Machine learning tools for Diagnosis of Heart Arrhythmia
| Thesis title in Czech: | Metody strojového učení pro klasifikaci arytmie EEG signálu |
|---|---|
| Thesis title in English: | Machine learning tools for Diagnosis of Heart Arrhythmia |
| Key words: | strojové učení |
| English key words: | Machine Learning, Heart Arrhythmia, Electrocardiogram, Discrete Wavelet Transform, Support Vector Machine, MIT-BIH Arrhythmia Database |
| Academic year of topic announcement: | 2019/2020 |
| Thesis type: | Bachelor's thesis |
| Thesis language: | angličtina |
| Department: | Department of Theoretical Computer Science and Mathematical Logic (32-KTIML) |
| Supervisor: | Mgr. Marta Vomlelová, Ph.D. |
| Author: | hidden - assigned and confirmed by the Study Dept. |
| Date of registration: | 27.11.2019 |
| Date of assignment: | 29.11.2019 |
| Confirmed by Study dept. on: | 11.05.2020 |
| Date and time of defence: | 07.07.2020 09:00 |
| Date of electronic submission: | 15.05.2020 |
| Date of submission of printed version: | 28.05.2020 |
| Date of proceeded defence: | 07.07.2020 |
| Opponents: | doc. Mgr. Martin Pilát, Ph.D. |
| Guidelines |
| The student reviews recent papers on machine learning tools applied to ECG beat arrhythmia recognition. The student selects a public dataset to focus on, MIT-BIH Arrhythmia Database is expected but not necessary.
Selected method will be experimentally evaluated. Recommended choice is the analysis of the Discrete Wavelet Transform preprocessing on the performance of a simple QRS detector. |
| References |
| Alickovic E. & Subasi A. (2016). Medical Decision Support System for Diagnosis of Heart Arrhythmia using DWT and Random Forests Classifier. Journal of Medical Systems 2016 vol: 40 (4) pp: 1-12
Nayak C. et al. (2016). Identification of Arrhythmia Classes Using Machine-Learning Techniques. International Journal of Biology and Biomedicine. 2016 vol: 1 pp: 48-53 Acir N . (2006). A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems. Expert Systems with Applications. 31 (2006) 150–158 Desai, Usha, Martis, Roshan Joy et al. (2016). Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques. 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015. MIT-BIH Arrhythmia Database https://www.physionet.org/content/mitdb/1.0.0/ |