Electrocardiogram heartbeat classification based on a deep convolutional neural network and focal loss

Romdhane, Taissir Fekih; Alhichri, Haikel; Ouni, Ridha; Atri, Mohamed

doi:10.1016/j.compbiomed.2020.103866

Cited by 108 publications

(68 citation statements)

References 31 publications

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“…The results of other methods are the same as they are reported in the literature. However, some methods VGGNet [ 49 ], MS-CNN [ 23 ] are implemented, and results are compared in Table 7 . The models are also compared based on F1-Score and G-Score measure given in Table 8 .…”

Section: Resultsmentioning

confidence: 99%

O-WCNN: an optimized integration of spatial and spectral feature map for arrhythmia classification

Jangra

Dhull

Singh

et al. 2021

Complex Intell. Syst.

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The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement.

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Section: Resultsmentioning

confidence: 99%

O-WCNN: an optimized integration of spatial and spectral feature map for arrhythmia classification

Jangra

Dhull

Singh

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…The focal loss function can effectively handle the imbalanced class problem by focusing the loss on minority classes. The results revealed that the focal loss function improved the classification accuracy and the overall metrics with accuracy of 98.41%, overall F1-score of 98.38% and overall recall of 98.41% [151]. Acharya et al employed CNN technique to automatically detect arrhythmias, which eliminates the tedious work of pretreatment and feature engineering [131].…”

Section: Disease Diagnosismentioning

confidence: 99%

Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

Xie

Zhou

et al. 2020

Sensors

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Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina, myocardial infarction, and ischemic heart failure, are the leading cause of death globally. Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death. Electrocardiogram (ECG) records the electrical impulses generated by heart muscles, which reflect regular or irregular beating activity. Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient’s ECG signal, which have achieved great success in recent years. Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here. The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy and robustness. Portable devices enable users to monitor their cardiovascular status at any time, bringing new scenarios as well as challenges to the application of ECG algorithms. Computational diagnostic techniques for ECG signal analysis show great potential for helping health care professionals, and their application in daily life benefits both patients and sub-healthy people.

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“…Research shows that by integrating RR interval features, the performance of heartbeat classification can be significantly improved (De Chazal, O'Dwyer & Reilly, 2004;Mondéjar-Guerra et al, 2019;Sannino & De Pietro, 2018). Romdhane et al (2020) try to use an improved heartbeat segmentation method to make CNN capture RR interval information, but in their work, CNN can only extract the previous RR interval information at most. This is due to the incomplete division of the right interval.…”

Section: Related Workmentioning

confidence: 99%

A hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss

Wang

Yang³

et al. 2020

PeerJ Computer Science

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Background Heart arrhythmia, as one of the most important cardiovascular diseases (CVDs), has gained wide attention in the past two decades. The article proposes a hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss. Methods In the method, a convolution neural network is used to extract the morphological features. The reason behind this is that the morphological characteristics of patients have inter-patient variations, which makes it difficult to accurately describe using traditional hand-craft ways. Then the extracted morphological features are combined with the RR intervals features and input into the multilayer perceptron for heartbeat classification. The RR intervals features contain the dynamic information of the heartbeat. Furthermore, considering that the heartbeat classes are imbalanced and would lead to the poor performance of minority classes, a focal loss is introduced to resolve the problem in the article. Results Tested using the MIT-BIH arrhythmia database, our method achieves an overall positive predictive value of 64.68%, sensitivity of 68.55%, f1-score of 66.09%, and accuracy of 96.27%. Compared with existing works, our method significantly improves the performance of heartbeat classification. Conclusions Our method is simple yet effective, which is potentially used for personal automatic heartbeat classification in remote medical monitoring. The source code is provided on https://github.com/JackAndCole/Deep-Neural-Network-For-Heartbeat-Classification.

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Electrocardiogram heartbeat classification based on a deep convolutional neural network and focal loss

Cited by 108 publications

References 31 publications

O-WCNN: an optimized integration of spatial and spectral feature map for arrhythmia classification

O-WCNN: an optimized integration of spatial and spectral feature map for arrhythmia classification

Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

A hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss

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