Detection and Classification of ECG Signal Through Machine Learning
Shalini Sahay1, A.K. Wadhwani2, Sulochana Wadhwani3, Sarita S. Bhadauria4
1Shalini Sahay, Research Scholar, RGPV Bhopal, India.
2A.K. Wadhwani, EE Department, MITS, Gwalior, India.
3Sulochana Wadhwani, EE Department, MITS, Gwalior, India.
4Sarita S. Bhadauria, EC Department, MITS, Gwalior, India.
Manuscript received on 06 August 2019 | Revised Manuscript received on 12 August 2019 | Manuscript published on 30 August 2019 | PP: 3221-3227 | Volume-8 Issue-10, August 2019 | Retrieval Number: J11650881019/2019©BEIESP | DOI: 10.35940/ijitee.J1165.0881019
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: The electrical activity which might be acquired by inserting the probes on the body exterior that is originated within the individual muscle cells of the heart and is summed to indicate an indication wave form referred to as the EKG (ECG). Cardiac Arrhythmia is an associate anomaly within the heart which may be diagnosed with the usage of signals generated by Electrocardiogram (ECG). For the classification of ECG signals a software application model was developed and has been investigated with the usage of the MIT-BIH database. The version is based on some existing algorithms from literature, entails the extraction of a few temporal features of an ECG signal and simulating it with a trained FFNN. The software version may be employed for the detection of coronary heart illnesses in patients. The neural network’s structure and weights are optimized using Particle Swarm Optimization (PSO). The FFNN trained with set of rules by PSO increase its accuracy. The overall accuracy and sensitivity of the algorithm is about 93.687 % and 92%.
Index Terms: Baseline Wander, Electrocardiogram, Feature Extraction, FFNN, PSO.
Scope of the Article: Classification