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Detection of Atrial Fibrillation from ECG Signal Using Efficient Feature Selection and Classification

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Atrial fibrillation (AF) is a life-threatening cardiac condition caused by inadequate blood flow, resulting in abnormal ECG records, blood clotting, and cardioembolic strokes. In recent years, physicians have been particularly concerned with early detection and diagnosis to overcome cardiogenic stroke. AF can be easily identified at the initial stages due to the development in computer-aided diagnosis. The performance of this method is affected by noise and the variations in pattern of the ECG, which leads to false diagnosis. Current signal processing and shallow machine learning (ML) approaches are severely limited in their ability to detect this condition accurately. Deep neural networks have been shown to be extremely effective at learning nonlinear patterns in a wide variety of problems, which include computer vision tasks. Deep learning models are computationally costly, non-explainable, and require a large quantity of data to discover characteristics. In contrast, ML approaches are explainable and require good feature extraction. In this manuscript, ML based supervised classification method is developed based on feature ensembling. ECG signals are preprocessed (mean subtraction followed by Butterworth filtering and computation of RR intervals) and subjected to feature extraction (by entropy-, wavelets-, & statistical-features). The variations due to AF are effectively captured and selective features are ensembled to perform classification by SVM and KNN. This method is experimented on five different databases (such as: PAF prediction Challenge, Long-Term AF, Intracardiac, AF termination Challenge, and MIT-BIH atrial fibrillation) and the classification performance is found to be the highest compared to the state of art. To evaluate the effectiveness of the proposed technique, AF-specific characteristics are retrieved from the ECG signal in the presence of artificially added noise and the features are fed to classifiers for classification. Performance of the proposed method is compared with the deep learning based approaches. The graphical abstract of the proposed atrial fibrillation detection method is presented. The overall accuracy of the proposed method was found to be 91.88\(\%\) and 91.99\(\%\) for wavelets-SVM and ensemble wavelet-SVM, respectively. This model attained 100\(\%\) accuracy for entropy and statistical features with SVM and KNN, respectively.

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Data Availibility Statement

The authors confirm that the data supporting the findings of this study are available from the corresponding author on request. The programs and the supporting files will be provided on request. The data that support the findings of this study are freely available in PhysioBank ATM.



Automatic atrial fibrillation detection




Atrial fibrillation


Atrial flutter


Artificial intelligence-based signal quality assessment


Area under the curve


Atrio ventricular


Conditional entropy


Convolutional neural network


Clinical outcomes in digital electrocardiology


China physiological signal challenge


Chriplet transform


Chapman University & Shaoxing People’s Hospital


Continuous wavelet transform


Deep learning


Dynamic time warping


Discrete wavelet transform


Ensemble boosted tree classifier




False negative


False positive


Graph convolution network


Heart rate


Infinite impulse response


K-nearest neighbors






Local mean decomposition


Long short-term memory


Lagrangian support vector machine


Mean absolute error


Matthews correlation coefficient


Medical information mart for intensive care


Machine learning


Multi linear singular value decomposition


Maximum relevance minimum redundancy


Neural network


Premature atrial contractions


Paroxysmal atrial fibrillation


Proprietary database


Permutation entropy


Product functions




Premature ventricular contractions


Root mean square


Root mean square error


Sino atrial


Standard deviation


Support vector machine


True negative


True positive


Quadratic neural network


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This work is carried out in the Department of ECE, National Institute of Technology Puducherry, Karaikal, India. The results obtained by the proposed technique are validated by Dr. Archana Anbalagan, MS (OG), Sri Venkateshwaraa Medical College Hospital and Research Centre, Puducherry, India.

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Correspondence to Thivya Anbalagan, Malaya Kumar Nath or Archana Anbalagan.

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Anbalagan, T., Nath, M.K. & Anbalagan, A. Detection of Atrial Fibrillation from ECG Signal Using Efficient Feature Selection and Classification. Circuits Syst Signal Process (2024).

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