ECG Pattern Analysis Using Artificial Neural Network
|International Journal of Electronics and Communication Engineering|
|© 2020 by SSRG - IJECE Journal|
|Volume 7 Issue 5|
|Year of Publication : 2020|
|Authors : Shubham Srivastava, Himanshu Bhardwaj, Aman Dixit, Prof. Namita Kalyan Shinde|
How to Cite?
Shubham Srivastava, Himanshu Bhardwaj, Aman Dixit, Prof. Namita Kalyan Shinde, "ECG Pattern Analysis Using Artificial Neural Network," SSRG International Journal of Electronics and Communication Engineering, vol. 7, no. 5, pp. 1-4, 2020. Crossref, https://doi.org/10.14445/23488549/IJECE-V7I5P101
(ECG) Electrocardiogram is the most noteworthy analytical examining tool used to determine the heart's health status. The most common heart disease is Arrhythmia, which can be detected by observing ECG and QRS complex noise and distortions within a patient's given time interval. ECG waves consisting of a P, QRS, and T wave can be used to analyze the pattern for any abnormalities present in the heart. Any noise or distortion seen in ECG is directly associated with abnormalities in the heart, so the major job of this project is to reduce the complexity and time in the classification of an ECG signal.
The project focuses on the biomedical signal processing-based approach for real-time self-classification of ECG signals; these features are usually a combination of statistical and morphological features. Adaptive Neuro-Fuzzy Inference System (ANFIS) is trained to distinguish the ECG pattern once the feature extraction is done. We classify the three classes as Normal, Fusion, and PVC. ANFIS is one of the most reliable methods for classification purposes of the three classes. Its result usually indicates an accuracy of more than 95%. Analyzing long term ECG signal say 24 hours is a time consuming and tedious job, hence is expected to automate the whole procedure of classification and diagnostics.
Artificial Neural Network (ANN), Fusion, PVC, Normal, Electrocardiogram (ECG), Python
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