Semi-supervised learning for ECG classification without patient-specific labeled data

Zhai, Xiaolong; Zhou, Zhanhong; Tin, Chung

doi:10.1016/j.eswa.2020.113411

Cited by 55 publications

(26 citation statements)

References 33 publications

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“…[121] Designing a semi-supervised deep learning approach, high accuracy, acceptable runtime Insufficient experiments to evaluate the final model, not considering the effect of different parameters on the final learning model, not using different deep learning algorithms to evaluate the learning model [122] Evaluating the performance of the learning model based on different datasets, evaluating the performance of the learning model based on different conditions Not considering runtime, not using different deep learning algorithms to evaluate the learning model, not considering a suitable pre-processing scheme [123] High accuracy, designing a classifier with different classes, using a semi-supervised learning to update the predicted label Not considering runtime, not using different artificial neural networks to evaluate the learning model, not considering a suitable pre-processing scheme, insufficient experiments to evaluate the final model [124] Evaluating the learning model based on different datasets, considering various conditions to evaluate the learning model, high accuracy, accepted runtime Not using different deep learning algorithms to evaluate the learning model, not considering a suitable pre-processing scheme, needing high time for the training process [125] Evaluating the learning model based on different datasets, high accuracy Not using different basic learning algorithms to evaluate the learning model, not mentioning any reason to use SVM and KNN as basic classifiers, not designing a suitable pre-processing scheme, not describing the feature selection process…”

Section: Scheme Strengths Weaknessesmentioning

confidence: 99%

“…Zhai et al [123] suggested a semi-supervised learning system to classify electrocardiogram (ECG). The purpose of the classification is to detect arrhythmia.…”

Section: Ecg Classification System Based On Semi-supervised Learningmentioning

confidence: 99%

“…Therefore, the combination of both unlabeled and labeled data can improve the performance of an ECG classifier. As a result, the authors of [123] select semi-supervised learning for designing such a system.…”

Section: Ecg Classification System Based On Semi-supervised Learningmentioning

confidence: 99%

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Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Rahmani

Yousefpoor

et al. 2021

Mathematics

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Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person’s behavior in solving problems or his ability for learning. Furthermore, ML is a subset of artificial intelligence. It extracts patterns from raw data automatically. The purpose of this paper is to help researchers gain a proper understanding of machine learning and its applications in healthcare. In this paper, we first present a classification of machine learning-based schemes in healthcare. According to our proposed taxonomy, machine learning-based schemes in healthcare are categorized based on data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementation-based evaluation in real environment) and applications (diagnosis, treatment). According to our proposed classification, we review some studies presented in machine learning applications for healthcare. We believe that this review paper helps researchers to familiarize themselves with the newest research on ML applications in medicine, recognize their challenges and limitations in this area, and identify future research directions.

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Section: Scheme Strengths Weaknessesmentioning

confidence: 99%

“…Zhai et al [123] suggested a semi-supervised learning system to classify electrocardiogram (ECG). The purpose of the classification is to detect arrhythmia.…”

Section: Ecg Classification System Based On Semi-supervised Learningmentioning

confidence: 99%

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Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Rahmani

Yousefpoor

et al. 2021

Mathematics

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show abstract

“…However, some methods require intervention from an expert to label some heartbeats that making the entire approach is not practical and time-consuming. Recently, semi-supervised and unsupervised techniques [29], [30] have been adapted to reduce or fully eliminate the need for extra manual labeling of data. In this paper, we propose a deep learning patient-independent approach that follows the "subject-oriented" scheme.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification

Essa

Xie

2021

IEEE Access

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An automatic system for heart arrhythmia classification can perform a substantial role in managing and treating cardiovascular diseases. In this paper, a deep learning-based multi-model system is proposed for the classification of electrocardiogram (ECG) signals. Two different deep learning bagging models are introduced to classify heartbeats into different arrhythmias types. The first model (CNN-LSTM) is based on a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) network to capture local features and temporal dynamics in the ECG data. The second model (RRHOS-LSTM) integrates some classical features, i.e. RR intervals and higher-order statistics (HOS), with LSTM model to effectively highlight abnormality heartbeats classes. We create a bagging model from the CNN-LSTM and RRHOS-LSTM networks by training each model on a different sub-sampling dataset to handle the high imbalance distribution of arrhythmias classes in the ECG data. Each model is also trained using a weighted loss function to provide high weight for not sufficiently represented classes. These models are then combined using a meta-classifier to form a strong coherent model. The meta-classifier is a feedforward fully connected neural network that takes the different predictions of bagging models as an input and combines them into a final prediction. The result of the meta-classifier is then verified by another CNN-LSTM model to decrease the false positive of the overall system. The experimental results are acquired by evaluating the proposed method on ECG data from the MIT-BIH arrhythmia database. The proposed method achieves an overall accuracy of 95.81% in the "subject-oriented" patient independent evaluation scheme. The averages of F1 score and positive predictive value are higher than all other methods by more than 3% and 8% respectively. The experimental results show the superiority of the proposed method for ECG heartbeats classification compared to many state-of-the-art methods.INDEX TERMS Electrocardiogram (ECG), CNN, LSTM, bagging, Ensemble, deep learning.

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“…By examining the correlation in spectrograms, normal cycles were identified in an unsupervised manner. They were then used to train a semi-supervised learning model to distinguish disease-altered ECG cycles [ 12 ]. Cardona et al employed Gaussian mixtures for the modeling of ECG signals, and a K-nearest neighbors clustering approach was used to separate normal from left bundle branch block ECG samples [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Tensor-Based ECG Anomaly Detection toward Cardiac Monitoring in the Internet of Health Things

Zhou

Chen

2021

Sensors

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Advanced heart monitors, especially those enabled by the Internet of Health Things (IoHT), provide a great opportunity for continuous collection of the electrocardiogram (ECG), which contains rich information about underlying cardiac conditions. Realizing the full potential of IoHT-enabled cardiac monitoring hinges, to a great extent, on the detection of disease-induced anomalies from collected ECGs. However, challenges exist in the current literature for IoHT-based cardiac monitoring: (1) Most existing methods are based on supervised learning, which requires both normal and abnormal samples for training. This is impractical as it is generally unknown when and what kind of anomalies will occur during cardiac monitoring. (2) Furthermore, it is difficult to leverage advanced machine learning approaches for information processing of 1D ECG signals, as most of them are designed for 2D images and higher-dimensional data. To address these challenges, a new sensor-based unsupervised framework is developed for IoHT-based cardiac monitoring. First, a high-dimensional tensor is generated from the multi-channel ECG signals through the Gramian Angular Difference Field (GADF). Then, multi-linear principal component analysis (MPCA) is employed to unfold the ECG tensor and delineate the disease-altered patterns. Obtained principal components are used as features for anomaly detection using machine learning models (e.g., deep support vector data description (deep SVDD)) as well as statistical control charts (e.g., Hotelling T2 chart). The developed framework is evaluated and validated using real-world ECG datasets. Comparing to the state-of-the-art approaches, the developed framework with deep SVDD achieves superior performances in detecting abnormal ECG patterns induced by various types of cardiac disease, e.g., an F-score of 0.9771 is achieved for detecting atrial fibrillation, 0.9986 for detecting right bundle branch block, and 0.9550 for detecting ST-depression. Additionally, the developed framework with the T2 control chart facilitates personalized cycle-to-cycle monitoring with timely detected abnormal ECG patterns. The developed framework has a great potential to be implemented in IoHT-enabled cardiac monitoring and smart management of cardiac health.

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Semi-supervised learning for ECG classification without patient-specific labeled data

Cited by 55 publications

References 33 publications

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification

Tensor-Based ECG Anomaly Detection toward Cardiac Monitoring in the Internet of Health Things

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