Using our tools backwards, AF detection by confusing time and frequency

Kraaijeveld, M.

Using our tools backwards, AF detection by confusing time and frequency

Master thesis (2024)

Authors

M. Kraaijeveld Electrical Engineering, Mathematics and Computer Science

Contributors

Richard C. Hendriks Signal Processing Systems (mentor)

C. Varon Signal Processing Systems (mentor)

Jorge Martinez (graduation committee member)

Faculty

Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science

Modelling Detection Atrial Fibrillation Heart Electrocardiogram (ECG) Gaussian Mixture Modeling (GMM) ARMA models

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Published Date

09-01-2024

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Atrial Fibrillation or AF is the most common heart rhythm anomaly affecting millions of people. This work explores the possibilities of reinterpreting speech processing techniques for use in atrial fibrillation detection. An existing method of modelling single heartbeat, single lead ECG signals by means of an ARMA model's amplitude response as a time domain signal is implemented. The parameters of the models are then used for AF detection by means of detecting P wave absence. For this detection, the distribution of the P wave associated parameters is compared to a GMM model of normal sinus rhythm beats obtained from a large number of recordings from different sources.

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