ECG Intervals and Segments Detection and Characterization for Analyzing Effects of Sahaja Yoga Meditation

Abstract

Meditation is expected to regularize autonomic nervous system and reduce metabolic movement, inciting physical and mental relaxation. A lot of research is being conducted to assess effects of different meditation techniques based on heart rate variability analysis or by observing characteristics of ECG. In this paper, effects of Sahaja Yoga meditation technique are analyzed based on ECG characteristics. For this, a new dataset from a total of 30 meditators and non-meditators recorded over a considerable period of 28 days, is used. The local ECG components like intervals and segments are detected using deep learning architecture. Furthermore, the detected fiducial points are localized and ECG characteristics are measured. Some ECG characteristics showed significant variations for meditators compared to non-meditators. From further results and analysis, it can be easily confirmed that sympathovagal balance is quickly attained and remains shifted to parasympathetic nervous system during meditation which helps not only to prevent stress, anxiety but also to cure cardiovascular diseases.