w1, w2, …, wk, …, wK} • wk = (i, j)k the kth element of W • it maps the elements of X and Y so that the total cumulative distance is minimized. • The DTW-distance between X and Y is X i = j = k → Euclidean distance TU/e and Philips Research, Xi Long

8. New Feature:DTW-based Feature An example of a respiratory signal series • “Minimal DTW-distance” • Signal is split into N non-overlapping epochs E1, E2, …, EN • For current epoch Ep (1 ≤ p ≤ N) • compute DTW (Ep, Eq) for all 1 ≤ q ≤ N and q ≠ p • the feature value is the minimalone among them. Do the same process for every epoch Ep wake Sleep wake Sleep … … … First find the most similar epoch for the current epoch, and then compute the DTW-distance between them 1 2 3 4 5 N … Current interesting epoch TU/e and Philips Research, Xi Long

9. New Feature: DTW-based Feature • Three situations, where the minimal value occurs between: • two sleep epochs (small feature value) • a wake and a sleep epoch (moderate feature value) • two wake epochs(large feature value) • Two cases: • The current epoch is “sleep” • 1) may happen • The current epoch is “wake” • 2) or 3) may happen Philips Research and TU/e, Xi Long

10. Results – Discrimination performance • Linear Discriminant (LD) Classifier • An appropriate classifier for sleep-wake classification D. Sandrine et al. 2010 • Cohen’s Kappa coefficient ĸ allows for • a better representation of the unbalanced problem to optimize performanceR. Bakeman et al. 1986 Significance of difference was examined via a paired t-test, p = 0.036 (p < 0.05) and df= 8 Philips Research and TU/e, Xi Long

11. Results – Sleep statistics • TST: total sleep time • TWT: total wake time • SE: sleep efficiency • TST / total time lying on bed • SOL: sleep onset latency • Detect at the first epoch of a block of 17 consecutive epochs of which at least 16 were annotated as sleep • ST: snooze time • Similar as SOL but for wake epochs • WASO: wake after sleep onset • Equal to TWT excluding SOL and ST Philips Research and TU/e, Xi Long

12. Results – Comparison Comparison between this study and the previous work Philips Research and TU/e, Xi Long

13. Conclusion • DTW • focuses more on signal shape and within-subject similarity • is without needs of signals having same phases and lengths • DTW is promising in • improving the performance of sleep-wake discrimination • giving more accurate estimation of sleep statistics • providing an opportunity of reducing the number of sensor modalities Philips Research and TU/e, Xi Long

14. Future work • A larger sized dataset • Phase shifting effect on computing DTW feature • Correlation analysis • Apply DTW on cardiac activity (e.g. heart rate variability) • Real-time classification Philips Research and TU/e, Xi Long

15. Acknowledgement • Funding - Philips Research Lab, Eindhoven, the Netherlands • Thank Dr. Igor Berezhnyy Dr. Sandrine Devot Mr. Sam Jelfs from Philips Research for insightful comments. Philips Research and TU/e, Xi Long

16. Any Questions? Philips Research and TU/e, Xi Long