TY - JOUR
T1 - Sleep-wake classification via quantifying heart rate variability by convolutional neural network
AU - Malik, John
AU - Lo, Yu Lun
AU - Wu, Hau Tieng
N1 - Publisher Copyright:
© 2018 Institute of Physics and Engineering in Medicine.
PY - 2018/8/20
Y1 - 2018/8/20
N2 - Objective: Fluctuations in heart rate are intimately related to changes in the physiological state of the organism. We exploit this relationship by classifying a human participant's wake/sleep status using his instantaneous heart rate (IHR) series. Approach: We use a convolutional neural network (CNN) to build features from the IHR series extracted from a whole-night electrocardiogram (ECG) and predict every 30 s whether the participant is awake or asleep. Our training database consists of 56 normal participants, and we consider three different databases for validation; one is private, and two are public with different races and apnea severities. Main results: On our private database of 27 participants, our accuracy, sensitivity, specificity, and values for predicting the wake stage are , 52.4%, 89.4%, and 0.83, respectively. Validation performance is similar on our two public databases. When we use the photoplethysmography instead of the ECG to obtain the IHR series, the performance is also comparable. A robustness check is carried out to confirm the obtained performance statistics. Significance: This result advocates for an effective and scalable method for recognizing changes in physiological state using non-invasive heart rate monitoring. The CNN model adaptively quantifies IHR fluctuation as well as its location in time and is suitable for differentiating between the wake and sleep stages.
AB - Objective: Fluctuations in heart rate are intimately related to changes in the physiological state of the organism. We exploit this relationship by classifying a human participant's wake/sleep status using his instantaneous heart rate (IHR) series. Approach: We use a convolutional neural network (CNN) to build features from the IHR series extracted from a whole-night electrocardiogram (ECG) and predict every 30 s whether the participant is awake or asleep. Our training database consists of 56 normal participants, and we consider three different databases for validation; one is private, and two are public with different races and apnea severities. Main results: On our private database of 27 participants, our accuracy, sensitivity, specificity, and values for predicting the wake stage are , 52.4%, 89.4%, and 0.83, respectively. Validation performance is similar on our two public databases. When we use the photoplethysmography instead of the ECG to obtain the IHR series, the performance is also comparable. A robustness check is carried out to confirm the obtained performance statistics. Significance: This result advocates for an effective and scalable method for recognizing changes in physiological state using non-invasive heart rate monitoring. The CNN model adaptively quantifies IHR fluctuation as well as its location in time and is suitable for differentiating between the wake and sleep stages.
KW - convolutional neural network
KW - heart rate variability
KW - instantaneous heart rate
KW - sleep stage
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U2 - 10.1088/1361-6579/aad5a9
DO - 10.1088/1361-6579/aad5a9
M3 - Article
C2 - 30043757
AN - SCOPUS:85053138377
SN - 0967-3334
VL - 39
JO - Physiological Measurement
JF - Physiological Measurement
IS - 8
M1 - 085004
ER -