Automated ECG Classification Using Dual Heartbeat Coupling Based on Convolutional Neural Network

Zhai, Xiaolong; Tin, Chung

doi:10.1109/access.2018.2833841

Cited by 228 publications

(130 citation statements)

References 24 publications

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“…In Experiment 3, as recommended by the Association for the Advancement of Medical Instrumentation (AAMI) [44], all beats were classified as beats originating in the sinus mode (N), supraventricular ectopic beats (S beats or SVEB), ventricular ectopic beats (V beats or VEB), fusion beats (F), or unclassifiable beats (Q), and four paced records (102, 104, 107, and 217) were excluded from the MITDB. Based on the characteristics and symptoms of the subjects, the 44 selected records were divided into two groups [5,18,35,36]. The first group, denoted by DS1, included 20 records with label numbers beginning with 1 as representative samples of a variety of waveforms and artifacts that may be encountered by an arrhythmia detector in routine clinical use.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Subsets are selected as they are easier to generalize, which will improve the accuracy of ECG heartbeat classification. However, manual selection may result in the loss of information [18,19]. Moreover, methods like the PCA and Fourier transform may increase the complexity and computational time required to identify a solution.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Such weak signals are susceptible to corruption by environmental noise and other factors; thus, recorded ECG signals often include noise and interference, such as myoelectric interference, baseline drift, and power frequency interference. In Experiment 1, which is described later, the baseline drift was removed using a bandpass filter (passband: 0.1-100 Hz) [18] and the high-frequency noise was partially removed using a three-scale discrete wavelet transform, which removes the coefficients below a certain threshold [38,39]. The Daubechies 5 (db5) wavelet basis function was adopted and the threshold was estimated via the Stein unbiased likelihood method.…”

Section: Classification Systemmentioning

confidence: 99%

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Deep Convolutional Neural Network Based ECG Classification System Using Information Fusion and One-Hot Encoding Techniques

et al. 2018

Mathematical Problems in Engineering

101

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Although convolutional neural networks (CNNs) can be used to classify electrocardiogram (ECG) beats in the diagnosis of cardiovascular disease, ECG signals are typically processed as one-dimensional signals while CNNs are better suited to multidimensional pattern or image recognition applications. In this study, the morphology and rhythm of heartbeats are fused into a two-dimensional information vector for subsequent processing by CNNs that include adaptive learning rate and biased dropout methods. The results demonstrate that the proposed CNN model is effective for detecting irregular heartbeats or arrhythmias via automatic feature extraction. When the proposed model was tested on the MIT-BIH arrhythmia database, the model achieved higher performance than other state-of-the-art methods for five and eight heartbeat categories (the average accuracy was 99.1% and 97%). In particular, the proposed system had better performance in terms of the sensitivity and positive predictive rate for V beats by more than 4.3% and 5.4%, respectively, and also for S beats by more than 22.6% and 25.9%, respectively, when compared to existing algorithms. It is anticipated that the proposed method will be suitable for implementation on portable devices for the e-home health monitoring of cardiovascular disease.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Section: Classification Systemmentioning

confidence: 99%

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Deep Convolutional Neural Network Based ECG Classification System Using Information Fusion and One-Hot Encoding Techniques

et al. 2018

Mathematical Problems in Engineering

101

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“…In this paper, we use two stages of ECG classification, namely beat segmentation and classification. For classification purposes, CNN could be used as stated in [27,29]. Although CNN hyperparameters such as number of filters, filter size, padding type, activation type, pooling, backpropagation, still have to be done intuitively or by trial and error, there are still some techniques that can be used to reduce the amount of trial and error attempts to achieve the best results.…”

Section: D Convolutional Neural Network and Its Enhancementmentioning

confidence: 99%

“…As described in [27,29], during the forward propagation, the input map of the next layer neuron will be obtained by the cumulation of the final output maps of the previous layer neurons convolved with their individual kernels as follows:…”

Section: -D Cnnsmentioning

confidence: 99%

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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Detecting Heart Arrhythmias Using Deep Learning Algorithms

Choubey

Jha

Kumari

et al. 2023

Convergence of Cloud With AI for Big Data Analytics

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Automated ECG Classification Using Dual Heartbeat Coupling Based on Convolutional Neural Network

Cited by 228 publications

References 24 publications

Deep Convolutional Neural Network Based ECG Classification System Using Information Fusion and One-Hot Encoding Techniques

Deep Convolutional Neural Network Based ECG Classification System Using Information Fusion and One-Hot Encoding Techniques

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

Detecting Heart Arrhythmias Using Deep Learning Algorithms

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