Morphological Arrhythmia Automated Diagnosis Method Using Gray-Level Co-Occurrence Matrix Enhanced Convolutional Neural Network

Sun, Weifang; Zeng, Nianyin; He, Yong

doi:10.1109/access.2019.2918361

Cited by 50 publications

(26 citation statements)

References 37 publications

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“…In this paper, after R-peak detection, beat segmentation starts from 1 4 L i to 3 4 R i , in which L i is the RR-interval right before the detected R-peak, while R i is the RR-interval right after the detected R-peak. This automatic segmentation window is necessary to ensure that there is only a single beat or R-peak in each segment.…”

Section: Beat Detection and Segmentationmentioning

confidence: 99%

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An Efficient Algorithm for Cardiac Arrhythmia Classification Using Ensemble of Depthwise Separable Convolutional Neural Networks

et al. 2020

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Many algorithms have been developed for automated electrocardiogram (ECG) classification. Due to the non-stationary nature of the ECG signal, it is rather challenging to use traditional handcraft methods, such as time-based analysis of feature extraction and classification, to pave the way for machine learning implementation. This paper proposed a novel method, i.e., the ensemble of depthwise separable convolutional (DSC) neural networks for the classification of cardiac arrhythmia ECG beats. Using our proposed method, the four stages of ECG classification, i.e., QRS detection, preprocessing, feature extraction, and classification, were reduced to two steps only, i.e., QRS detection and classification. No preprocessing method was required while feature extraction was combined with classification. Moreover, to reduce the computational cost while maintaining its accuracy, several techniques were implemented, including All Convolutional Network (ACN), Batch Normalization (BN), and ensemble convolutional neural networks. The performance of the proposed ensemble CNNs were evaluated using the MIT-BIH arrythmia database. In the training phase, around 22% of the 110,057 beats data extracted from 48 records were utilized. Using only these 22% labeled training data, our proposed algorithm was able to classify the remaining 78% of the database into 16 classes. Furthermore, the sensitivity ( S n ), specificity ( S p ), and positive predictivity ( P p ), and accuracy ( A c c ) are 99.03%, 99.94%, 99.03%, and 99.88%, respectively. The proposed algorithm required around 180 μs, which is suitable for real time application. These results showed that our proposed method outperformed other state of the art methods.

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Section: Beat Detection and Segmentationmentioning

confidence: 99%

“…ECG signals can be easily acquired by putting one's finger on the sensor for about 30 s [1]. There are at least two types of important information contained in the ECG signal, including those related to health or biomedical [2][3][4] and those related to the person identification or biometrics [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Algorithm for Cardiac Arrhythmia Classification Using Ensemble of Depthwise Separable Convolutional Neural Networks

et al. 2020

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“…Directly reading the multimodal data, similar to what would be found in hospital readings, mitigates the need for intermediate estimating methods like signal differencing, filterbanks, wavelet transform, and Hilbert transform [43]- [46]. Examples of other solutions include the use of co-occurrence matrices [47] and 3-dimensional data structures [48].…”

Section: B Deep Learning For Heartbeat Detectionmentioning

confidence: 99%

Robust Multimodal Heartbeat Detection Using Hybrid Neural Networks

et al. 2020

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Many arrhythmia datasets are multimodal due to the simultaneous collection of physiological signals of a subject. These datasets frequently have missing modalities or missing block-wise data, a characteristic that various recent applications of neural networks fail to consider. Most arrhythmic detection models only use electrocardiogram and blood pressure recordings. Unconsidered physiological signals may be strongly correlated with other modalities despite having missing data. To improve robustness and accuracy of heartbeat detection, all available modalities should be considered in multimodal arrhythmia datasets. Several hybrid neural networks are proposed to robustly analyze heartbeats by considering every available physiological signal. These networks combine elements from convolutional neural networks, recurrent neural networks, and a deep learning architecture. This enables researchers to analyze every signal of subjects while the set of signals collected among subjects may differ. The proposed hybrid neural networks provide more robust results in heartbeat detection when utilizing missing data modalities. INDEX TERMS Multimodal, heartbeat detection, deep learning, neural networks.

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“…Although the electricity amount is, in fact, very small, it can be picked up reliably with ECG electrodes attached to the skin (in microvolts, or uV) [ 2 ]. ECG signals contain no less than two critical pieces of statistics, including correlated to biomedicine’s healthiness [ 3 , 4 , 5 ] and associated with personal credentials or biometrics [ 6 , 7 , 8 ]. As a result of its easiness, several ECG categorizations processes have been established, counting manuals methods [ 9 , 10 ] and machine learning approaches [ 11 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal

Ullah

Rehman

et al. 2021

Sensors

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Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models’ effectiveness.

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Morphological Arrhythmia Automated Diagnosis Method Using Gray-Level Co-Occurrence Matrix Enhanced Convolutional Neural Network

Cited by 50 publications

References 37 publications

An Efficient Algorithm for Cardiac Arrhythmia Classification Using Ensemble of Depthwise Separable Convolutional Neural Networks

An Efficient Algorithm for Cardiac Arrhythmia Classification Using Ensemble of Depthwise Separable Convolutional Neural Networks

Robust Multimodal Heartbeat Detection Using Hybrid Neural Networks

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal

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