Detection of Arrhythmia using Neural Network

• Autorzy: Mohapatra S. K. , Palo H. K. , Mohanty M. N.

Identyfikatory

DOI

10.15439/2017KM42

• Konferencja: International Conference on Information Technology and Knowledge Management (1 ; 22-23.12.2017 ; New Delhi, India)
• Języki publikacji: EN

Abstrakty

EN

There is an increase in cardio logical patients all over the world due to change in modern life style. It forces the medical researchers to search for smart devices that can diagnosis and predict the onset of cardiac problem before it is too late. This motivates the authors to predict Arrhythmia that can help both the patients and the medical practitioners for better healthcare services. The proposed method uses the frequency domain information which can represent the ECG signals of Arrhythmia patients better. Features representing the MIT-BIH Arrhythmia are extracted using the efficient Short Time Fourier Transform and the Wavelet transform. A comparison of these features is made with that of normal human being using Neural Network based classifier. Wavelet based features has shown an improvement of Accuracy over that of STFT features in classifying Arrhythmia as our results reveal. A Mean Square Error (MSE) of with wavelet transform has validated our results.

Słowa kluczowe

EN

feature extraction; short-time Fourier transform; wavelet transform; classification; neural network

PL

wyodrębnianie funkcji; krótkoczasowa transformata Fouriera; transformacja falkowa; klasyfikacja; sieć neuronowa

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2018
• Tom: Vol. 14
• Strony: 97--100
• Opis fizyczny: Bibliogr. 13 poz., wz., tab., il.

Twórcy

autor

Mohapatra S. K.

• Electronics & Communication Engineering, ITER, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, India

autor

Palo H. K.

• Electronics & Communication Engineering, ITER, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, India

autor

Mohanty M. N.

• Electronics & Communication Engineering, ITER, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, India

Bibliografia

• 1. G C. S. Dangare, and S. S. Apte, "Improved study of heart disease prediction system using data mining classification techniques. International Journal of Computer Applications", Vol.47, No.10, pp.44-48, 2012.
• 2. M. Gandhi, and S. N. Singh, "Predictions in heart disease using techniques of data mining", In Futuristic Trends on Computational nalysis and Knowledge Management (ABLAZE), 2015 International Conference IEEE, pp.520-525, Feb. 2015.
• 3. Z. Wu,, X. Ding, G. Zhang, X. Xu, X. Wang, Y. Tao, and C. Ju, "A novel features learning method for ECG arrhythmias using deep belief networks". In Digital Home (ICDH), IEE2016 6th International Conference, pp.192-196, Dec. 2016.
• 4. P. Chazal, M. O'Dwyer, and R. B. Reilly, "Automatic classification of heartbeats using ECG morphology and heartbeat interval features", IEEE Transactions on Biomedical Engineering, Vol.51, No.7, pp.1196-1206, 2004.
• 5. O. T. Inan, L. Giovangrandi, and G. T. Kovacs, "Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features" IEEE Transactions on Biomedical Engineering, Vol.53, No.12, pp.2507-2515, 2006.
• 6. R. J. Martis, U. R. Acharya, and L. C. Min, "ECG beat classification using PCA, LDA, ICA and discrete wavelet transform", Biomedical Signal Processing and Control, Vol.8, No.5, pp.437-448, 2013.
• 7. A. Kampouraki, G. Manis, and C. Nikou, "Heartbeat time series classification with support vector machines", IEEE Transactions on Information Technology in Biomedicine, Vol.13, No.4, pp.512-518, 2009.
• 8. M. K. Gautam, and V. K. Giri, March. "A Neural Network approach and Wavelet analysis for ECG classification" In Engineering and Technology (ICETECH), IEEE International Conference on, pp. 1136-1141,2016,
• 9. L. T. M. Thuy, N. T. Nghia, D. V. Binh, N. T. Hai, and N. M. Hung, "Error-rate analysis for ECG classification in diversity scenario", In System Science and Engineering (ICSSE),International Conference on, IEEE, pp. 39-43, Jul. 2017.
• 10. H. K. Palo, M. N. Mohanty, "Wavelet based feature combination for recognition of emotions," Ain Shams Engineering Journal, Jan 2017.
• 11. H. K. Palo, M. Chandra, M. N. Mohanty, "Emotion recognition using MLP and GMM for Oriya language", International Journal of Computational Vision and Robotics. No.7,Vol.4, pp. 426-42, 2017.
• 12. R. D. Raut, and S. V. Dudul, "Arrhythmias classification with MLP neural network and statistical analysis," In Emerging Trends in Engineering and Technology, International Conference on , IEEE, pp. 553-558, 2008.
• 13. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender, "Learning to rank using gradient descent", In Proceedings of the 22nd international conference on Machine learning, ACM, pp. 89-96,2005.

Uwagi

1. Preface

2. Technical Session: First International Conference on Information Technology and Knowledge Management