Prediction of ECG-Biomarkers for Fetal Arrhythmia Using Non-invasive Fetal ECG

Ela, Razia Zaman

dc.contributor.author	Ela, Razia Zaman
dc.date.accessioned	2023-03-16T06:24:27Z
dc.date.available	2023-03-16T06:24:27Z
dc.date.issued	2022-05-30
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dc.identifier.uri	http://hdl.handle.net/123456789/1774
dc.description	Supervised by Dr. Md. Azam Hossain, Asst. Professor, Department of Computer Science and Engineering(CSE), Islamic University of Technology (IUT) Board Bazar, Gazipur-1704, Bangladesh. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.	en_US
dc.description.abstract	A noninvasive Fetal Electrocardiogram (ECG) is supposed to be a potential prognostic tool in fetal arrhythmia identification and post-treatment. Fetal ECG data generated cannot be real-time accumulated, processed, and used for enterprise-level healthcare and wellness services with the existing fetal heart monitoring system used in hospitals. This study aims to quantify the ECG biomarkers and predict Fetal arrhythmia using Non-invasive fetal ECG data. We investigated the recordings of a total of 24 pregnant women using the Non-Invasive Fetal ECG Arrhythmia Database (NIFEA DB) (February 19, 2019) from physionet.org. We extracted ECG Fiducial Features and performed various statistical analyses on them to quantify ECG biomarkers. After performing statistical analysis we can conclude that Fetal arrhythmia ECG changes are associated with the ECG fiducial features. Machine-learning algorithms were investigated to predict fetal arrhythmia through Noninvasive Fetal ECG signals. The Overall accuracy of various Machine Learning Models is as followed: C5.0 is 95%, KNN is 94 %, CHAID is 90%, Neural Network is 81 %, and CART is 79 %. A noninvasive Fetal ECG-based Fetal Arrhythmia prediction approach is expected to utilize in a wearable fetus monitoring system.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh	en_US
dc.subject	Bio signal processing, Fetal ECG, machine learning, statistical analysis	en_US
dc.title	Prediction of ECG-Biomarkers for Fetal Arrhythmia Using Non-invasive Fetal ECG	en_US
dc.type	Thesis	en_US