Combining Low-dimensional Wavelet Features and Support Vector Machine for Arrhythmia Beat Classification

Qin, Qin; Li, Jianqing; Zhang, Li; Yue, Yinggao; Liu, Chengyu

doi:10.1038/s41598-017-06596-z

Cited by 83 publications

(42 citation statements)

References 52 publications

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“…We have selected several ML methods to benchmark our model, and the result is compared in terms of accuracy, sensitivity, specificity, precision, and F1-score (see Tables 8 and 9) [34][35][36]. From Table 8, shallow architecture like SVM and DNNs produce a good performance, but from all results, a DNNs model with PCA and DWT produces high performance, such as accuracy of about 99.76%, precision of about 98.20%, sensitivity of about 91.80%, specificity of about 99.78% and an F1-measure of about 97.80%.…”

Section: Discussionmentioning

confidence: 99%

An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique

et al. 2019

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An automated classification system based on a Deep Learning (DL) technique for Cardiac Disease (CD) monitoring and detection is proposed in this paper. The proposed DL architecture is divided into Deep Auto-Encoders (DAEs) as an unsupervised form of feature learning and Deep Neural Networks (DNNs) as a classifier. The objective of this study is to improve on the previous machine learning technique that consists of several data processing steps such as feature extraction and feature selection or feature reduction. It is also noticed that the previously used machine learning technique required human interference and expertise in determining robust features, yet was time-consuming in the labeling and data processing steps. In contrast, DL enables an embedded feature extraction and feature selection in DAEs pre-training and DNNs fine-tuning process directly from raw data. Hence, DAEs is able to extract high-level of features not only from the training data but also from unseen data. The proposed model uses 10 classes of imbalanced data from ECG signals. Since it is related to the cardiac region, abnormality is usually considered for an early diagnosis of CD. In order to validate the result, the proposed model is compared with the shallow models and DL approaches. Results found that the proposed method achieved a promising performance with 99.73% accuracy, 91.20% sensitivity, 93.60% precision, 99.80% specificity, and a 91.80% F1-Score. Moreover, both the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR) curve from the confusion matrix showed that the developed model is a good classifier. The developed model based on unsupervised feature extraction and deep neural network is ready to be used on a large population before its installation for clinical usage.

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Section: Discussionmentioning

confidence: 99%

An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique

et al. 2019

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“…The weights of the classifier are extracted from the pre-trained DAEs-AEs model, and the fully connected layer is added as an output layer with Softmax as an activation function and categorical cross-entropy as a loss function. The classification performances were measured by using the following five metrics in the literature: accuracy, sensitivity, specificity, precision and F1-score [22,30]. While accuracy can be used for evaluating the overall performance as benchmarking, the other metrics can measure the performance of a specific class of validation model.…”

Section: Pre-training and Fine-tuningmentioning

confidence: 99%

“…The classification phase based on ECG signal processing studies can be divided into two types of learning: supervised and unsupervised [22][23][24][25][26][27]. Such two types of learning provide good performance in ECG beats [26][27][28], or rhythm classification [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification

et al. 2020

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The electrocardiogram (ECG) is a widely used, noninvasive test for analyzing arrhythmia. However, the ECG signal is prone to contamination by different kinds of noise. Such noise may cause deformation on the ECG heartbeat waveform, leading to cardiologists’ mislabeling or misinterpreting heartbeats due to varying types of artifacts and interference. To address this problem, some previous studies propose a computerized technique based on machine learning (ML) to distinguish between normal and abnormal heartbeats. Unfortunately, ML works on a handcrafted, feature-based approach and lacks feature representation. To overcome such drawbacks, deep learning (DL) is proposed in the pre-training and fine-tuning phases to produce an automated feature representation for multi-class classification of arrhythmia conditions. In the pre-training phase, stacked denoising autoencoders (DAEs) and autoencoders (AEs) are used for feature learning; in the fine-tuning phase, deep neural networks (DNNs) are implemented as a classifier. To the best of our knowledge, this research is the first to implement stacked autoencoders by using DAEs and AEs for feature learning in DL. Physionet’s well-known MIT-BIH Arrhythmia Database, as well as the MIT-BIH Noise Stress Test Database (NSTDB). Only four records are used from the NSTDB dataset: 118 24 dB, 118 −6 dB, 119 24 dB, and 119 −6 dB, with two levels of signal-to-noise ratio (SNRs) at 24 dB and −6 dB. In the validation process, six models are compared to select the best DL model. For all fine-tuned hyperparameters, the best model of ECG heartbeat classification achieves an accuracy, sensitivity, specificity, precision, and F1-score of 99.34%, 93.83%, 99.57%, 89.81%, and 91.44%, respectively. As the results demonstrate, the proposed DL model can extract high-level features not only from the training data but also from unseen data. Such a model has good application prospects in clinical practice.

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“…However, the higher the number of data, the more computational cost. Some of the features may be correlated, resulting in a large number of irrelevant variables, which will significantly affect computational efficiency due to the large redundant data [93]. Hence, it is vital to remove some correlated features while improving the accuracy and efficiency of classification.…”

Section: Dimensionality Reductionmentioning

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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Combining Low-dimensional Wavelet Features and Support Vector Machine for Arrhythmia Beat Classification

Cited by 83 publications

References 52 publications

An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique

An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique

Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification

Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

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