Improving deep-learning electrocardiogram classification with an effective coloring method

Chen, Wei-Wen; Tseng, Chien-Chao; Huang, Ching-Chun; Lu, Henry Horng-Shing

doi:10.1016/j.artmed.2024.102809

Cited by 3 publications

References 35 publications

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Real-Time PVC Recognition System Design Based on Multi-Parameter SE-ResNet

Li,

Liu,

Sun

et al. 2024

IEEE Access

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The real-time and accurate detection of premature ventricular contractions (PVC) in patients is of great significance for preventing the occurrence of high-risk events such as sudden cardiac death and guiding cardiac surgical procedures such as radiofrequency ablation. To improve the diagnostic accuracy and real-time performance, and expand application scenarios, an economical wearable PVC real-time auxiliary diagnosis system based on the multi-parameter squeeze excitation residual network (MP-SE-ResNet) is proposed. We have realized the real-time acquisition, processing, and wireless transmission of dynamic ECGs based on ESP32, furthermore, realized the PVCs recognition based on MP-SE-ResNet. Using the lead-II ECGs in the MIT-BIH arrhythmia databases as training samples, and the network was evaluated using the remainder of this dataset and data recorded by our device, respectively. The accuracy of the MIT-BIH dataset reached 99.34%, and the sensitivity and specificity of PVC recognition reached 98.26% and 99.64%, respectively. Using the ECGs recorded by our system, we achieved the following results: the accuracy was 94.07%, the sensitivity and specificity of PVC were 92.76% and 97.63%, respectively. The experimental results show that the system meets the requirements of remote monitoring and auxiliary diagnosis. Therefore, it provides a new method and design idea for wearable remote arrhythmia monitoring and auxiliary diagnosis.

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Real-Time PVC Recognition System Design Based on Multi-Parameter SE-ResNet

Li,

Liu,

Sun

et al. 2024

IEEE Access

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Cardiac Arrhythmia Diagnosis Using Deep Learning: A 1D CNN-GRU Approach with Multiclass SVM and DWT Analysis

Vijay Anand,

et al. 2024

2024 Tenth International Conference on Bio Signals, Images, and Instrumentation (ICBSII)

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ECG Signal Classification based on combined CNN Features and Optimised Support Vector Machine

HASSANI,

BOUMEHRAZ,

HAMZI

2024

EEA

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The electrocardiogram (ECG) is a visual depiction of the electrical activity of the heart. It is utilised to detect and diagnose different cardiac conditions. Over the last decade, the categorization of cardiac conditions based on electrocardiogram signals have become very significant for both patients and medical professionals. This article presents a novel method for classifying Electrocardiogram signals into arrhythmia (ARR), congestive heart failure (CHF), or normal sinus rhythm (NSR) using combined deep learning features and optimised Support Vector Machine (Op-SVM). First, to perform classification via Deep Learning (DL)the continuous wavelet transform (CWT) was used to transform one-dimensional (1-D) ECG signals into two-dimensional (2-D) images (scalograms) which are sent then to two pre-trained convolutional neural networks (CNN) architectures (ResNet50 and DenseNet201). Next, the features extracted from both CNNs were combined and fed to the SVM classifier. To enhance the performance of the classifier, Bayesian optimisation was used to optimise its hyperparameters. The suggested method was tested using a public dataset (PhysioNet) and evaluated using performance metric techniques. It succeeded in achieving values of 99.44 % for accuracy (Acc), 99.44 % for sensitivity (Sen), 99.72 % for specificity (Sp), and 99.44 % for precision (Pr), respectively, which are exceptional compared to the values produced by models considered to be state-of-the-art. Our results showed that the suggested method is suitable for in-clinic application in diagnosing cardiac conditions using ECG signals.

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Improving deep-learning electrocardiogram classification with an effective coloring method

Cited by 3 publications

References 35 publications

Real-Time PVC Recognition System Design Based on Multi-Parameter SE-ResNet

Real-Time PVC Recognition System Design Based on Multi-Parameter SE-ResNet

Cardiac Arrhythmia Diagnosis Using Deep Learning: A 1D CNN-GRU Approach with Multiclass SVM and DWT Analysis

ECG Signal Classification based on combined CNN Features and Optimised Support Vector Machine

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