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

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Abstract

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.

Abbreviations

AAFD:

Automatic atrial fibrillation detection

ACC:

Accelerometer

AF:

Atrial fibrillation

AFL:

Atrial flutter

AISQA:

Artificial intelligence-based signal quality assessment

AUC:

Area under the curve

AV:

Atrio ventricular

CE:

Conditional entropy

CNN:

Convolutional neural network

CODE:

Clinical outcomes in digital electrocardiology

CPSC:

China physiological signal challenge

CT:

Chriplet transform

CUSPH:

Chapman University & Shaoxing People’s Hospital

CWT:

Continuous wavelet transform

DL:

Deep learning

DTW:

Dynamic time warping

DWT:

Discrete wavelet transform

EBTC:

Ensemble boosted tree classifier

ECG:

Electrocardiogram

FN:

False negative

FP:

False positive

GCN:

Graph convolution network

HR:

Heart rate

IIR:

Infinite impulse response

KNN:

K-nearest neighbors

K–S:

Kolmogorov–Sinai

K–W:

Kruskal–Wallis

LMD:

Local mean decomposition

LSTM:

Long short-term memory

LSVM:

Lagrangian support vector machine

MAE:

Mean absolute error

MCC:

Matthews correlation coefficient

MIMIC:

Medical information mart for intensive care

ML:

Machine learning

MLSVD:

Multi linear singular value decomposition

MRMR:

Maximum relevance minimum redundancy

NN:

Neural network

PAC:

Premature atrial contractions

PAF:

Paroxysmal atrial fibrillation

PDB:

Proprietary database

PE:

Permutation entropy

PF:

Product functions

PPG:

Photoplethysmography

PVC:

Premature ventricular contractions

RMS:

Root mean square

RMSE:

Root mean square error

SA:

Sino atrial

SD:

Standard deviation

SVM:

Support vector machine

TN:

True negative

TP:

True positive

QNN:

Quadratic neural network

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Acknowledgements

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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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). https://doi.org/10.1007/s00034-024-02727-w

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