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Volume - 3 | Issue - 1 | march 2021

Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach
Dr. T. Vijayakumar, Mr. R. Vinothkanna, Dr. M. Duraipandian  312  151
Pages: 1-16
Cite this article
Vijayakumar, D. T., Vinothkanna, M. R. & Duraipandian, D. M. (2021). Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach. Journal of Artificial Intelligence and Capsule Networks, 3(1), 1-16. doi:10.36548/jaicn.2021.1.001
Published
11 March, 2021
Abstract

Our human heart is classified into four sections called the left side and right side of the atrium and ventricle accordingly. Monitoring and taking care of the heart of every human is the very essential part. Therefore, the early prediction is essential to save and give awareness to humans about diet plan, lifestyle schedule. Also, this is used to improve the clinical diagnosis and treatment of any patients. To predict or identifying any cardiovascular problems, Electro Cardio Gram (ECG) is used to record the electrical signal of the heart from the body surface of humans. The algorithm learns the dataset from before cluster is called supervised; The algorithm learns to train the data from the set of a dataset is called unsupervised. Then the classification of more amount of heartbeat for different category of normal, abnormal, irregular heartbeats to detect cardiovascular diseases. In this research article, a comparison of various methods to classify the dataset with a fusion-based feature extraction method. Besides, our research work consists of a de-noising filter to reconstruct the raw data from the original input. Our proposed framework performing preprocessing that consists of a filtering approach to remove noises from the raw data set. The signal is affected by thermal noise and instrumentation noise, calibration noise due to power line fluctuation. This interference is high in many handheld devices which can be eliminated by de-noising filters. The output of the de-noising filter is input for fusion-based feature extraction and prediction model construction. This workflow progress has given good results of classifier effectiveness and imbalance arrangement conditions. We achieved good accuracy 96.5% and minimum computation time for classification of ECG signal.

Keywords

ECG classification Random Forest Feature Extraction

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