1. 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

2. Outline Objective Dataset Methodology Results
Conclusions and Future Scopes

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

4. 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

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

6. 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

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

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

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

10. 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

11. 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

12. Confusion Matrix for Ensemble (Subspace K-NN) classifier

13. 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.

14. Thank You

15. References
[1] K. Daqrouq and I. N. Abu-Isbeih, "Arrhythmia Detection using Wavelet Transform," in EUROCON, The International Conference on "Computer as a Tool", [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, [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, [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, [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 , [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