In this paper, a novel real-time algorithm for detecting ischemia in the ECG signal is proposed. The goal of this research is to meet the requirements of some smart cardiac home care devices, which can automatically diagnose the ECG and detect the heart risks outside the hospital, especially heart ischemia without symptoms in their early stages. The algorithm is developed based on a real time R peak detector, time domain traditional ECG parameters, the advanced morphologic parameters from Karhunen-Loève transform, and the adaptive neurofuzzy logic classification. Besides, in order to improve the reliability of our algorithm, several significant constraints of the ECG signal are considered. As a result, the ischemia episodes can be detected if the ischemic alteration persists longer than one minute in the ECG signal.

In following the directive "ambulant medical treatment is preferable to in hospital treatment" the home care area is becoming more and more important in western societies. In this paper a system for monitoring cardiological risk patients is presented. Special attention was paid to an automatic detection of life-threatening events and the initiation of immediate help.

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Abstract

The algorithms used in automated external defibrillators (AED) do not provide the necessary reliability of detection of fibrillation, especially in presence of artifacts. The system described in this article allows detecting different artifacts, improving the detection quality. Also an approach is suggested, to extend the AED functions to detect pulseless electrical activity. The new algorithm uses the impedance signal as an additional parameter to diagnose the cardiac arrest. The parallel analysis of ECG and impedance signals allows detecting noise, motion, respiration and hemodynamic parameters. The comparative tests of the algorithm demonstrated the excellent performance of the algorithm.