Patient-Specific ECG Classification Based on Recurrent Neural Networks and Clustering Technique

Zhang, Chenshuang; Wang, Guijin; Zhao, Jingwei; Gao, Pengfei; Lin, Jianping; Yang, Huazhong

doi:10.2316/p.2017.852-029

Cited by 68 publications

(43 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shemi and Shareena [34] have found the capability of the double-density DTCWT to deal with the composite noise in the ECG signals. Zhang et al [35] have leveraged the DTCWT and the median filtering to denoise the ECG signals before doing arrhythmia classification by the recurrent neural network and the density based clustering technique. B'charri et al [36] have tuned the parameters of the DTCWT based denoiser to search for the best threshold function, the optimal threshold value, and the most suitable decomposition level to handle the noises in the ECG signals.…”

Section: D: Dual-tree Complex Wavelet Transform Can Work As a Capablementioning

confidence: 99%

Wavelets for Electrocardiogram: Overview and Taxonomy

2019

IEEE Access

View full text Add to dashboard Cite

Physiological and pathological information within electrocardiogram (ECG) is crucial for the diagnosis of heart diseases. Computer-aided diagnosis for the ECG signals has drawn growing research attention up to date. Automatic ECG analysis mainly includes signal denoising, wave detection, and heartbeat classification. These three issues are relevant that the signal denoising can help attenuate the noises and accentuate the typical waves in ECG signals for wave detection, and wave detection can help locate the typical ECG waves and acquire the diagnostically valuable heartbeats based on these waves for the heartbeat classification. The wavelet-based methods play important roles in the three issues, but these methods are scattered and unorganized in the literature. In order to manifest the value of these methods, this paper contributes an overview and taxonomy on them. This paper does the comprehensive summary and systematic categorization on the methods for signal denoising, wave detection, and heartbeat classification according to the deep analysis of their methodological characteristics. By doing so, this paper not only uncovers the inner mechanism that why wavelet-based methods are suitable for ECG analysis but also reveals the designing principles that these methods potentially follow. Finally, this paper has provided an outlook for the developing prospect of ''wavelets for ECG'' in the future. INDEX TERMS Wavelets for electrocardiogram, signal denoising, wave detection, heartbeat classification, overview and taxonomy. I. OUTLINE AND INTRODUCTION A. OUTLINE AND CONTRIBUTION

show abstract

Section: D: Dual-tree Complex Wavelet Transform Can Work As a Capablementioning

confidence: 99%

Wavelets for Electrocardiogram: Overview and Taxonomy

2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Recent advances in heartbeat classification are largely driven by deep neural networks (DNNs). In consideration of the sporadic occurrence of S -type heartbeats, which imposes a great challenge to DNN training, many DNN-based studies use synthetic heartbeats for model training and evaluation [1,8,9,15,16]. However, these efforts suffer from data leakage because, after augmentation, data is not partitioned patient-wise into training and test sets.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Two-Step DNN-Based Framework for Arrhythmia Detection

Rong

Sun

et al. 2020

Advances in Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

Heart arrhythmia is a severe heart problem. Automated heartbeat classification provides a cost-effective screening for heart arrhythmia and allows at-risk patients to receive timely treatments, which is a highly demanded but challenging task. Recent works have brought visible improvements to this area, but to identify the problematic supraventricular ectopic (S -type) heartbeats is still a bottleneck in most existing studies. This paper presents a two-step DNN-based framework to identify arrhythmia-related heartbeats. In the first step, a deep dual-channel convolutional neural network (DDCNN) is proposed to classify all heartbeat classes, except for the normal and S -type heartbeats. In the second stage, a central-towards LSTM supportive model (CLSM) is specially designed to distinguish S -type heartbeats from the normal ones. By processing heart rhythms in central-towards directions, CLSM learns and abstracts hidden temporal information between a heartbeat and its neighbors to reveal the deep differences between the two heartbeat types. As an improvement, we also propose a rule-based data augmentation method to solve the training data imbalance problem. The proposed framework is evaluated over three real-world ECG databases. The results show that our method outperforms the baselines in most evaluation metrics.

show abstract

“…DL may be categorized into different types based on the training methods such as recurrent neural networks (RNNs), deep neural networks (DNNs), convolutional neural networks (CNNs), and Long short-term memory (LSTM). Zhang et al [21] proposed an RNN and clustering-based method to find patient-specific ECG classification algorithms by using the MIT-BIH arrhythmia database. Al Rahhal et al [22] proposed a DNN based method to classify ECG signals using three different databases.…”

Section: Introductionmentioning

confidence: 99%

ECG Heartbeat Classification Based on Signal-to-Image Transformation Using Convolutional Neural Networks

Degirmenci¹,

Özdemir²,

Izci³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Electrocardiogram (ECG) is a method of recording the electrical activity of the heart and provides a diagnostic mean for heart-related diseases. An arrhythmia is any irregularity of heartbeat that causes an abnormality in one’s heart rhythm. Early detection of arrhythmia has great importance to prevent many diseases. Manual analysis of ECG signal is not sufficient for quickly identifying arrhythmias that may cause sudden deaths. Hence, many studies have been presented to developed computer-aided diagnosis (CAD) systems to automatically identify arrhythmias.Methods: This paper proposes a novel deep learning approach to identify arrhythmias in ECG signals. The signals are obtained from MIT-BIH arrhythmia database and are categorized according to five arrhythmia types. The proposed approach identifies arrhythmia classes by using Convolutional Neural Network (CNN) architecture trained by two-dimensional (2D) ECG beat images. CNN architecture is selected due to high image recognition performance. ECG signals are segmented into heartbeats, then each heartbeat is transformed into a 2D grayscale image. The heartbeat images are used as input for the CNN. Results: The proposed CNN model is compared to other common CNN architectures such as LeNet and ResNet-50 to evaluate the performance of our study. Overall, the proposed study achieved 99.7% test accuracy in the classification of five different ECG arrhythmias.Conclusions: Testing results demonstrate that CNN trained by ECG image representations provide outstanding classification performance of arrhythmic ECG signals and outperforms similar network architectures. Hence, the proposed approach provides a robust method for the classification of ECG arrhythmias.

show abstract

Patient-Specific ECG Classification Based on Recurrent Neural Networks and Clustering Technique

Cited by 68 publications

References 13 publications

Wavelets for Electrocardiogram: Overview and Taxonomy

Wavelets for Electrocardiogram: Overview and Taxonomy

An Advanced Two-Step DNN-Based Framework for Arrhythmia Detection

ECG Heartbeat Classification Based on Signal-to-Image Transformation Using Convolutional Neural Networks

Contact Info

Product

Resources

About