Open Access Open Access  Restricted Access Subscription or Fee Access

Design of Power Reduced Architecture of ECG Based Processor for Predicting Ventricular Arrhythmia

C. Sathya, Dr. T. R. Ganesh Babu, S. Aruna Devi


This work is the prediction of ventricular arrhythmia using a unique set of ECG features. The design of a Radial Basis Function classifier (RBF) in fully integrated electrocardiogram (ECG) Signal Processor (ESP) is used to classify each heart beat as normal or abnormal. In this classifier containing an Artificial Neural Networks (ANN) are now being increasingly standard in the area of classification and prediction, where failure model and other related statistical techniques have usually been employed. The adaptive techniques for the detection and the delineation of the P-QRS-T waves were investigated to extract the fiducial points in real-time applications. Here the process of detecting the all intervals in the ECG signal and compare the stored record for Ventricular Arrhythmia with area architecture design. Classification of electrocardiogram (ECG) signals plays a vital role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the ventricular ectopic beat (V),normal beat (N), fusion beat (F), supraventricular ectopic beat (S), and unknown beat (Q) using a mixture of features. In this paper there are two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform function based features along with temporal features and (ii) mixture of WT and ST based features along with temporal features. The extracted feature set is independently classified by using an Artificial Neural Network (ANN). The performances are evaluated on several normal and abnormal ECG signals from recordings of the MIT-BIH arrhythmia database.


Artificial Neural Network, Electrocardiogram, Ventricular Arrhythmia, Ventricular Tachycardia, Ventricular Fibrillation.

Full Text:



J. W. Schleifer, 2013 ‘Ventricular arrhythmias: State of the art’,vol . 31, no. 4, pp. 595–605.

D. P. Zipes and H. J. J. Wellens, “Sudden cardiac death,” Circulation, vol. 98, no. 21, pp. 2334–2351, 1998.

C. J. Garratt, Mechanisms and Management of Cardiac Arrhythmias. London, U.K.: BMJ Books, 2001.

P. de Chazal, M. O’Dwyer, and R. B. Reilly, “Automatic classification of heartbeats using ECG morphology and heartbeat interval features,” IEEE Trans. Biomed. Eng., vol. 51, no. 7, pp. 1196–1206, Jul. 2004.

A. Amann, R. Tratnig, and K. Unterkofler, “Detecting ventricular fibrillation by time-delay methods,” IEEE Trans. Biomed. Eng., vol. 54, no. 1, pp. 174–177, Jan. 2007.

O. Sayadi, M. B. Shamsollahi, and G. D. Clifford, “Robust detection of premature ventricular contractions using a wave-based Bayesian framework,” IEEE Trans. Biomed. Eng., vol. 57, no. 2, pp. 353–362, Feb. 2010.

X.-S. Zhang, Y.-S. Zhu, N. V. Thakor, and Z.-Z. Wang, “Detecting ventricular tachycardia and fibrillation by complexity measure,” IEEE Trans. Biomed. Eng., vol. 46, no. 5, pp. 548–555, May 1999.

J. Pardey, “Detection of ventricular fibrillation by sequential hypoth- esis testing of binary sequences,” in Proc. IEEE Comput. Cardiol. Sep./Oct. 2007, pp. 573–576.

Q. Li, C. Rajagopalan, and G. D. Clifford, “Ventricular fibrillation and tachycardia classification using a machine learning approach,” vol. 61, no. 3, pp. 1607–1613, Jun. 2013.

B.-Y. Shiu, S.-W. Wang, Y.-S. Chu, and T.-H. Tsai, “Low-power low-noise ECG acquisition system with dsp for heart disease iden- tification,” in Proc. IEEE Biomed. Circuits Syst. Conf. (BioCAS), Oct./Nov. 2013, pp. 21–24.

S.-Y. Lee, J.-H. Hong, C.-H. Hsieh, M.-C. Liang, S.-Y. C. Chien, and K.-H. Lin, “Low-power wireless ECG acquisition and classification system for body sensor networks,” IEEE J. Biomed. Health Informat. vol. 19, no. 1, pp. 236–246, Jan. 2015.

J. Pan and W. J. Tompkins, “A real-time QRS detection algorithm,” IEEE Trans. Biomed. Eng., vol. BME-32, no. 3, pp. 230–236, Mar. 1985.

N. Bayasi, T. Tekeste, H. Saleh, A. Khandoker, B. Mohammad, and M. Ismail, “Adaptive technique for P and T wave delineation in electrocardiogram signals,” in Proc. IEEE 36th Annu. Int. Conf. Eng. Med. Biol. Soc., Aug. 2014, pp. 90–93.

P. Tadejko and W. Rakowski, “Mathematical morphology based ECG feature extraction for the purpose of heartbeat classification,” in Proc. IEEE 6th Int. Conf. Comput. Inf. Syst. Ind. Manage. Appl. (CISIM), Jun. 2007, pp. 322–327.

A. L. Goldberger et al., “Physiobank, physiotoolkit, and physionet: Com- ponents of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215–e220, Jun. 2000.

J. J. Nobel. [Online]. Available:


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.