Recognition of arrhythmic Electrocardiogram using Wavelet based Feature Extraction Authors Atrija Singh Dept. Of Electronics and Communication Engineering Academy Of Technology Debanshu Bhowmick Department Of Applied Electronics and Instrumentation Engineering Subhadeep Biswas

Outline Objective Dataset Methodology Results Conclusions and Future Scopes

Objective of the Study To develop a unique feature extraction approach to classify a set of ECG signals into normal and arrhythmic set

Dataset Collected from MIT-BIH ARRHYTMIA DATABASE Sampled at 360 Hz Considered 35 ECG recording of 1 minute duration The 15 recordings correspond to healthy Subjects while the rest are associated with diseased(arrhythmic )Subjects. The signals were High-pass filtered using a 6th order Butterworth filter at cut off frequency 0.5 Hz to remove the base line drift. Savitzky Golay filter were used for smoothening the ECG signal and removing any noise. ECG Signal Acquisition High Pass Butterworth Filter Savitzky Golay Filter Analog to Digital Conversion at 360 Hz Sampling Frequency

Classification Scheme ECG Signals in Digital Form Feature Extraction (Time Domain) Classifiers

Previously Used Approaches on computer based Arrhythmia detection Daqrouq et al proposal Wavelet transform to recognize Arrhythmic ECG recordings Rizel et al proposal Hjorth descriptor to classify ECG signal Wachowiak et al proposal Analyzing multi resolution wavelet entropy with visual analytics Balachandran et al proposal Daubechies algorithm for ECG signal feature extraction

Proposed Time Domain Multi-Feature Set Proposed Multi Feature Set Hjorth Descriptor Entropy

Classifiers Used Classifier A: Ensemble(Subspace K-NN) Classifier B: Linear SVM Classifier C: Weighted K-NN

Division of Dataset for Classification Training : 60% Validation : 40%

Results Classification Accuracy (%) Feature Set Used Set I Set IV Classification performance comparison with DWT Coefficients(Set I) and Our Proposed feature Set IV Classification performance comparison with HJORTH Descriptor (Set II) and Our Proposed feature Set IV Classification Accuracy (%) Feature Set Used Set I Set IV Ensemble(Subspace K-NN) 81.8 82.9 Linear SVM 76.0 80.0 Weighted K-NN 74.3 77.0 Classification Accuracy (%) Feature Set Used Set II Set IV Ensemble(Subspace K-NN) 63.6 82.9 Linear SVM 68.6 80.0 Weighted K-NN 66.7 77.0

Classification Accuracy (%) Classification performance with entropy(Set III) and our proposed feature Set IV Classification Accuracy (%) Feature Set Used Set III Set IV Ensemble(Subspace K-NN) 79.9 82.9 Linear SVM 62.9 80.0 Weighted K-NN 74.3 77.0

Confusion Matrix for Ensemble (Subspace K-NN) classifier

Conclusions and Future Scope Our feature set shows a good score of accuracy with Ensemble(Subspace K-NN)Classifier Only R peak count cannot be considered as a good scheme for disease detection. HRV can not be treated as the sole parameter to classify arrhythmic ECG signals. We must calculate other attributes of the ECG signals for better and accurate detection. This study can be further implemented for classification and clustering of other bio-signals.

Thank You

References [1] K. Daqrouq and I. N. Abu-Isbeih, "Arrhythmia Detection using Wavelet Transform," in EUROCON, 2007. The International Conference on "Computer as a Tool", 2007. [2] A. Rizal and S. Hadiyoso, "ECG signal classification using Hjorth Descriptor," in Automation, Cognitive Science, Optics, Micro Electro- Mechanical System, and Information Technology (ICACOMIT), 2015 International Conference on, 2015. [3] M. P. Wachowiak, R. Wachowiak-Smolikova, D. J. DuVal and M. J. Johnson, "Analyzing multiresolution wavelet entropy of ECG with visual analytics techniques," in Electrical and Computer Engineering (CCECE), 2016 IEEE Canadian Conference on, 2016. [4] A. Balachandran, M. Ganesan and E. P. Sumesh, "Daubechies algorithm for highly accurate ECG feature extraction," in Green Computing Communication and Electrical Engineering (ICGCCEE), 2014 International Conference on, 2014. [5] G. Moody and R. Mark, " The impact of the MIT-BIH Arrhythmia Database," IEEE Eng in Med and Biol, vol. 20, no. 3, pp. 45-50, 2001. [6] S. P. M and S. E. M, "Analysis of ECG signal denoising using discrete wavelet transform," in Engineering and Technology (ICETECH), 2016 IEEE international conference on ,2016