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In this paper, we suggest and analyze a new modified predictor-corrector algorithm for solving a nonconvex generalized variational inequality using the auxiliary principle technique; the convergence of the algorithm requires the partially relaxed strong monotonicity of the operator.

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.

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