Electrocardiogram (ECG) Circuit Design and Using the Random Forest to ECG Arrhythmia Classification

Pham, Van Nam; Tran, Hoai-Nam

doi:10.1007/978-3-031-22200-9_54

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Electrocardiogram (ECG) classification, an essential domain in cardiovascular health, has seen the adoption of a broad array of methodologies ranging from conventional machine learning (ML) techniques to advanced deep learning (DL) strategies. Classical ML approaches like Support Vector Machines (SVMs) [10][11][12][13] and Random Forests [14][15][16][17][18] have demonstrated their efficacy in ECG signal classification [19,20]. However, despite their competency in certain scenarios, these techniques often struggle with complex, high-dimensional patterns characteristic of ECG signals [8,9], potentially leading to subpar performance and highlighting the need for enhanced methodologies.…”

Section: Related Workmentioning

confidence: 99%

Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach

Benchaira,

Bitam

2024

Biomed. Phys. Eng. Express

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Rapid and accurate electrocardiogram (ECG) signal classification is crucial in high-stakes healthcare settings. However, existing computational models often struggle to balance high performance with computational efficiency. This study introduces an innovative computational framework that combines transfer learning with traditional machine learning to optimize ECG classification. We use a pre-trained Stacked Convolutional Neural Network (SCNN) to generate high-dimensional feature embeddings, which are then evaluated by an array of machine learning classifiers. Our models demonstrate exceptional performance, particularly when utilizing embeddings from SCNNs trained on diverse datasets. This underscores the importance of data diversity in improving classifier discrimination. Notably, Multilayer Perceptrons (MLPs) stand out for their ability to balance computational efficiency with strong performance, achieving test F1-scores of 0.94 and 1.00 in multi-class and binary tasks on the CinC2017 dataset, and 0.85 and 0.99 on the CPSC2018 dataset. Our approach consistently outperforms existing methods, setting new benchmarks in ECG classification. The synergy between deep learning-based feature extraction and traditional machine learning through transfer learning offers a robust, efficient, and adaptable strategy for ECG classification, addressing a critical research gap and laying the groundwork for future advancements in this crucial healthcare field.

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Section: Related Workmentioning

confidence: 99%

Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach

Benchaira,

Bitam

2024

Biomed. Phys. Eng. Express

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“…Most classical machine learning methods based on the extraction of features from single-channel ECG signal have been proposed, such as random forest [7,8] , support vector machines (SVMs) [9,10], artificial neural networks [11,12,13], KNN [14,15,16], and hidden Markov models [17,18]. All these studies have achieved much better performances.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Automatic Detection of Arrhythmias Using the Unsupervised Convolutional Neural Network

Zhang

Yao

Gao

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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In recent years, various models based on convolutional neural networks (CNN) have been proposed to solve the cardiac arrhythmia detection problem and achieved saturated accuracy. However, these models are often viewed as “blackbox” and lack of interpretability, which hinders the understanding of cardiologists, and ultimately hinders the clinical use of intelligent terminals. At the same time, most of these approaches are supervised learning and require label data. It is a time-consuming and expensive process to obtain label data. Furthermore, in human visual cortex, the importance of lateral connection is same as feed-forward connection. Until now, CNN based on lateral connection have not been studied thus far. Consequently, in this paper, we combines CNNs, lateral connection and autoencoder (AE) to propose the building blocks of lateral connection convolutional autoencoder neural networks (LCAN) for cardiac arrhythmia detection, which learn representations in an unsupervised manner. Concretely, the LCAN contains a convolution layer, a lateral connection layer, an AE layer, and a pooling layer. The LCAN detects salient wave features through the lateral connection layer. The AE layer and competitive learning is used to update the filters of the convolution network—an unsupervised process that ensures similar weight distribution for all adjacent filters in each convolution layer and realizes the neurons’ semantic arrangement in the LCAN. To evaluate the performances of the proposed model, we have implemented the experiments on the well-known MIT–BIH Arrhythmia Database. The proposed model yields total accuracies and kappa coefficients of 98% and 0.95, respectively. The experiment results show that the LCAN is not only effective, but also a useful tool for arrhythmia detection.

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“…The result shows the sensitivity and specificity of the classifier and further accuracy can be improved by modifying the technique. To overcome the drawback of entering the number of trees manually as a parameter, the researchers [18] introduced an enhanced random forest method that uses simulated annealing (SA) algorithm for an optimal number of trees calculation. The enhanced random forest method needs to be analysed with the other classifiers.…”

Section: Introductionmentioning

confidence: 99%

An Improved Firefly Optimization Algorithm for Analysis of Arrhythmia Types

Sinnoor,

Janardhan

2023

IJRITCC

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Irregular heartbeats rhythm is the result of arrhythmia condition which can be a threat to life if not treated at the early stage. If it is necessary to know the type of arrhythmia to treat the patient appropriately. The traditional method is complex and an efficient algorithm is required to diagnose. An improved firefly optimization algorithm is proposed to analyze arrhythmia types. Four performance measures confirm the model's effectiveness and experimental evaluation shows that it achieves a sensitivity of 86.27%, accuracy of 86.14%, precision of 87.52%, and specificity of 87.37% in arrhythmia-type classification. The algorithm can effectively classify the arrhythmia types with high accuracy and specificity.

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Electrocardiogram (ECG) Circuit Design and Using the Random Forest to ECG Arrhythmia Classification

Cited by 5 publications

References 7 publications

Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach

Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach

A Novel Method for Automatic Detection of Arrhythmias Using the Unsupervised Convolutional Neural Network

An Improved Firefly Optimization Algorithm for Analysis of Arrhythmia Types

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