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System Design Approach for Heartbeat Detection and Classification of Individuals Irrespective of their Physical Condition


Affiliations
1 Department of Electronics and Telecommunication, Shri Shankaracharaya College of Engineering and Technology, Chhattisgarh Swami Vivekananda Technical University, Bhilai 490 020, India
 

In an electrocardiogram (ECG), the heartbeat feature QRS is an important parameter for analysis in any heartbeat classification automated diagnosis system. In this communication the method which we have proposed is by using the counter which is used in parallel. The first level is detection of heartbeats, which uses hashing of ECG features. In the second level, the profiler profiles a person's regular and irregular ECG characteristic behaviour. The proposed method works on data related with ECG, instead of particular features of ECG. Because of parallel processing, data storage unit requirements and the processing time are less. The dependent values in the proposed method vary according to the changes in the ECG waveform. Such type of analysis is suitable for detection of heart disease. The most significant application of such characteristic plotting is to generate an alert signal for irregular ECG behaviour in a person. Such automated system will be useful in remote areas where a cardiologist may not be easily available.

Keywords

Data Storage Units, Electrocardiogram Signal, Parallel Processing, QRS Detection.
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  • Lilly, L. S., Pathophysiology of Heart Disease, Lippincott Williams & Wilkins, Philadelphia, USA, 2003, 3rd edn, pp. 57–90.

  • SNORT Network Intrusion Detection System; www.snort.org

  • MIT-BIH; http://www.physionet.org

  • Senhadji, L. et al., Comparing wavelet transforms for recognizing cardiac patterns. IEEE Eng. Med. Biol., 1995, 14(2), 167–173.

  • Sahambi, J. S., Tandon, S. M. and Bhatt, R. K. P., Using wavelet transforms for ECG characterization: an on-line digital signal processing system. IEEE Eng. Med. Biol., 1997, 16(1), 77–83.

  • Hamilton, P. S. and Tompkins, W. J., Quantitative investigation of QRS detection rules using the MIT–BIH arrhythmia database. IEEE Trans. Biomed. Eng., 1986, 33(12), 1157–1165.

  • Thakor, N. V., Webster, J. G. and Tompkins, W. J., Estimation of QRS complex power spectra for design of a QRS filter. IEEE Trans. Biomed. Eng., 1986, 31(11), 702–706.

  • Li, C., Zheng, C. and Tai, C., Detection of ECG characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng., 1995, 42(1), 21–28.

  • Castro, K. D. and Geva, A. B., ECG feature extraction using optimal mother wavelet. In IEEE EMBE International Conference, 2000, pp. 346–350.

  • Romero, I., Serrano, L. and Ayesta, ECG frequency domain features extraction: a new characteristic for arrhythmias classification. Conf. IEEE Eng. Med. Boil. Soc., 2001, p. 2.

  • Kadambe, S., Murray, R. and Boudreaux-Bartels, G. F., Wavelet transform based QRS complex detector. IEEE Trans. Biomed. Eng., 1999, 46(7), 838–848.

  • Romero, L., Addison, P. S. and Grubb, N., R-wave detection using continuous wavelet modulus maxima. IEEE Proc. Comp. Cardiol., 2003, 30, 565–568.

  • Tkacz, E. J. and Komorowski, D., An improved statistical approach to the QRS detection problem using matched filter facilities. Biomed. Technik/Biomed. Eng., 1992, 37(1), 99–109.

  • Pan, J. and Tompkins, W. J., A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng., 1885, 32(3), 230–236.

  • Dotsinsky, I. A. and Stoyanov, T. V., Ventricular beat detection in single channel electrocardiograms. BioMed. Eng. Online, 2004, 3, 3.

  • Zhang, F., Tan, J. and Lian, Y., An effective QRS detection algorithm for wearable ECG in body area network. In Proceedings of IEEE Biomedical Circuits System Conference, 2007, pp. 195– 199.

  • Iliev, I., Krasteva, V. and Tabakov, S., Real-time detection of pathological cardiac events in the electrocardiogram. Physiol. Meas., 2007, 28(3), 259–276.

  • Zhou, H.-Y. and Hou, K.-M., Embedded real-time QRS detection algorithm for pervasive cardiac care system. In Proceedings of 9th IEEE International Conference on Signal Process, 2008, pp. 2150–2153.

  • Cvikl, M. and Zemva, A., FPGA-oriented HW/SW implementation of ECG beat detection and classification algorithm. Digital Signal Process, 2010, 20(1), 238–248.

  • Ning, X. and Selesnick, I. W., ECG enhancement and QRS detection based on sparse derivatives. Biomed. Signal Process. Control., 2013, 8, 713–723.

  • Jain, S., Kumar, P. and Subashini, M. M., LABVIEW based expert system for detection of heart abnormalities. In International Conference on Advances in Electrical Engineering, 2014, pp. 1–5.

  • Yazdani, S. and Vesin, J.-M., Adaptive mathematical morphology for QRS fiducial points detection in the ECG. Comput. Cardiol., 2014, 38, 725–728.

  • Nallathambi, G. and Principe, J. C., Integrate and fire pulse train automation for QRS detection. IEEE Trans. Biomed. Eng., 2014; 61(2), 317–326.

  • Faezipour, M., Nourani, M. and Panigrahy, R., A real-time worm outbreak detection system using shared counters. In Proceedings of the 15th Annual IEEE Symposium High Performing Interconnects, Dallas, 2007, pp. 65–72.

  • Song, M. H., Lee, J., Cho, S. P., Lee, K. J. and Yoo, S. K., Support vector machine based arrhythmia classification using reduced features. Int. J. Control, Autom. Syst., 2005, 3(4), 571–579.

  • Besrour, R., Lachiri, Z. and Ellouze, N., ECG beat classifier using support vector machine. In Proceedings of the Third IEEE International Conference on Information and Communication Technology: From Theory Application, 2008, pp. 1–5.

  • Hu, Y. H., Palreddy, S. and Tompkins, W. J., A patient adaptive ECG beat classifier using a mixture of experts approach. IEEE Trans. Biomed. Eng., 1997, 44(9), 891–900.

  • de Chazal, P., O’Dwyer, M. and Reilly, R. B., Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng., 2004, 51(7), 1196–1206.

  • Christov, I., Gómez-Herrero, G., Krasteva, V., Jekova, I., Gotchev, A. and Egiazarian, K., Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification. Med. Eng. Phys., 2006, 28(9), 876–887.

  • Haseena, H. H., Mathew, A. T. and Paul, J. K., Fuzzy clustered probabilistic and multi layered feed forward neural networks for electrocardiogram arrhythmia classification. J. Med. Syst., 2009, 29(4), 1–10.

  • Chandrakar, C. and Sharma, M., A real time approach for ECG signal denoising and smoothing using adaptive window technique. In IEEE 9th International Conference on Industrial and Information Systems, Indian Institute of Information Technology, Gawlior, 2014, pp. 1–6.


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  • System Design Approach for Heartbeat Detection and Classification of Individuals Irrespective of their Physical Condition

Abstract Views: 425  |  PDF Views: 129

Authors

Chinmay Chandrakar
Department of Electronics and Telecommunication, Shri Shankaracharaya College of Engineering and Technology, Chhattisgarh Swami Vivekananda Technical University, Bhilai 490 020, India
Monisha Sharma
Department of Electronics and Telecommunication, Shri Shankaracharaya College of Engineering and Technology, Chhattisgarh Swami Vivekananda Technical University, Bhilai 490 020, India

Abstract


In an electrocardiogram (ECG), the heartbeat feature QRS is an important parameter for analysis in any heartbeat classification automated diagnosis system. In this communication the method which we have proposed is by using the counter which is used in parallel. The first level is detection of heartbeats, which uses hashing of ECG features. In the second level, the profiler profiles a person's regular and irregular ECG characteristic behaviour. The proposed method works on data related with ECG, instead of particular features of ECG. Because of parallel processing, data storage unit requirements and the processing time are less. The dependent values in the proposed method vary according to the changes in the ECG waveform. Such type of analysis is suitable for detection of heart disease. The most significant application of such characteristic plotting is to generate an alert signal for irregular ECG behaviour in a person. Such automated system will be useful in remote areas where a cardiologist may not be easily available.

Keywords


Data Storage Units, Electrocardiogram Signal, Parallel Processing, QRS Detection.

References





DOI: https://doi.org/10.18520/cs%2Fv112%2Fi09%2F1915-1920