Signal Quality Classification of Impedance Plethysmogram and Ballistocardiogram for Pulse Transit Time Measurement

Abstract

Mobile health (mHealth) was developed ten years ago, which used wireless wearable devices to collect the many physiological messages in daily life, regardless of time and place, for some health services including monitoring chronic diseases and reducing the cost of empowering patients and families for handling their daily healthcare. However, the challenge for these measurements is the lower signal quality because users would measure their conditions not on a resting status. Now, the pulse transit time (PTT) is highly related to blood pressure has been proposed, which is acquired from the impedance plethysmography (IPG) and ballistocardiogram (BCG) measured by the weight-fat scale. However, the lower signal quality of IPG and BCG, lowers the accuracy of blood pressure. This study aims to use deep learning techniques to classify the signal quality of BCG and IPG signals. The reference PTTs were measured by the electrocardiogram (ECG) and photoplethysmogram (PPG). The signal quality of each segment was labeled with the error between proposed and reference PTTs. We used three signals, BCG, IPG, and differential IPG, as the input. The proposed one-dimensional stacking convolutional neural network and gait recursive unit (1-D CNN+GRU) model to approach the classification. The good performances achieved high accuracy (98.85%), recall (99.4%), precision (94.29%), and F1-score (96.78%). These results show the potential benefit of the signal quality classification for the PTT measurement.