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. 2023 Sep 6;13(18):2867.
doi: 10.3390/diagnostics13182867.

Reseek-Arrhythmia: Empirical Evaluation of ResNet Architecture for Detection of Arrhythmia

Shams Ul Haq  1 , Sibghat Ullah Bazai  1 , Ali Fatima  1 , Shah Marjan  2 , Jing Yang  3 , Lip Yee Por  3 , Mohd Anjum  4 , Sana Shahab  5 , Chin Soon Ku  6
Affiliations

Reseek-Arrhythmia: Empirical Evaluation of ResNet Architecture for Detection of Arrhythmia

Shams Ul Haq et al. Diagnostics (Basel). .

Abstract

Arrhythmia is a cardiac condition characterized by an irregular heart rhythm that hinders the proper circulation of blood, posing a severe risk to individuals' lives. Globally, arrhythmias are recognized as a significant health concern, accounting for nearly 12 percent of all deaths. As a result, there has been a growing focus on utilizing artificial intelligence for the detection and classification of abnormal heartbeats. In recent years, self-operated heartbeat detection research has gained popularity due to its cost-effectiveness and potential for expediting therapy for individuals at risk of arrhythmias. However, building an efficient automatic heartbeat monitoring approach for arrhythmia identification and classification comes with several significant challenges. These challenges include addressing issues related to data quality, determining the range for heart rate segmentation, managing data imbalance difficulties, handling intra- and inter-patient variations, distinguishing supraventricular irregular heartbeats from regular heartbeats, and ensuring model interpretability. In this study, we propose the Reseek-Arrhythmia model, which leverages deep learning techniques to automatically detect and classify heart arrhythmia diseases. The model combines different convolutional blocks and identity blocks, along with essential components such as convolution layers, batch normalization layers, and activation layers. To train and evaluate the model, we utilized the MIT-BIH and PTB datasets. Remarkably, the proposed model achieves outstanding performance with an accuracy of 99.35% and 93.50% and an acceptable loss of 0.688 and 0.2564, respectively.

Keywords: ECG; arrhythmia detection; convolutional blocks; convolutional neural network; deep learning; heart disease; identity blocks.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Graphical representation of a normal rhythm and different arrhythmia conditions: (a) Displays the normal rhythm; (b) Describes the tachycardia arrhythmia heartbeat; (c) Describes the bradycardia arrhythmia heartbeat; (d) Shows an irregular heartbeat.
Figure 2
Figure 2
Main waves from the normal rhythm with the specifications.
Figure 3
Figure 3
Different steps are followed in the testing and training phases.
Figure 4
Figure 4
Shows the visual representation of the proposed model’s architecture, using ResNet50 as a base model. The architecture has various building blocks, skip connections, and convolutional layers.
Figure 5
Figure 5
The convolutional block specifications.
Figure 6
Figure 6
The layers used in each identity block.
Figure 7
Figure 7
Demonstrates the model’s accuracy for the MIT-BIH and PTB datasets.
Figure 8
Figure 8
The model training and validation specificity for the MIT-BIH and PTB datasets.
Figure 9
Figure 9
The model training and validation sensitivity for the MIT-BIH and PTB datasets.
Figure 10
Figure 10
The precision of the suggested model during training and validation using MIT-BIH and PTB datasets.
Figure 11
Figure 11
The model training and validation recall for the MIT-BIH and PBT datasets.
Figure 12
Figure 12
Loss of the suggested model during training and validation using MIT-BIH and PTB datasets.
See this image and copyright information in PMC

References

    1. Walker F.M. Ph.D. Thesis. University of California; Oakland, CA, USA: Sep, 2015. Advances at the Interface: Merging Information Technologies with Genomic Methodologies.
    1. Khoury A.L., Jernigan E.G., Nelson J.S., Strassle P.D., Gonzalez V.J., Essaid L., Chowdhury M.H., Long J.M., Sharma M.S. The University of north carolina heart-lung transplant experience: Historical perspective and notes on surveillance for very long-term survivors. J. Patient Care. 2021;7:161.
    1. Su Q., Huang Y., Wu X., Zhang B., Lu P., Lyu T. Atrial Fibrillation Detection Based on a Residual CNN Using BCG Signals. Electronics. 2022;11:2974. doi: 10.3390/electronics11182974. - DOI
    1. Atal D.K., Singh M. Arrhythmia classification with ECG signals based on the optimization-enabled deep convolutional neural network. Comput. Methods Programs Biomed. 2020;196:105607. doi: 10.1016/j.cmpb.2020.105607. - DOI - PubMed
    1. Almutairi H., Hassan G.M., Datta A. Classification of Obstructive Sleep Apnoea from single-lead ECG signals using convolutional neural and Long Short Term Memory networks. Biomed. Signal Process. Control. 2021;69:102906. doi: 10.1016/j.bspc.2021.102906. - DOI

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