During an exercise, muscle contraction occurs for body movements and requires more amount of oxygen. Then, the heart beating rate tends to increases during an exercise [1]. While the heart beating rate varies, the sympathetic nerve, respiratory, and blood circulation systems adapt to the muscle contracting. The sympathetic nerve system with a calcium ion handling problem causes abnormal myocardial actions and can develop arrhythmia [2]. However, the heartbeat electric signal variations through wearable electrodes are less noticeable without an electric signal data screening process. The proposed electric signal data screening process determines whether the ischemic heartbeat is present and evaluates the T wave variation characteristics from the standard deviation of amplitude and beat-by-beat duration comparison. The T-wave represents depolarization and repolarization of the left ventricle. The duration of the T-wave is from the Q point of the QRS complex and represents the QT interval. QT interval prolongation with the T-wave morphology becomes a biomarker of acute myocardial ischemia and represents the ventricular repolarization abnormalities [3]. This paper characterizes depolarization and repolarization actions of the left ventricle from the electric signal outputs of electrocardiography (ECG or EKG). The proposed electric signal data screening process evaluates beat-to-beat information of amplitude and duration of T-waves among electric signals from multi-electrode EKG. Among the experimental data in this paper, the heartbeat showing the maximum QT interval tended to have amplitude values with higher standard deviation. For the experimental 12-lead EKG data, certain beat-order tended to represent the maximum QT interval through the 12 channels. The experimental dataset from the channel with abnormal QT interval prolongation represented more dispersed QT intervals than the dataset from the other channel without abnormal QT interval prolongation.

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J-R. Park and J. E. Park, “A T-wave variation characteristics evaluation algorithm for ischemic heart beats,” International Journal of Health and Medical Sciences, vol. 3, no. 3, pp. 80-84, 2017.