Paper Info
Title
Automated Detection of Left Bundle Branch Block from ECG Signal Utilizing the Maximal Overlap Discrete Wavelet Transform with ANFIS
Abstract
Left bundle branch block (LBBB) is a common disorder in the heart's electrical conduction system that leads to the ventricles' uncoordinated contraction. The complete LBBB is usually associated with underlying heart failure and other cardiac diseases. Therefore, early automated detection is vital. This work aimed to detect the LBBB through the QRS electrocardiogram (ECG) complex segments taken from the MIT-BIH arrhythmia database. The used data contain 2655 LBBB (abnormal) and 1470 normal signals (i.e., 4125 total signals). The proposed method was employed in the following steps: (i) QRS segmentation and filtration, (ii) application of the Maximal Overlapped Discrete Wavelet Transform (MODWT) on the ECG R wave, (iii) selection of the detailed coefficients of the MODWT (D2, D3, D4), kurtosis, and skewness as extracted features to be fed into the Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier. The obtained results proved that the proposed method performed well based on the achieved sensitivity, specificity, and classification accuracies of 99.81%, 100%, and 99.88%, respectively (F-Score is equal to 0.9990). Our results showed that the proposed method was robust and effective and could be used in real clinical situations.
Year
DOI
Venue
2022
10.3390/computers11060093
COMPUTERS
Keywords
DocType
Volume
left bundle branch block, Maximal Overlapped Discrete Wavelet, QRS complex, R-heartbeat, Adaptive Neuro-Fuzzy Inference System
Journal
11
Issue
ISSN
Citations 
6
2073-431X
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Bassam Al-Naami100.34
Hossam Fraihat200.68
Hamza Abu Owida300.34
Khalid Al-Hamad400.34
Roberto De Fazio501.69
Paolo Visconti602.37