Detection of Different Stages of Anxiety From Single-Channel Wearable ECG Sensor Signal Using Fourier–Bessel Domain Adaptive Wavelet Transform

Abstract

In this letter, the Fourier-Bessel domain adaptive wavelet transform (FBDAWT) is proposed for the automated detection of anxiety stages using the single-channel wearable electrocardiogram (ECG) sensor signal. The modes or components are evaluated using the FBDAWT of the ECG signal. The increment entropy (IE) and energy features are computed from each mode of ECG data. The cross gradient boosting (XGBoost) model is employed for the normal (N) versus light anxiety (LA) versus moderate (MA) versus severe anxiety (SA) based detection task using the FBDAWT domain ECG signal features. The wearable sensor-based ECG signals from a publicly available database are used to assess the performance of the proposed approach. The results show that the XGBoost model has obtained the accuracy, F1-score, and Kappa score values of 92.27%, 92.13%, and 0.89, respectively. We have compared the performance of the proposed FBDAWT domain approach with existing methods for anxiety detection using physiological signals.