Time-frequency analysis of heart rate variability for sleep and wake classification

Abstract

This paper describes a method to adapt the spectral features extracted from heart rate variability (HRV) for sleep and wake classification. HRV series can be derived from electrocardiogram (ECG) signals obtained from single-night polysomnography (PSG) recordings. Traditionally, the HRV spectral features are extracted from the spectrum of an HRV series with fixed boundaries specifying bands of very low frequency (VLF), low frequency (LF), and high frequency (HF). However, because they are fixed, they may fail to accurately reflect certain aspects of autonomic nervous activity, which in turn may limit their discriminative power when using HRV spectral features, e.g., in sleep and wake classification. This is in part related to the fact that the sympathetic tone (partially reflected in the LF band) and the respiratory activity (modulated in the HF band) will vary over time. In order to minimize the impact of these differences, we adapt the HRV spectral boundaries using time-frequency analysis. Experiments conducted on a dataset acquired from 15 healthy subjects show that the discriminative power of the adapted HRV spectral features are significantly increased when classifying sleep and wake. Additionally, this method also provides a significant improvement of the overall classification performance when used in combination with some other (non-spectral) HRV features.