Key result
A hybrid model combining Convolution Neural Network (CNN) for feature extraction and XGBoost for classification achieved an overall accuracy of 99.84% and sensitivity of 92.61% for detecting 11 types of cardiac arrhythmia.
Why the study?
Advances in machine learning allow the development of computer-aided diagnostic models to identify heart disease type from ECG signals.
Does a hybrid CNN and XGBoost model accurately detect and classify cardiac arrhythmias from ECG signals?
Does a hybrid CNN and XGBoost model accurately detect and classify cardiac arrhythmias from ECG signals?
A hybrid model combining CNN for feature extraction and XGBoost for classification demonstrates high accuracy in detecting and classifying cardiac arrhythmias from ECG signals.
May support automated ECG arrhythmia classification in research; leaves open prospective clinical validation before practice adoption.
Abnormal behaviour of the heart called arrhythmia can be recorded as an electrocardiogram (ECG) signal. ECG plays a vital role in the diagnosis of heart disease. Advances in machine learning allow the development of computer-aided diagnostic models to identify heart disease type. We have proposed a hybrid model with Convolution Neural Network (CNN) to auto extract features from ECG and use XGBoost to assess the type of arrhythmia. We tested our model to diagnosis eleven kinds of arrhythmia beats from the MIT-BIH arrhythmia database and obtained overall sensitivity of 92.61%, specificity of 99.85%, positive predictive value of 95.99% and accuracy of 99.84%. The robustness of the proposed model is further confirmed by classifying the arrhythmia beats into 5 classes according to the AAMI standard and comparing the results with the state-of-the-art methods. Attained overall Sensitivity of 94.36%, specificity of 99.44%, positive predicative value of 96.40% and Accuracy of 99.69% for 5 AAMI Classes. The results demonstrated that the proposed model could be used in the diagnosis of arrhythmia.
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A 2022 study studied Cardiac Arrhythmia. CNN and XGBoost hybrid model vs. State-of-the-art machine learning methods was evaluated on Overall accuracy for 11-class arrhythmia detection. A hybrid model combining Convolution Neural Network (CNN) for feature extraction and XGBoost for classification achieved an overall accuracy of 99.84% and sensitivity of 92.61% for detecting 11 types of cardiac arrhythmia.
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