Abstract
Batch ECG signal sequences provide real-time, dynamic measurements for groups of heart disease patients as special biomedical big datasets. To extract various diseases information, we propose a new method using multiple statistical probability distributions to process batch ECG signal sequences on variant maps. Using this approach, it is more convenient to identify normal ECG datasets and abnormal ECG datasets on distinct distributions. Sample cases are illustrated.
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