A robust deep convolutional neural network with batch-weighted loss for heartbeat classification

Sellami, Anis; Hwang, Heasoo

doi:10.1016/j.eswa.2018.12.037

Cited by 181 publications

(102 citation statements)

References 14 publications

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“…The algorithms provide a very in-depth analysis for an artificial real-time cardiac imaging with better spatial and temporal resolution. It potentially improves the quality of health caring and reducing costs [15][16][17][18][19]. Such algorithms can be trained using an unsupervised learning approach with unlimited memory [9,20,21] and, it is also suitable for noisy data [5,15,16].…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Third, we use the original input ECG data with an imbalance between normal and disease categories. To overcome the problem of category imbalance, on the basis of studies [ 39 , 40 ], we used a hybrid method to increase the training set and changed the sample batch weights to optimize our model. We adopted the Borderline-SMOTE algorithm to add minority samples to the training set; additionally, the FL function was employed to solve the category imbalance problem by reducing the internal weights of simple samples.…”

Section: Discussionmentioning

confidence: 99%

A Robust Multilevel DWT Densely Network for Cardiovascular Disease Classification

Zhang

Yang

et al. 2020

Sensors

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Cardiovascular disease is the leading cause of death worldwide. Immediate and accurate diagnoses of cardiovascular disease are essential for saving lives. Although most of the previously reported works have tried to classify heartbeats accurately based on the intra-patient paradigm, they suffer from category imbalance issues since abnormal heartbeats appear much less regularly than normal heartbeats. Furthermore, most existing methods rely on data preprocessing steps, such as noise removal and R-peak location. In this study, we present a robust classification system using a multilevel discrete wavelet transform densely network (MDD-Net) for the accurate detection of normal, coronary artery disease (CAD), myocardial infarction (MI) and congestive heart failure (CHF). First, the raw ECG signals from different databases are divided into same-size segments using an original adaptive sample frequency segmentation algorithm (ASFS). Then, the fusion features are extracted from the MDD-Net to achieve great classification performance. We evaluated the proposed method considering the intra-patient and inter-patient paradigms. The average accuracy, positive predictive value, sensitivity and specificity were 99.74%, 99.09%, 98.67% and 99.83%, respectively, under the intra-patient paradigm, and 96.92%, 92.17%, 89.18% and 97.77%, respectively, under the inter-patient paradigm. Moreover, the experimental results demonstrate that our model is robust to noise and class imbalance issues.

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“…It has a wide variety of applications such as biometrics authentication, object detection, classification, compression, image classification, and other computer vision related technology fields. Deep learning has great potential of applications in cardiology such as ECG arrhythmia detection with Deep-CNN [71], [72], [74], [76], [77], [79], [80], Robust Deep Dictionary Language (RDDL) [73], Deep Brief Network with Restricted Boltzmann Machine (DBN+RBM) [75] and Deep Neural Network (DNN) [78]. MI detection is performed with Deep-CNN [81] and Deep Neural Network (DNN) [82] while detecting heartbeats is performed by DNN in [83].…”

Section: ) Traditional Ecg Classification Approachesmentioning

confidence: 99%

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

et al. 2020

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Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this paper. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined. INDEX TERMS ECG analysis, cardiac arrhythmias, QRS and ST detection, ECG classification, deep learning.

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A robust deep convolutional neural network with batch-weighted loss for heartbeat classification

Cited by 181 publications

References 14 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

A Robust Multilevel DWT Densely Network for Cardiovascular Disease Classification

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

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