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Real-time Detection of Atrial Fibrillation from Short time single lead ECG traces using Recurrent neural networks
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- 1. Real-time Detection of Atrial Fibrillation from Short time single lead ECG traces using Recurrent neural networks Sujadevi VG*, Soman KP and Vinayakumar R 1Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, India.
- 2. Outline • Introduction • Background information / Related works • Proposed Method – Deep Learning • Description of the data set and Results • Summary • Future Work • References 2
- 3. Introduction • Atrial fibrillation (AF) is a disorder of the functioning of the heart’s electrical system that is characterized by the irregular beating of the heart [1]. • Atrial fibrillation (AF) is the predominant type of cardiac arrhythmia affecting more than 45 Million individuals globally. • It is one of the leading contributors of strokes and hence detecting them in real-time is of paramount importance for early intervention. 3
- 4. Background information / Related works • Machine learning methods are used to identify the pathological ECG from the normal sinus rhythm. • Machine learning methods relies on the feature engineering and deposing mechanisms. • Deep learning is a new filed of machine learning which can learn the patterns by taking the raw input ECG signals. 4
- 5. Proposed Method Figure 1. Architecture of proposed system for normal sinus rhythm and atrial fibrillation. 5
- 6. Description of the data set and Results We used the publically available raw signals of Atrial fibrillation (AF) and normal sinus rhythm (NSR) from MITBIH Physionet; MIT- BIH Atrial Fibrillation Database and MIT-BIH Normal Sinus Rhythm Database [2]. 6
- 7. ECG signal Figure (a) A single lead ECG wave form of normal sinus rhythm,(b) A single lead ECG wave form with atrial fibrillation 7
- 8. Contd. Table 1. Summary of test results 8
- 9. Summary and Future work • Deep learning based mechanism such as RNN, LSTM and GRU architecture is proposed to distinguish AF and NSR on a single lead ECG. • All the deep learning methods have performed well, mostly LSTM and GRU outperformed RNN and GRU takes less training cost in comparison to LSTM. • The proposed method is considered as more accurate in real- time ECG classification because it doesn’t rely on any feature engineering mechanisms. • Though the deep network methods showed significant results, we lack in showing the inner mechanics of the deep models. This can be achieved by transforming the non-linearity to linearized form, thereby computing the Eigen values and Eigen vectors on them across time-steps [3]. 9
- 10. References [1] Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby JV, Singer DE. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. JAMA. 2001 May 9;285(18):2370-5. [2] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [3] Moazzezi, R. Change-based population coding. PhD thesis,UCL (University College London), 2011. 10