A Novel Features Learning Method for ECG Arrhythmias Using Deep Belief Networks

Wu, Zhiyong; Ding, Xiaoqing; Zhang, Guangrui; Xu, Xiaowei; Wang, Xiaodong; Tao, Ye; Ju, Chuanxiang

doi:10.1109/icdh.2016.048

Cited by 16 publications

(7 citation statements)

References 21 publications

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“…In another study, Acharya et al (2017) designed a deep CNN model for ECG beats detection into five common classes recommended by AAMI. Wu et al (2016) used a deep belief networks to classify heartbeats into five classes. It should also be mentioned here that there is an interesting recent study by Mjahad et al (2017) which reported a non-featured ECG arrhythmia classification.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for real-time detection of atrial fibrillation

Andersen

Peimankar

Puthusserypady

2019

Expert Systems with Applications

308

169

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Section: Introductionmentioning

confidence: 99%

A deep learning approach for real-time detection of atrial fibrillation

Andersen

Peimankar

Puthusserypady

2019

Expert Systems with Applications

308

169

View full text Add to dashboard Cite

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“…Learning features based on deep learning algorithms and convolutional neural networks (CNNs) created an additional improvement for effective ECG signal classification. Feature extraction using CNN architectures, multi-scale deep feature learning (Zhou et al 2016) recurrent neural networks, stacked sparse auto-encoder (Zhang et al 2017), Boltzmann machine (Wu et al 2016) and deep belief networks (Yan et al 2015) have aimed to effectively analyze and classify the ECG signals. Classification of multiple arrhythmias utilizing ECG signals remains a challenge as the ECG signals related to many arrhythmias represent similar characteristics that need complicated analysis methods for the classification employing traditional signal processing approaches (Hasan and Bhattacharjee 2019).…”

Section: Motivationmentioning

confidence: 99%

A novel automated tower graph based ECG signal classification method with hexadecimal local adaptive binary pattern and deep learning

Subaşı

Doğan

Tuncer

2021

J Ambient Intell Human Comput

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Electrocardiography (ECG) signal recognition is one of the popular research topics for machine learning. In this paper, a novel transformation called tower graph transformation is proposed to classify ECG signals with high accuracy rates. It employs a tower graph, which uses minimum, maximum and average pooling methods altogether to generate novel signals for the feature extraction. In order to extract meaningful features, we presented a novel one-dimensional hexadecimal pattern. To select distinctive and informative features, an iterative ReliefF and Neighborhood Component Analysis (NCA) based feature selection is utilized. By using these methods, a novel ECG signal classification approach is presented. In the preprocessing phase, tower graph-based pooling transformation is applied to each signal. The proposed one-dimensional hexadecimal adaptive pattern extracts 1536 features from each node of the tower graph. The extracted features are fused and 15,360 features are obtained and the most discriminative 142 features are selected by the ReliefF and iterative NCA (RFINCA) feature selection approach. These selected features are used as an input to the artificial neural network and deep neural network and 95.70% and 97.10% classification accuracy was obtained respectively. These results demonstrated the success of the proposed tower graph-based method.

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“…A DL method called greedy deep dictionary learning [34] outperformed traditional and other DL methods. In [35], a new deep belief networks method that encompassed ECG signal pre-processing, segmentation and resampling, feature learning, and validation was able to learn the features of ECG arrhythmia and successfully classify them into five classes. By eliminating the need for manual feature extraction, the examples reviewed in this article underscore the generalizability and potential of DL models for detecting arrhythmia like AF on raw ECG signals [36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Review of Deep Learning-Based Atrial Fibrillation Detection Studies

Murat

Sadak

Yıldırım

et al. 2021

IJERPH

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Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming and prone to errors. To overcome these limitations, computer-aided diagnosis systems are developed using artificial intelligence techniques for automated detection of AF. Various machine learning and deep learning (DL) techniques have been developed for the automated detection of AF. In this review, we focused on the automated AF detection models developed using DL techniques. Twenty-four relevant articles published in international journals were reviewed. DL models based on deep neural network, convolutional neural network (CNN), recurrent neural network, long short-term memory, and hybrid structures were discussed. Our analysis showed that the majority of the studies used CNN models, which yielded the highest detection performance using ECG and heart rate variability signals. Details of the ECG databases used in the studies, performance metrics of the various models deployed, associated advantages and limitations, as well as proposed future work were summarized and discussed. This review paper serves as a useful resource for the researchers interested in developing innovative computer-assisted ECG-based DL approaches for AF detection.

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A Novel Features Learning Method for ECG Arrhythmias Using Deep Belief Networks

Cited by 16 publications

References 21 publications

A deep learning approach for real-time detection of atrial fibrillation

A deep learning approach for real-time detection of atrial fibrillation

A novel automated tower graph based ECG signal classification method with hexadecimal local adaptive binary pattern and deep learning

Review of Deep Learning-Based Atrial Fibrillation Detection Studies

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