Sugiarto, Irena and Iskandar, Aulia Arif and Sapardan, Ade Imasanti (2019) Real-Time Wearable ECG Signal Analysis For Cardiac Arrhythmia Detection. Bachelor thesis, Swiss German University.

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Abstract

Cardiac arrhythmias are irregular heartbeats caused by malfunctions of the heart’s electrical system. Usually harmless, arrhythmias can be predispositions to severe heart diseases. Since arrhythmia does not emerge in a short time, some indications might pass undetected during clinical electrocardiogram (ECG) tests. Therefore, wearable ECG with arrhythmia recognition is emerging. This research has succeeded to develop embedded software for arrhythmias recognition in real-time, such as irregular beats, premature ventricular contraction, premature atrial contraction, supraventricular tachyarrhythmia, and atrial fibrillation. The recognition began with acquiring ECG signal Lead I using a single-lead sensor. The main idea is to construct the algorithms that analyses the amplitude and duration of the P-QRS wave, in which contained useful information about the characteristics of each type of arrhythmias. The developed algorithm is a combination of Kim-Lee formula and Pan-Tompkins algorithm to precisely locate the Q-wave and S-wave locations as well as calculating the QS-interval. The software is then embedded to a wearable ECG and underwent performance tests by implementations onto databases and human subjects in a hospital with cardiac arrhythmia risk. The output is transmitted via Bluetooth Low Energy and displayed in smartphone applications. This method gave ease to both doctors and patients in handling arrhythmias.

Item Type:	Thesis (Bachelor)
Uncontrolled Keywords:	Cardiac Arrhythmia ; Real-Time Signal Analysis ; Wearable ECG ; Qs-Interval ; Smartphone
Subjects:	R Medicine > R Medicine (General) > R856 Biomedical engineering R Medicine > RC Internal medicine > RC683.5 Electrocardiography
Divisions:	Faculty of Life Sciences and Technology > Department of Biomedical Engineering
Depositing User:	Maria Lamury
Date Deposited:	21 Aug 2020 04:42
Last Modified:	21 Aug 2020 04:42
URI:	http://repository.sgu.ac.id/id/eprint/1741

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