Abstract: 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.

Soha Ahmed, Ali Hilal-Alnaqbi, Mohamed Al Hemairy and Mahmoud Al Ahmad. “ECG Abnormality Detection Algorithm”. International Journal of Advanced Computer Science and Applications (IJACSA) 9.8 (2018). http://dx.doi.org/10.14569/IJACSA.2018.090827

@article{Ahmed2018,
title = {ECG Abnormality Detection Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090827},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090827},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {8},
author = {Soha Ahmed and Ali Hilal-Alnaqbi and Mohamed Al Hemairy and Mahmoud Al Ahmad}
}