Telemedical transport layer security based platform for cardiac arrhythmia classification using quadratic time–frequency analysis of HRV signal

Abstract

The heart rate variability signal is a valuable tool for cardiovascular system diagnostics. Processing this signal detects arrhythmia during long-term cardiac monitoring. It is also analyzed to recognize abnormalities within the autonomic nervous system. Processing this signal helps in detecting various pathologies, such as atrial fibrillation (AF), supraventricular tachycardia (SVT), and congestive heart failure (CHF). As a beneficial alternative to the commonly used HRV spectrum analysis, quadratic time–frequency analysis of HRV signals could be helpful in heart pathology detection. Indeed, in this study, we have created a client-server paradigm deployed as a telemedical platform for real-time remote monitoring of the cardiovascular function in patients suffering from arrhythmia. This platform detects arrhythmia in real-time by deploying time–frequency analysis, feature extraction, feature selection, and classification of Heart Rate Variability (HRV) signals. We gathered all these functionalities in a Graphical User Interface (GUI) in addition to data acquisition. As a client, a Raspberry Pi Zero ensures data acquisition and connects to a server over TCP/IP that involves a 4G/3G connection encrypted through the transport layer security (TLS). This telemedical tool continuously monitors the heart rate variability. In the case of an alarm, medical professionals may immediately interact with their patients in the hospital or at home.