A pervasive energy-efficient ECG monitoring approach for detecting abnormal cardiac situations

Mobile and pervasive ECG monitoring systems require continuous connectivity with server-side ECG analyser for instantaneously detecting abnormal cardiac situations. Normally, these systems generate a large amount of data, resulting in a high energy expenditure with data transmission on pervasive ECG platform. In this context, data reduction mechanisms can be applied for saving transmission energy of pervasive ECG monitoring devices, maximizing the availability and confiability of ECG monitoring systems. This paper proposes an pervasive energy-efficient ECG monitoring approach for detecting abnormal cardiac situations for ubiquitous health systems. The data reduction approach based on error prediction maximize the life time of pervasive ECG monitoring device by gathering and reducing heart signal before sending it to server-side ECG analyzer application. Moreover, Pearson's Coefficient (correlation rate) is applied on the proposed data reduction approach, enhancing the quality of monitored heart signal.