Efficient ECG classification based on Chi-square distance for arrhythmia detection

Dhiah Al-Shammary; Mustafa Noaman Kadhim; Ahmed M. Mahdi; Ayman Ibaida; Khandakar Ahmedb

doi:10.1016/j.jnlest.2024.100249

Journal of Electronic Science and Technology, Volume. 22, Issue 2, 100249(2024)

Efficient ECG classification based on Chi-square distance for arrhythmia detection

Dhiah Al-Shammary1... Mustafa Noaman Kadhim1,*, Ahmed M. Mahdi1, Ayman Ibaida2 and Khandakar Ahmedb2 |Show fewer author(s)

Author Affiliations

¹College of Computer Science and Information Technology, University of Al-Qadisiyah, Al Diwaniyah, 58001, Iraq

²Intelligent Technology Innovation Lab, Victoria University, Melbourne, 3011, Australia

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Figures & Tables(14)

Fig. 1. Arrhythmia detection based Chi-square classification with the medical organization.

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Fig. 2. Schematic representation of the entire methodology, including the preprocessing, feature selection, and the Chi-square-based classifier.

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Fig. 3. Results of the preprocessing step (a) before and (b) after normalization.

Fig. 4. Chi-PSO.

Fig. 5. Confusion matrix.

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Fig. 6. Confusion matrices of (a) KNN, (b) RF, (c) SVM, (d) NB, (e) DT, and (f) our classifier (Chi-square).

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Fig. 7. Confusion matrices of (a) KNN, (b) RF, (c) SVM, (d) NB, (e) DT, and (f) our classifier (Chi-square).

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Fig. 8. Evaluated performance of classifiers (a) without and (b) with PSO feature selection.

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Table 1. Literature overview.

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Table 1. Literature overview.

Ref.	Year	Method	Accuracy	Limitation
[17]	2018	CNN	81.33%	The suggested model takes more time to achieve moderate levels of accuracy on the MIT-BIH dataset.
[18]	2016	GB + SVM	84.82%	The performance of the model was evaluated on a redundant dataset including 500 records, exhibiting average accuracy across many classes.
[19]	2019	Best ﬁrst selection (BFS) + RF	85.58%	A restricted dataset consisting of just 500 entries was used for 16 classes.
[20]	2022	RF + SVM	77.4%	The use of a traditional hybrid ML approach is associated with moderate precision levels and substantial processing costs.
[21]	2021	KNN / RF	89.83% / 90.21%	It takes a lot of data preparation (3-phase preprocessing) to get moderate results.
[22]	2021	BiLSTM	95%	The computational cost is increased due to the prolonged training time of a deep LSTM-BiLSTM model.
[23]	2022	MobileNetV2+ BiLSTM	91.7%	The model needs a longer period of training in order to provide significant outcomes.
[24]	2020	DNN	94%	The use of DNN in conjunction with a genetic algorithm results in a computational process that incurs significant computing expenses.
[25]	2022	Fusion of CNN	98.8%	The model’s ability to perform well across different scenarios is limited due to its dependence on a small dataset.
[26]	2019	DWT + Sparse autoencoder (S-AE)	96.82%	In the presence of elevated levels of noise, the suggested methodology has a limited capability for accurately identifying the precise positions of R-peaks.
[27]	2022	SVM + Deep CNN	99.2%	The research did not examine feature selection optimization methods, which may include identification of the most relevant deep features, classification performance optimization, and reduction of computational costs.

Table 2. Details of classes in the dataset.

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Table 2. Details of classes in the dataset.

Class	Description	Included beats	Number of extracted records
N	Non-ectopic beats	Normal beats, left bundle branch block, right bundle branch block, nodal (junctional) escape beat, and atrial escape beat	100
S	Supraventricular ectopic beats	Aberrated atrial premature beat, supraventricular premature beat, atrial premature beat, and nodal (junctional) premature beat	100
V	Ventricular ectopic beats	Ventricular escape beat and premature ventricular contraction	100
F	Fusion beats	Fusion of ventricular and normal beat	100
Q	Unknown beats	Paced beat, unclassified beat, and fusion of paced and normal beats	100

Table 3. Details of the implementation environment.
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Table 3. Details of the implementation environment.
Components Speciﬁcations
Processor 6th Generation Intel^® Core™ i7
RAM 16 GB
Editor Visual Studio Code
Programming language Python 3.12
Operating system Windows 10 Pro

Table 4. Comparison of our classifier’s performance with that of the standard classifiers without feature selection.
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Table 4. Comparison of our classifier’s performance with that of the standard classifiers without feature selection.
Classifier Accuracy (%) F1-score (%) Precision (%) Recall (%)
KNN 84 83.36 83.48 84
RF 77 73.20 76.18 77
SVM 78 72.34 83.14 78
NB 74 74.49 75.22 74
DT 83 84.29 87.01 83
Our classifier (Chi-square) 89 89.43 90.40 89

Table 5. Performance comparison of our classifier (Chi-square) and standard classifiers with feature selection.
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Table 5. Performance comparison of our classifier (Chi-square) and standard classifiers with feature selection.
Classifier Accuracy (%) F1-score (%) Precision (%) Recall (%)
KNN 96 96.06 96.23 96
RF 93 92.89 92.83 93
SVM 95 94.92 94.89 95
NB 82 83.26 86.91 82
DT 91 91.11 91.26 91
Our classifier (Chi-square) 98 98.03 98.18 98

Table 6. Comparison of the approach presented in this paper with similar work on the MIT-BIH arrhythmia dataset.

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Table 6. Comparison of the approach presented in this paper with similar work on the MIT-BIH arrhythmia dataset.

Ref.	Year	Feature selection	Classifier	Accuracy (%)
[33]	2020	Wavelet + Gabor filter	Bat-rider optimization algorithm deep CNN (BaRoA-DCNN)	93.19
[34]	2021	Rapid-ramp (RR) + ECG segments	Artificial deep neural network (ADNN) + Conv1D	94.70
[35]	2017	Linear discriminant analysis (LDA) + Principal component analysis (PCA) + DWT	Weighted k-nearest neighbors (WKNN)	96.12
[19]	2019	Three-filter feature selection (TFFS)	RF + BFS	85.58
[36]	2021	Wavelet	CNN	97.41
[37]	2020	Z-score + High order statistics (HOS) / DWT	RF	93.45
			KNN	72.56
			SVM	90.09
			LSTM	92.16
			Ensemble SVM	94.40
[38]	2019	Fractal dimension + Renyi entropy + Fuzzy entropy	KNN	94.5
[39]	2019	HOS + Local binary patterns (LBP) + RR	Ensemble SVM	94.50
[40]	2023	ECG segments + RR	Conv1D MF	96.48
[41]	2022	RR-intervals + Higher order statistics + DWT	EasyEnsemble	95.6
[42]	2021	Attention mechanism	Dual-level attentional (DLA) + Convolutional long short-term memory (CLSTM) neural network	88.76
[43]	2023	Augmented attention	CNN + Attention	96.19
[44]	2023	Lightweight transformer	CNN	97.66
[44]	2023	Lightweight transformer	Denoising autoencoder (DAE)	97.93
This work		PSO	Chi-square	98

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Dhiah Al-Shammary, Mustafa Noaman Kadhim, Ahmed M. Mahdi, Ayman Ibaida, Khandakar Ahmedb. Efficient ECG classification based on Chi-square distance for arrhythmia detection[J]. Journal of Electronic Science and Technology, 2024, 22(2): 100249

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Paper Information

Category:

Received: Dec. 20, 2023

Accepted: Apr. 2, 2024

Published Online: Aug. 8, 2024

The Author Email: Kadhim Mustafa Noaman (mustafa.noaman@qu.edu.iq)

DOI:10.1016/j.jnlest.2024.100249

Topics

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Instrumentation, Measurement and Metrology

Table 1. Literature overview.

Table 1. Literature overview.

Table 2. Details of classes in the dataset.

Table 2. Details of classes in the dataset.

Table 3. Details of the implementation environment.

Table 3. Details of the implementation environment.

Table 4. Comparison of our classifier’s performance with that of the standard classifiers without feature selection.

Table 4. Comparison of our classifier’s performance with that of the standard classifiers without feature selection.

Table 5. Performance comparison of our classifier (Chi-square) and standard classifiers with feature selection.

Table 5. Performance comparison of our classifier (Chi-square) and standard classifiers with feature selection.

Table 6. Comparison of the approach presented in this paper with similar work on the MIT-BIH arrhythmia dataset.

Table 6. Comparison of the approach presented in this paper with similar work on the MIT-BIH arrhythmia dataset.

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