Sleep-wake classification via quantifying heart rate variability by convolutional neural network

Autor: John Malik, Yu-Lun Lo, Hau-Tieng Wu
Rok vydání: 2018
Předmět:
Adult
Male
FOS: Computer and information sciences
Databases
Factual
Physiology
Computer science
0206 medical engineering
Biomedical Engineering
Biophysics
FOS: Physical sciences
Machine Learning (stat.ML)
02 engineering and technology
Statistics - Applications
Convolutional neural network
Electrocardiography
03 medical and health sciences
0302 clinical medicine
Heart Rate
Statistics - Machine Learning
Robustness (computer science)
Physiology (medical)
Humans
Heart rate variability
Applications (stat.AP)
Sensitivity (control systems)
Wakefulness
Sleep Stages
Artificial neural network
business.industry
Signal Processing
Computer-Assisted
Pattern recognition
Probability and statistics
020601 biomedical engineering
Healthy Volunteers
Physics - Data Analysis
Statistics and Probability
Scalability
Female
Neural Networks
Computer
Artificial intelligence
Sleep
business
030217 neurology & neurosurgery
Data Analysis
Statistics and Probability (physics.data-an)
DOI: 10.48550/arxiv.1808.00142
Popis: 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 [Formula: see text] values for predicting the wake stage are [Formula: see text], 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.
Databáze: OpenAIRE
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