Detection of electrocardiographic changes in partial epileptic patients using local binary pattern based composite feature
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In this paper, we propose a novel method for detecting electrocardiographic (ECG) changes in partial epileptic patients using a composite feature set. At the core of our approach is a local binary pattern (LBP) based feature representation containing a set of statistical features derived from the distribution of LBPs of the ECG signal. In order to enhance the discriminating power, a set of statistical features are also extracted from the original ECG signal. The composite feature is then generated by combining the two homogeneous feature sets. The discriminating ability of the proposed composite feature is investigated using two different classifiers namely, support vector machine and a bagged ensemble of decision trees. Results from the experimental evaluation on the publicly available MIT-BIH ECG dataset demonstrate the superiority of the proposed features over conventional histogram based LBP features. Our results also show that the proposed approach provides better classification accuracy than methods existing in the literature for classification of normal and partial epileptic beats in ECG.
KeywordsLocal binary pattern Statistical features Electrocardiograph Partial epileptic beat Normal beat
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Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- 1.Scott RA, Lhatoo SD, Sander JWAS (2003) Treatment of epilepsy in developing countries: where do we go from here? Bull World Health Org 79(4):344–351Google Scholar
- 16.Cooman T, De, Carrette E, Boon P, Meurs A, Van Huffel S (2014) Online seizure detection in adults with temporal lobe epilepsy using single-lead ECG. Proc Eur IEEE Signal Proc Conf (EUSIPCO). 1532–1536Google Scholar
- 19.Zhu P, Chatlani N, Soraghan JJ (2012) 1-D local binary patterns based VAD used INHMM-based improved speech recognition. In Proceedings of 20th European IEEE Signal Processing Conference (EUSIPCO), pp 1633–1637Google Scholar
- 20.Kumar TS, Hussain MA, Kanhangad V (2015) Classification of voiced and non-voiced speech signals using empirical wavelet transform and multi-level local patterns. In Proceedings of 20th IEEE International Conference on Digital Signal Processing (DSP 2015), pp 163–167Google Scholar
- 21.McCool P, Chatlani N, Petropoulakis L, Soraghan JJ, Menon R, Lakany H (2012) 1D-local binary patterns for onset detection of myoelectric signals. In Proceedings of 20th European IEEE Signal Processing Conference, pp 499–503Google Scholar
- 25.Louis W, Hatzinakos D, Venetsanopoulos A (2014) One dimensional multi-resolution Local Binary Patterns features (1DMRLBP) for regular electrocardiogram (ECG) waveform detection. In Proceedings 19th IEEE Conference on Digital Signal Processing (DSP 2014), pp 601–606Google Scholar
- 28.Papoulis A (1991) Probability, random variables and stochastic processes, 3rd edn. McGraw-Hill Companies, BostonGoogle Scholar
- 32.Breiman L (1996) Bagging predictors. Springer Mach Learn 24(2):123–140Google Scholar
- 33.Freund RJ, Mohr D, Wilson WJ (2010) Statistical methods, 3rd edn. Academic Press, OxfordGoogle Scholar