Date of Completion

12-15-2019

Embargo Period

11-18-2019

Advisors

Dr. Ki Chon, Dr. Shengli Zhou, Dr. Monty Escabi

Field of Study

Electrical Engineering

Degree

Master of Science

Open Access

Open Access

Abstract

Detection of atrial fibrillation (AF) from a wrist watch photoplethysmography (PPG) signal is important because the wrist watch form factor enables long term continuous monitoring of arrhythmia in an easy and non-invasive manner. We have developed a novel method not only to detect AF from a smart wrist watch PPG signal, but also to determine whether the recorded PPG signal is corrupted by motion artifacts or not. In order to detect the motion and noise artifacts from PPG signal, we have used both the accelerometer signal and variable frequency complex demodulation based time-frequency analysis of the PPG signal. After the clean PPG signals are determined, we use the root mean square of successive differences and sample entropy, calculated from the beat-to-beat intervals of the PPG signal, to distinguish AF from normal rhythm. Next, we use a premature atrial and ventricular contraction detection algorithm to have more accurate AF identification and to reduce false alarms. Two separate data sets have been used in this study to test the efficacy of our proposed method, which shows a combined sensitivity, specificity and accuracy of 98.18%, 97.43% and 97.54% across the data sets.

Major Advisor

Dr. Ki Chon

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