Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier

Rajesh, Kandala N. V. P. S.; Dhuli, Ravindra

doi:10.1016/j.bspc.2017.12.004

Cited by 135 publications

(52 citation statements)

References 53 publications

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“…The huge imbalance of dataset distribution is another problem for healthcare analysis and particularly for heart disease classification. In order to increase the adaptability and the precision of the machine learning solutions, ensemble-learning models have been proposed to contribute better diagnosis results, for example using the AdaBoost ensemble classifier for heartbeat classification [30], and cardiovascular disease detection using hybrid models [31,32]. Ensemble of neural networks has been also suggested for phonocardiogram recordings detection using a feed-forward neural network without segmentation [33].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.

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Section: Introductionmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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“…Sanabila et al [24] used the generated oversampling method (GenOMe) to solve the problem of imbalanced arrhythmias, which generated new data points with specific distributions (beta, gamma, and Gaussian) as constraints. Rajesh and Dhuli [25] employed three data-level preprocessing techniques on an extracted feature set to balance the distribution of ECG heartbeats. These were random oversampling and undersampling (ROU), synthetic minority oversampling technique with random undersampling (SMOTE + RU), and distribution-based balancing (DBB).…”

Section: Introductionmentioning

confidence: 99%

An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset

Gao

Zhang

et al. 2019

Journal of Healthcare Engineering

122

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To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems. However, the complex variations and imbalance of ECG beats make this a challenging issue. Since ECG beat data exist in heavily imbalanced category, an effective long short-term memory (LSTM) recurrence network model with focal loss (FL) is proposed. For this purpose, the LSTM network can disentangle the timing features in complex ECG signals, while the FL is used to resolve the category imbalance by downweighting easily identified normal ECG examples. The advantages of the proposed network have been verified in the MIT-BIH arrhythmia database. Experimental results show that the LSTM network with FL achieved a reliable solution to the problem of imbalanced datasets in ECG beat classification and was not sensitive to quality of ECG signals. The proposed method can be deployed in telemedicine scenarios to assist cardiologists into more accurately and objectively diagnosing ECG signals.

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“…To address these two issues, an improved CEEMDAN (ICEEMDAN) was further proposed and modes with less noise as well as more physical meaning can be obtained [13]. Although ICEEMDAN was originally proposed for biomedical signal processing [13,14], very recently few work [15] has researched its potential for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM

Ran

Zhang

et al. 2020

Applied Sciences

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Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.

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Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier

Cited by 135 publications

References 53 publications

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset

Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM

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