A Novel Deep Learningbased Model for the Efficient Classification of Electrocardiogram Signals

Cardiometry

article

Saurabh Mehata¹  Rakesh Ashok Bhongade²  Roopashree Rangaswamy³ 

[1] Department of Life Science, Faculty of Applied Science, Parul University Vadodara;Department of Panchkarma, Sanskriti University Mathura;Department of Chemistry, School of Sciences, B-II, Jain ,(Deemed to be University) JC Road

关键词: Arrhythmia;    Congestive Heart Failure (CHF);    Deep learning;    Deep Neural Network;    Electrocardiogram (ECG);    Convolution neural network (CNN);   

DOI  :  10.18137/cardiometry.2022.24.10331039

学科分类：环境科学（综合）

来源: Russian New University

【 摘 要 】

To manage healthcare, an electrocardiogram, often known as an “EKG” or “ECG”, is a measurement of the electrical activity of the organ “heart”. Deep Learning (DL) or Deep Neural Networks have recently attracted the attention of researchers in many other sectors, including healthcare and medicine. There has been a frequent rise in the number of researchers developing the model to classify and detect several diseases, out of which cardiac complications are the keen focus due to the mortality associated with it. Therefore, the objective of the present research is to develop a classification model for efficient and accurate classification of signals received from ECG. The present study uses a “deep neural network” for the classification of the ECG signal into a total of five criteria including Normal ECG, QRS Widening, ST Elevation, ST Depression, and Sinus Rhythm. The developed classification method is tested and evaluated with the “MITBIH arrhythmia database” which revealed significant matrices for all parameters such as “precision”, “accuracy”, “recall”, and “F-1 score”. In addition to that, the proposed model demonstrated competent results which further highlights the practical applicability of the model to implementation and adoption in the healthcare sector.

【 授权许可】

CC BY