Short-time fourier transform based ensemble classifiers for detection of atrial fibrillation from ECG datasets

Gowri Shankar Manivannan, Kalaiyarasi M, Harikumar Rajaguru


Atrial Fibrillation (Afib) is a common cardiac arrhythmia characterized by irregular and often rapid heart rate, leading to inefficient blood pumping from the atria. It increases the risk of stroke, heart failure, and other heart-related complications. Afib is often associated with symptoms like palpitations, shortness of breath, and fatigue. Diagnosis typically involves electrocardiography (ECG) to detect irregular electrical activity in the heart. Treatment options range from medication to procedures like catheter ablation, aimed at restoring normal heart rhythm and reducing associated risks. Soft computing methods can aid in automating the classification of cardiovascular diseases, assisting clinicians in diagnosing arrhythmias. In this research paper, ensemble classifiers are employed for the classification of Atrial Fibrillation based on ECG datasets. When utilizing the Catboost Classifier in conjunction with the STFT-based GEO implementation, the results indicate an average perfect classification rate of approximately 99%, an error rate of 1%, and a kappa coefficient of 0.9689% for detection of Afib.


Adaboost, Cardiovascular Disease, Catboost, ECG, XGboost

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S. Nattel, “New ideas about atrial fibrillation 50 years on”, Nature, Vol. 415, No. 6868, pp. 219-226, 2002.

Z.I. Attia, P.A. Noseworthy, F. Lopez-Jimenez, S.J. Asirvatham, A.J. Deshmukh, B.J. Gersh and P.A. Friedman, “An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction”, The Lancet, Vol. 394, No. 10201, pp. 861-867, 2019.

B.E. Moody, L.W. Lehman, I. Silva, A. Johnson and R.G. Mark, “AF classification from a short single lead ECG recording: the PhysioNet”, Computing in Cardiology Challenge 2017.

C.L. Bumgardner, W.J. Tompkins and L. Zhang, “Atrial Fibrillation Detection via Consumer Wearable Devices and Machine Learning”, IEEE Access, Vol. 7, pp. 36582-36593, 2019.

A.Y. Hannun, P. Rajpurkar, M. Haghpanahi, G.H. Tison, C. Bourn, M.P. Turakhia and A.Y. Ng, “Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network”, Nature Medicine, Vol. 25, No. (1), pp. 65-69, 2019.

S. Kiranyaz, T. Ince and M. Gabbouj, “Real-time patient-specific ECG classification by 1-D convolutional neural networks”, IEEE Transactions on Biomedical Engineering, Vol. 63, No. (3), pp. 664-675, 2016.

J. Smith, “A Novel Approach for Atrial Fibrillation Detection Using Deep Learning”, Journal of Biomedical Informatics, Vol. 94, pp. 103188, 2019.

L. Chen, “Atrial Fibrillation Detection from Short Single-Lead ECG Records Using a Deep Learning Model”, Nature Communications, Vol. 11, No. 1, pp. 1-10, 2020.

Y. Kim, “A Machine Learning Approach for Atrial Fibrillation Detection in Wearable ECG Devices”, IEEE Transactions on Biomedical Engineering, Vol. 68, No. 4, pp. 1014-1023, 2021.

X. Li, X, “Atrial Fibrillation Detection Using Random Forest and Convolutional Neural Networks”, Computing in Cardiology, Vol. 45, pp. 1-4, 2018.

H. Wang, “A Hybrid Model for Atrial Fibrillation Detection Based on Recurrent Neural Networks and Support Vector Machines”, Computing in Cardiology, Vol. 46, pp. 1-4, 2019.

P. Rajpurkar, A.Y. Hannun, M. Haghpanahi, C. Bourn and A.Y. Ng, “Cardiologist-level arrhythmia detection with convolutional neural networks”, Computing in Cardiology (CinC), Vol. 44, 2017.

S.P. Shashikumar, A.J. Shah, Q. Li, G.D. Clifford, and S. Nemati, “Atrial Fibrillation Detection from Short Single-Lead ECG Recordings Using Deep Learning”, IEEE Transactions on Biomedical Engineering, Vol. 67, No. 7, pp. 1961-1970, 2020.

S. Asgari, A. Mehrnia, and Z.A. Sani, “Atrial Fibrillation Detection Using Wearable Smartwatch Data”, In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4095-4098, 2019.

K.Y. Li, Y. Wang, C. Shi and Y. Li, “Automatic Detection of Atrial Fibrillation in ECGs Based on Deep Learning”, IEEE Access, Vol. 8, pp. 195531-195540, 2020.

G. Liu, Y. Li and X. Zhang, “Deep Learning for Atrial Fibrillation Detection in Mobile Health Monitoring”, Sensors, Vol. 18, No. 7, pp. 2175, 2018.

Z. Xiong, M.P. Nash, E. Cheng, and V.V. Fedorov, “Atrial Fibrillation Detection from ECG Signals Using Convolutional Neural Networks”, Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3883-3886, 2018.

J. Lee, B.A. Reyes, D.D. McManus and M. Maitin-Shepard, “Atrial Fibrillation Detection from Photoplethysmography Signals Using Machine Learning”, Computing in Cardiology (CinC), pp. 1-4, 2019.

Z. Ma, X. Cheng, J. Li and J. Liu, “ECG-Based Atrial Fibrillation Detection Using a Novel Algorithm with LSTM Recurrent Neural Networks”, Frontiers in Physiology, Vol. 10, pp. 1558, 2019.

F. Maturo and R. Verde, “Pooling random forest and functional data analysis for biomedical signals supervised classification: Theory and application to electrocardiogram data”, Statistics in Medicine, Vol. 41, No. 12, pp. 2247-2275, 2022.

F. Maturo, F and R. Verde, “Combining unsupervised and supervised learning techniques for enhancing the performance of functional data classifiers”, Computational Statistics, pp. 1-32, 2022.

F. Maturo and R. Verde, “Supervised classification of curves via a combined use of functional data analysis and tree-based methods”, Computational Statistics, Vol. 38, No. 1, pp. 419-459, 2023.

Kaggle Dataset: (, 2020.

D. Griffin and J. Lim, “Signal estimation from modified short-time Fourier transform”, IEEE Transactions on acoustics, speech, and signal processing, Vol. 32, No. 2, pp. 236-243, 1984.

L. Durak and O. Arikan, “Short-time Fourier transform: two fundamental properties and an optimal implementation”, IEEE Transactions on Signal Processing, Vol. 51, No. 5, pp. 1231-1242, 2003.

H. Sharma, G. Hazrati and J.C. Bansal, “Spider monkey optimization algorithm”, Evolutionary and swarm intelligence algorithms, pp. 43-59, 2019.

A. Mohammadi-Balani, M.D. Nayeri, A. Azar, M. Taghizadeh-Yazdi, “Golden eagle optimizer: A nature-inspired metaheuristic algorithm”, Computers & Industrial Engineering, Vol. 152, pp. 107050, 2021.

B. Dhananjay and J. Sivaraman, “Analysis and classification of heart rate using CatBoost feature ranking model”, Biomedical Signal Processing and Control, Vol. 68, pp. 102610, 2021.

A.A. Rawi, M.K. Elbashir and A.M. Ahmed, “ECG heartbeat classification using CONVXGB model”, Electronics, Vol. 11, No. 15, pp. 2280, 2022.

M. Barstuğan and R. Ceylan, “The effect of dictionary learning on weight update of AdaBoost and ECG classification”, Journal of King Saud University-Computer and Information Sciences, Vol. 32, No. 10, pp. 1149-1157, 2020.

M.G. Shankar, C.G. Babu, H. Rajaguru, “Classification of cardiac diseases from ECG signals through bio inspired classifiers with Adam and R-Adam approaches for hyperparameters updation”, Measurement, Vol. 194, pp. 111048, 2022



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