ECG Classification using Machine Learning
K Sandeep1, Padmavathi Kora2, K Swaraja3, K Meenakshi4, Lakshmi kala Pampana5
1K Sandeep*, M Tech Scholar, Dept. of ECE, GRIET, Hyderabad, India.
2Padmavathi Kora*, Dept. of ECE, GRIET, Hyderabad, India.
3K Swaraja, Professor, Dept. of ECE, GRIET, Hyderabad, India.
4K Meenakshi, Professor, Dept. of ECE, GRIET, Hyderabad, India.
5Lakshmi kala Pampana, Assistant Professor, Dept. of ECE, GRIET Hyderabad, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 2492-2494 | Volume-8 Issue-4, November 2019. | Retrieval Number: D6989118419/2019©BEIESP | DOI: 10.35940/ijrte.D6989.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recently, the obvious increasing number of cardiovascular disease, the automatic classification research of Electrocardiogram signals (ECG) has been playing a important part in the clinical diagnosis of cardiovascular disease. Convolution neural network (CNN) based method is proposed to classify ECG signals. The proposed CNN model consists of five layers in addition to the input layer and the output layer, i.e., two convolution layers, two down sampling layers and one full connection layer, extracting the effective features from the original data and classifying the features using wavelet .The classification of ARR (Arrhythmia), CHF (Congestive Heart Failure), and NSR (Normal Sinus Rhythm) signals. The experimental results contains on ARR signals from the MIT-BIH arrhythmia,CHF signals from the BIDMC Congestive Heart Failure and NSR signals from the MIT-BIH Normal Sinus Rhythm Databases show that the proposed method achieves a promising classification accuracy of 90.63%, significantly outperforming several typical ECG classification methods.
Keywords: Cardiovascular Disease; Convolution Neural Network; ECG Signal Classification; Wavelet Transform.
Scope of the Article: Machine Learning.