Modeling and Reproducing Textile Sensor Noise: Implications for Textile-Compatible Signal Processing Algorithms

Abstract

Smart textiles provide an opportunity to simultaneously record various electrophysiological signals from the human body, such as ECG, in a non-invasive and continuous manner. Accurate processing of ECG signals recorded using textile sensors is challenging due to the very low signal- to-noise ratio (SNR). Signal processing algorithms that can extract ECG signal out of textile-based electrode recordings, despite low SNR are needed. Presently, there are no textile ECG datasets available to develop, test and validate these algorithms. In this paper we attempted to model textile ECG signals by adding the textile sensor noise to open access ECG signals. We employed the linear predictive coding method to model different features of this noise. By approximating the linear predictive coding residual signals using Kernel Density Estimation, an artificial textile ECG noise signal was generated by filtering the residual signal with the linear predictive coding coefficients. The obtained textile sensor noise was added to the MIT-BIH Arrhythmia Database (MITDB), thus creating Textile-like ECG dataset consisting of 108 channels (30 min each). Furthermore, a Python code for generating textile-like ECG signals with variable SNR was also made available online. Finally, to provide a benchmark for the performance of R-peak detection algorithms on textile ECG, the five common R-peak detection algorithms: Pan & Tompkins, improved Pan & Tompkins (in Biosppy), Hamilton, Engelse, and Khamis, were tested on textile- like MITDB. This work provides an approach to generating synthetic textile ECG signals, and facilitating the development, testing, and evaluation of signal processing algorithms for textile ECGs.