A proposed two-step cross-correlation algorithm for real-time ECG monitoring demonstrated 100% accuracy in identifying 7 simulated cardiac abnormalities while reducing processing time.
May support real-time ECG monitoring for early detection; leaves open prospective clinical validation before practice change.
The monitoring and early detection of abnormalities in the cardiac cycle morphology have significant impact on the prevention of heart diseases and their associated complications. Electrocardiogram (ECG) is very effective in detecting irregularities of the heart muscle functionality. In this work, we investigate the detection of possible abnormalities in ECG signal and the identification of the corresponding heart disease in real-time using an efficient algorithm. The algorithm relies on cross-correlation theory to detect abnormalities in ECG signal. The algorithm incorporates two cross-correlations steps. The first step detects abnormality in a real-time ECG signal trace while the second step identifies the corresponding disease. The optimization of search-time is the main advantage of this algorithm.
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Ahmed et al. (2018) studied this question.
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