Mohd Zubir Suboh1,2, Nazrul Anuar Nayan1,3,*, Noraidatulakma Abdullah4,5, Nurul Ain Mhd Yusof4, Mariatul Akma Hamid4, Azwa Shawani Kamalul Arinfin4, Syakila Mohd Abd Daud4, Mohd Arman Kamaruddin4, Rosmina Jaafar1, Rahman Jamal4
CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1111-1132, 2022, DOI:10.32604/cmc.2022.022123
Abstract A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The Malaysian Cohort project. An automated peak detection algorithm to detect nine fiducial points of electrocardiogram (ECG) was developed. Forty-eight features were extracted in both time and frequency domains, including statistical features obtained from heart rate variability and Poincare plot analysis. These include five new features derived from spectrum counts of five different frequency ranges. Feature selection was then made based on p-value and correlation matrix. Selected features were used as input for five classifiers of… More >
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