Automated Arrhythmia Detection Based on RR Intervals

Faust, Oliver; Kareem, Murtadha; Ali, Ali; Ciaccio, Edward J.; Acharya, U. Rajendra

doi:10.3390/diagnostics11081446

Cited by 33 publications

(14 citation statements)

References 44 publications

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“…As a result of their studies, they obtained 98.96% accuracy in the teacher model and 98.13% accuracy in the student mode for seven rhythm classes. Faust et al [ 31 ] achieved 99.98% success in the SPNH dataset with the ResNet deep learning algorithm used by the authors in their study. Dhananjay et al [ 32 ] compared classical classification algorithms as well as their proposed model, the CatBoost model, in their study.…”

Section: Discussionmentioning

confidence: 99%

Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification

Ozpolat

Karabatak

2023

Diagnostics

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The electrocardiogram (ECG) is the most common technique used to diagnose heart diseases. The electrical signals produced by the heart are recorded by chest electrodes and by the extremity electrodes placed on the limbs. Many diseases, such as arrhythmia, cardiomyopathy, coronary heart disease, and heart failure, can be diagnosed by examining ECG signals. The interpretation of these signals by experts may take a long time, and there may be differences between expert interpretations. Since technological developments are intertwined with the medical sciences, computer-assisted diagnostic methods have recently come forward. In computer science, machine learning techniques are often preferred for automatic detection. Quantum-based structures have emerged to increase the machine learning algorithm’s speed and classification performance. In this study, a quantum-based machine learning algorithm is applied to classify heart rhythms. The ECG properties were converted to qubit structure using principal component analysis (PCA). The resulting qubits are classified using the quantum support vector machine (QSVM) algorithm. Quantum computer simulation over Qiskit was used for classification studies. Within the scope of experimental studies, comparisons between classical SVM and QSVM were made using different data amounts and qubit numbers. In the results of the analysis, classical SVM achieved 86.96% accuracy, and QSVM achieved 84.64% accuracy. Despite the fact that the entire dataset was not used due to various limitations, these successful performances were achieved. Classification of medical data such as that from ECG has shown that quantum-based machine learning frameworks perform well despite current resource constraints. In this respect, the study includes essential contributions to the use of quantum-based machine learning methods on signal data in medicine.

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Section: Discussionmentioning

confidence: 99%

Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification

Ozpolat

Karabatak

2023

Diagnostics

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“…It would be useful to structure a further study that used ECG signals obtained prior to identified AF episodes to provide insight into whether subtle differences can precede the onset of a PAF diagnosis. Since the SPAR attractor is independent of the heart rate, 21 it may also be helpful to incorporate complementary metrics that have been shown to be useful in the discrimination of AF episodes, such as R-R intervals 36 or features drawn from the Poincaré plots of heart rate differences. 37 Subtle changes in these metrics may also be distinguishable during normal sinus rhythm (distant from the actual AF episodes) and support the detection of the PAF diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Detecting paroxysmal atrial fibrillation from normal sinus rhythm in equine athletes using Symmetric Projection Attractor Reconstruction and machine learning

Huang

Lyle

Razak

et al. 2022

Cardiovascular Digital Health Journal

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“…The authors used the PhysioNet public dataset and achieved 92.8% accuracy, but the ECG signal in 1D is considered classification. In [ 11 ], the authors calculated RR intervals to detect arrhythmia. The approach uses conventional methods and achieves an accuracy of 99.98%.…”

Section: Related Workmentioning

confidence: 99%

A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia

Jamil

Rahman

2022

J. Imaging

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Cardiovascular diseases (CVDs) are the primary cause of death. Every year, many people die due to heart attacks. The electrocardiogram (ECG) signal plays a vital role in diagnosing CVDs. ECG signals provide us with information about the heartbeat. ECGs can detect cardiac arrhythmia. In this article, a novel deep-learning-based approach is proposed to classify ECG signals as normal and into sixteen arrhythmia classes. The ECG signal is preprocessed and converted into a 2D signal using continuous wavelet transform (CWT). The time–frequency domain representation of the CWT is given to the deep convolutional neural network (D-CNN) with an attention block to extract the spatial features vector (SFV). The attention block is proposed to capture global features. For dimensionality reduction in SFV, a novel clump of features (CoF) framework is proposed. The k-fold cross-validation is applied to obtain the reduced feature vector (RFV), and the RFV is given to the classifier to classify the arrhythmia class. The proposed framework achieves 99.84% accuracy with 100% sensitivity and 99.6% specificity. The proposed algorithm outperforms the state-of-the-art accuracy, F1-score, and sensitivity techniques.

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Automated Arrhythmia Detection Based on RR Intervals

Cited by 33 publications

References 44 publications

Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification

Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification

Detecting paroxysmal atrial fibrillation from normal sinus rhythm in equine athletes using Symmetric Projection Attractor Reconstruction and machine learning

A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia

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