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 Long Pedro Fonseca Jerome Foussier Dr. Reinder Haakma Prof. Ronald Aarts

PhD Candidate (Philips Research and TU/e, NL), IEEE Member Senior Scientist (Philips Research, NL) PhD Candidate (RWTH University, DE), IEEE Member Senior Scientist and Director (Philips Research , NL) Fellow (Philips Research, NL) and Professor (TU/e, NL), IEEE Fellow

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 – … April 24, 2020 TU/e and Philips Research, Xi Long 2

sensing

sensor data acquisition physiological signals

Flow

classification

feature extraction sleep stage classification April 24, 2020 • • 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 TU/e and Philips Research, Xi Long

this paper

3

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 “Gold Standard”

A. Rechtschaffen et al. 1968

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

April 24, 2020 TU/e and Philips Research, Xi Long 4

Existing Features

• • Epoch-based (each epoch 30 seconds)

We want to further improve discrimination performance

– Actigraphic feature • Sum of activity counts over an epoch – Respiratory features • Standard deviation • Respiration rate • VLF and HF respiration contents • … April 24, 2020 Typical examples of wake and sleep respiratory effort signal segments TU/e and Philips Research, Xi Long 5

New Feature?

•

Dynamic Time Warping (DTW) technique

– DTW has been widely used in • • speech processing Bioinformatics

e.g. L. Rabiner et al. 1978 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? …

April 24, 2020 TU/e and Philips Research, Xi Long 6

DTW (-distance)

• • • • Given two time series (signals) X = {x 1 , x 2 , …, x

i

, …, x

n

}, length n Y = {y 1 , y 2 , …, y

j

, …, y

m

}, length m (Here n = m) Form an n-by-m matrix – each element of the matrix (i, j) corresponds to a distance function D: D(i, j) = (x

i

- y

j

) 2 Construct a warping path – – W = {w 1 , w 2 , …, w

k

, …, w

K

}

w k

= (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

Y

i = j = k → Euclidean distance 7

X

April 24, 2020 TU/e and Philips Research, Xi Long

Y X

New Feature: DTW-based Feature

• “Minimal DTW-distance” – – Signal is split into N non-overlapping epochs E 1 , E 2 , …, E

N

For current epoch E

p

• (1 ≤ p ≤ N) compute DTW (E

p

, E

q

) for all 1 ≤ q ≤ N and q ≠ p • the feature value is the minimal one among them.

Do the same process for every epoch E p

An example of a respiratory signal series

wake Sleep wake Sleep

… …

First find the most similar epoch for the current epoch, and then compute the DTW-distance between them

April 24, 2020 1 2 3 4 5 … … Current interesting epoch

N

TU/e and Philips Research, Xi Long 9

New Feature: DTW-based Feature

• Three situations, where the minimal value occurs between:

1) 2) 3)

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 April 24, 2020 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 performance

R. Bakeman et al. 1986

April 24, 2020 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 April 24, 2020 Philips Research and TU/e, Xi Long 12

Results – Comparison

Comparison between this study and the previous work This study

D. Sandrine et al. 2010

Classifier

LD LD

Signals (Modalities)

• • Actigraphy Respiratory effort signal • • • Actigraphy Respiratory effort signal

ECG signal Features

Original set +

DTW (resp.)

Kappa

0.69

Original set 0.70

April 24, 2020 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 April 24, 2020 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 April 24, 2020 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.

April 24, 2020 Philips Research and TU/e, Xi Long 16

Any Questions?

April 24, 2020 Philips Research and TU/e, Xi Long 17