ECG-based heartbeat classification using exponential-political optimizer trained deep learning for arrhythmia detection

Choudhury, Avishek; Shankar, Vuppu; Singh, Suryabhan Pratap; Kumar, M.; Kumar, Sanjay Nakharu Prasad

doi:10.1016/j.bspc.2023.104816

Cited by 9 publications

(2 citation statements)

References 37 publications

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“…The fundamental steps to compute-diagnose Ha symptoms or its related abnormalities using ECG signal include: processing ECG signal, segmentation of heartbeat, feature extraction, and categorization. Identified in the literature, methods for ECG signal are but not limited to continuous wavelet transform (CWT), Empirical Mode Decomposition (EMD), Discrete wavelet Transform (DWT), Empirical Wavelet Transform (EWT), and Signal Energy Thresholding (SET) coefficients [2,14,18,19]. In segmentation, an ECG signal based on an R-peak is utilized to detect the waves, segments, and intervals and compare them with well-known patterns using their temporal and morphological properties [12,20,21].…”

Section: Related Workmentioning

confidence: 99%

A Cost-Based Dual ConvNet-Attention Transfer Learning Model for ECG Heartbeat Classification

Victor,

Chew,

Khaw

et al. 2023

JIWE

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The heart is a very crucial organ of the body. Concerted efforts are constantly put forward to provide adequate monitoring of the heart. A heart disorder is reported to cause a lot of hidden ailments resulting in numerous deaths. Early heart monitoring using an electrocardiogram (ECG) through the advancement of computer-aided diagnostic (CAD) systems is widely used. Meanwhile, the use of human reading of ECG results are faced with many challenges of inaccurate and unreliable interpretations. Over two decades, studies provided artificial intelligence (AI) technique using machine learning (ML) algorithms as a fast and reliable technique for ECG heartbeat classification. Moreover, in recent times, deep learning (DL) techniques have been focused on providing automatic feature extraction and better classification performance. On the other hand, the challenge with the ECG data is its imbalance nature. Therefore, this paper proposes a cost-based dual convolutional attention transfer DL model for ECG classification. The proposed model uses PhysionNet-MIT-BIH and Physikalisch-Technische Bundesanstalt (PTB) Diagnostics datasets. The first part uses the MIT-BIH for ECG categorization, while representations learned from the first classifier are used for PTB analysis through transfer learning (TL). The proposed model is evaluated and compared with well-performing conventional ML models based on their F1-score and accuracy scores. Our experimental finding show that the proposed model outperformed the well-performing ML models as well as competitive with past studies for both the classification and TL part, having obtained 98.45% for both F1-score and accuracy. The proposed model is applicable to real-life trials and experiments for ECG heartbeat and other similar domains.

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Section: Related Workmentioning

confidence: 99%

A Cost-Based Dual ConvNet-Attention Transfer Learning Model for ECG Heartbeat Classification

Victor,

Chew,

Khaw

et al. 2023

JIWE

View full text Add to dashboard Cite

show abstract

“…Machine Learning is a technique for extracting knowledge from massive amounts of data. It comprises a set of rules, methods, or complex "transfer functions" that can be used to discover intriguing patterns or estimate behaviour in a wide range of applications (Abu Al-Haija et al, 2022;Choudhury et al, 2023;Dua & Du, 2016;Mangal et al, 2023;Prasad Yadav et al, 2023;Sinha & Sharma, 2021). The machine learning techniques use training data to acquire complex patternmatching capabilities.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection System

Singh,

Prakash,

Kumar

et al. 2024

Journal of Database Management

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The use of encrypted data, the diversity of new protocols, and the surge in the number of malicious activities worldwide have posed new challenges for intrusion detection systems (IDS). In this scenario, existing signature-based IDS are not performing well. Various researchers have proposed machine learning-based IDS to detect unknown malicious activities based on behaviour patterns. Results have shown that machine learning-based IDS perform better than signature-based IDS (SIDS) in identifying new malicious activities in the communication network. In this paper, the authors have analyzed the IDS dataset that contains the most current common attacks and evaluated the performance of network intrusion detection systems by adopting two data resampling techniques and 10 machine learning classifiers. It has been observed that the top three IDS models—KNeighbors, XGBoost, and AdaBoost—outperform binary-class classification with 99.49%, 99.14%, and 98.75% accuracy, and XGBoost, KNneighbors, and GaussianNB outperform in multi-class classification with 99.30%, 98.88%, and 96.66% accuracy.

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Electrocardiogram-based heart disease prediction using hybrid deep feature engineering with sequential deep classifier

Golande,

Pavankumar

2024

Multimed Tools Appl

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ECG-based heartbeat classification using exponential-political optimizer trained deep learning for arrhythmia detection

Cited by 9 publications

References 37 publications

A Cost-Based Dual ConvNet-Attention Transfer Learning Model for ECG Heartbeat Classification

A Cost-Based Dual ConvNet-Attention Transfer Learning Model for ECG Heartbeat Classification

Intrusion Detection System

Electrocardiogram-based heart disease prediction using hybrid deep feature engineering with sequential deep classifier

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