IoT based ECG monitoring system with encryption and authentication in secure data transmission for clinical health care approach. (April 2022)

• Record Type: Journal Article
• Authors: Raheja, Nisha; Kumar Manocha, Amit
• Is Part Of: Biomedical signal processing and control. Volume 74(2022)
• Publication Date: 2022-04
• Subjects: ECG -- Triple data encryption standard (3-DES) -- Water cycle optimization (WCO) -- Maximal overlap wavelet packet transform (MOWPT) -- Internet of things (IoT) -- Healthcare -- Authentication -- Golden hour; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103481
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A novel method for secured ECG transmission to cardiologist for clinical healthcare has been proposed. 3-DES has been implemented for encryption and water cycle optimization (WCO) algorithm is employed for authentication. The encrypted and authenticated ECG features are transmitted to cardiologist for analysis via an IoT platform. The significant of the proposed technique attains better value of results as compared to the existing methods. Abstract: Telecardiology offers early detection, consultations and disease management in Golden Hours to patients. Transmitting ECG data to a cardiologist without security may incline to misdiagnosis. In this paper, a novel method for secured ECG transmission to cardiologist for clinical healthcare has been proposed. Firstly, ECG datasets are taken from the MIT-BIH database and pre-processed. Savitzky-Golay (SG) filter has been used for the removal of low-frequency noise, while high frequency noise is eliminated using maximal overlap wavelet packet transform (MOWPT) followed by features extraction. For security, triple data encryption standard (3-DES) has been implemented for encryption and water cycle optimization (WCO) algorithm is employed for authentication. Then encrypted and authenticated ECG features are transmitted to cardiologist for analysis via ThingSpeak, an internet of things (IoT) platform. The performance of the proposed method is calculated viz. avalanche effect is 50.12% and mean square error is 0.463, number of pixels change rate (NPCR) is 100, unified averaged changed intensity (UACI) is 39.698 and execution time is 0.003 msec. The significant of the proposed technique provides minimum error with less processing time, better values of avalanche effect, NCPR and UACI as compared to the existing methods.