TY - GEN
T1 - ECG arrhythmia Discrimination using SVM and Nonlinear and Non-stationary Decomposition
AU - Abdalla, Fakheraldin Y.O.
AU - Wu, Longwen
AU - Ullah, Hikmat
AU - Mkindu, Hassan
AU - Nie, Yuting
AU - Zhao, Yaqin
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - ECG signals represent the all heart's electrical activity. Consequently, it performs a key function in the diagnosis of cardiac disorder and arrhythmia detection. Based on small variations in the ECG's amplitude, length, and morphology, Computer-assisted diagnosis has to turn out to be a recognized method to classifying the heartbeats of one-of-a-kind types of arrhythmia. Due to the nature of the ECG signal, a classification method was created based on the techniques of time-frequency decomposition. Discrete Wavelet Transform (DWT) was used to acquire various frequency components where Multiresolution Analysis (MRA) was implied. Based on these frequency components (MARs), the features vector was calculated using four statistical parameters. Average Power (AP), Dispersion Coefficient (CD), Sample Entropy (SE) and Singular Values (SV) were calculated from 9 RAMs as statistical parameters. SVM was then presented to use the features vector and discriminate ten distinct heartbeats of arrhythmia downloaded from the MIT-BIH database in the Physionet. Confusion matrix, Sensitivity (SEN), specificity (SPE), precision (PRE) was used and calculated to assess the efficiency of the suggested technique and compare it with the past algorithms. The performance of the suggested was discovered to be better than the current techniques, and the accuracy was 99.84
AB - ECG signals represent the all heart's electrical activity. Consequently, it performs a key function in the diagnosis of cardiac disorder and arrhythmia detection. Based on small variations in the ECG's amplitude, length, and morphology, Computer-assisted diagnosis has to turn out to be a recognized method to classifying the heartbeats of one-of-a-kind types of arrhythmia. Due to the nature of the ECG signal, a classification method was created based on the techniques of time-frequency decomposition. Discrete Wavelet Transform (DWT) was used to acquire various frequency components where Multiresolution Analysis (MRA) was implied. Based on these frequency components (MARs), the features vector was calculated using four statistical parameters. Average Power (AP), Dispersion Coefficient (CD), Sample Entropy (SE) and Singular Values (SV) were calculated from 9 RAMs as statistical parameters. SVM was then presented to use the features vector and discriminate ten distinct heartbeats of arrhythmia downloaded from the MIT-BIH database in the Physionet. Confusion matrix, Sensitivity (SEN), specificity (SPE), precision (PRE) was used and calculated to assess the efficiency of the suggested technique and compare it with the past algorithms. The performance of the suggested was discovered to be better than the current techniques, and the accuracy was 99.84
KW - DWT
KW - Features extraction
KW - PCA
KW - SVM
UR - https://www.scopus.com/pages/publications/85081346620
U2 - 10.1109/ISSPIT47144.2019.9001889
DO - 10.1109/ISSPIT47144.2019.9001889
M3 - 会议稿件
AN - SCOPUS:85081346620
T3 - 2019 IEEE 19th International Symposium on Signal Processing and Information Technology, ISSPIT 2019
BT - 2019 IEEE 19th International Symposium on Signal Processing and Information Technology, ISSPIT 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2019
Y2 - 10 December 2019 through 12 December 2019
ER -