Title: Implementation of non-contact bed embedded ballistocardiogram signal measurement and valvular disease detection from this BCG signal

Authors: M.A. Hafiz; Abdullah Mahammed Hashem; Ainul Anam Shahjamal Khan; Md. Hossainur Rashid; Md. Azad Kabir Faruqui

Addresses: Department of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology (CUET), Chittagong, Bangladesh ' Department of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology (CUET), Chittagong, Bangladesh ' Department of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology (CUET), Chittagong, Bangladesh ' Department of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology (CUET), Chittagong, Bangladesh ' Department of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology (CUET), Chittagong, Bangladesh

Abstract: In a traditional system, ECG leads are connected to the patient's chest to detect the electrical performance of the heart which create long term discomfort for the patient. As ballistocardiogram (BCG) and valvular diseases are both mechanical phenomena, we conjectured that valvular disease could be diagnosed from non-contact BCG measurement. In this paper, we proposed a non-contact way to determine the valvular diseases of the heart which is favourable for long term observation of the patient. We classified the data using artificial neural network (ANN) and support vector machine (SVM). We collected data from normal persons and persons affected by mitral and pulmonary valve stenosis. We compared the result using overall accuracy, misclassification rate and fitness. We got the highest test accuracy of 79.12% for SVM technique for decomposition level 1. As this technique is completely new and advantageous, it can lead to a new research area of valvular disease detection.

Keywords: non-contact; ballistocardiogram; BCG; electrocardiogram; ECG; valvular disease; artificial neural network; supporting vector machine.

DOI: 10.1504/IJMEI.2021.115970

International Journal of Medical Engineering and Informatics, 2021 Vol.13 No.4, pp.289 - 296

Received: 02 Apr 2019
Accepted: 19 Dec 2019
Published online: 06 Jul 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article