Classification of atrial fibrillation using second order dynamic system with pattern recognition algorithm
According to World Health Organization (WHO), an estimated 17.9 million people died from cardiovascular diseases (CVD) in 2019, representing 32 % of all global deaths. Of these deaths, 85 % were due to heart attack and stroke. The occurrence and prevalence of atrial fibrillation (AF) is growing worl...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2022
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/99515/1/LeeWeiQiMSKE2022.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | According to World Health Organization (WHO), an estimated 17.9 million people died from cardiovascular diseases (CVD) in 2019, representing 32 % of all global deaths. Of these deaths, 85 % were due to heart attack and stroke. The occurrence and prevalence of atrial fibrillation (AF) is growing worldwide. Limited tools are available to evaluate clinical outcomes and response to thrombolysis in stroke patients with AF. Therefore, this study analysed the ECG features of AF and the normal sinus rhythm signals for AF recognition. The first objective is to extract AF features using second-order dynamic system (SODS) algorithm. The following objective is to investigate the effect of windowing length towards AF classification. Next, to compare the two-pattern recognition machine learning support vector machine (SVM) and artificial neural network (ANN) on the accuracy, specificity, and sensitivity of AF classification. In this study, the ECG signals database from Physiobank included MITBIH Atrial Fibrillation Dataset and MITBIH Normal Sinus Rhythm Dataset are used. For signal pre-processing, butterworth filter are used to diminish the muscle noise and the features signals are extracted by using second order dynamic system. Multiple episodes of the windowing size 2s, 4s, 6s, 8s and 10s included in this design to evaluate the appropriate windowing size for AF signal processing. The pattern recognition machine learning SVM algorithm has higher accuracy compared to ANN accuracy of AF classification, which are having 100 % with 4s windowing size. In conclusion, the 4s windowing size having the highest detection rate in AF classification system. |
|---|