The unsupervised pattern recognition for the ECG signal features detection

Smart devices and cloud-based technologies allow continuous non-invasive bio-signal recordings in daily life activities. There are many programs and applications that allow monitoring health condition and sharing that information with other people. However, bio-signals that are recorded in real nonstationary conditions are highly contaminated with noise that depends on various activities. Improper signal processing algorithms may lead to a faulty diagnosis or inaccurate decision-making results. Movement contaminated bio-signals require adaptive filtering and feature extraction algorithms because low frequency trends are mostly unstable, and the noise may cause higher impulses than the signal itself. That is why ordinary ECG signal parameters extraction algorithms fail in real time signal processing. In this research signals were filtered using two different methods: Butterworth filter for the high frequency noise reduction and BEADS algorithm for the low frequency noise removal. A new ECG feature extraction algorithm was proposed that is based on the unsupervised MTEO algorithm together with additional local extremum search. The proposed algorithm was compared with other methods on the MIT-BIH database. The additional comparison was made with Pan-Tompkins and k-TEO algorithms on ECG signals that were recorded in movement. Several examples are presented that show how each algorithm performs during training sessions with various intensity levels. The suggested method performed in linear time complexity that made it sufficient for the real time data processing. The obtained ECG parameter values could be used for di-agnostics and fatigue recognition in health monitoring processes.

Automated summary

This study investigates the application of continuous non-invasive bio-signal recordings in daily life activities using smart devices and cloud-based technologies. A new ECG feature extraction algorithm for movement-contaminated signals was proposed and validated through comparisons with other methods. This research is significant for real-time data processing and health monitoring.