Atrial Fibrillation Detection from Photoplethysmography Data Using Artificial Neural Networks
Rezaei Yousefi, Zeinab (2018)
2018
Electrical Engineering
Tieto- ja sähkötekniikan tiedekunta - Faculty of Computing and Electrical Engineering
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Hyväksymispäivämäärä
2018-06-06
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201805241825
https://urn.fi/URN:NBN:fi:tty-201805241825
Tiivistelmä
Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmia- especially in elderly and hypertensive patients, leading to increased risk of heart failure and stroke. Therefore, early screening and diagnosis can reduce the AF impact. The development of photoplethysmography (PPG) technology has enabled comfortable and unobtrusive physiological monitoring of heart rate with a wrist-worn device. It is important to examine the possibility of using PPG signal to diagnose AF in real-world situations.
There are several recent studies classifying cardiac arrhythmias with artificial neural networks (ANN) based on RR intervals derived from ECG, but no one has evaluated ANN approach for wrist PPG data. The aim of this MSc thesis is to present an ANN-based classifier to detect AF episodes from PPG data. The used classifier is multilayer perceptron (MLP) that utilizes backpropagation for learning. This classifier is able to distinguish between AF and non-AF rhythms. The input feature of the ANN is based on the information obtained from an interbeat interval (IBI) sequence of 30 consecutive PPG pulses.
The PPG dataset was acquired with PulseOn (PO) wearable optical heart rate monitoring device and the recordings were performed in the post-anesthesia care unit of Tampere University Hospital. The study was approved by the local ethical committee. The guidelines of the Declaration of Helsinki were followed. In total 30 patients with multiple comorbidities were monitored during routine postoperative treatment. 15 subjects had sinus rhythm (SR) and 15 had AF during the recording. The average duration of each recording was 1.5 hours. The monitoring included standard ECG as a reference and a wrist-worn PPG monitor with green and infrared light sources.
As IBIs extracted from the PPG signals are highly sensitive to motion artefacts, IBI reliability was automatically evaluated using PPG waveform and acceleration signals before AF detection. Based on the achieved results, the ANN algorithm demonstrated excellent performance at recognizing AF from SR, using wrist PPG data.
There are several recent studies classifying cardiac arrhythmias with artificial neural networks (ANN) based on RR intervals derived from ECG, but no one has evaluated ANN approach for wrist PPG data. The aim of this MSc thesis is to present an ANN-based classifier to detect AF episodes from PPG data. The used classifier is multilayer perceptron (MLP) that utilizes backpropagation for learning. This classifier is able to distinguish between AF and non-AF rhythms. The input feature of the ANN is based on the information obtained from an interbeat interval (IBI) sequence of 30 consecutive PPG pulses.
The PPG dataset was acquired with PulseOn (PO) wearable optical heart rate monitoring device and the recordings were performed in the post-anesthesia care unit of Tampere University Hospital. The study was approved by the local ethical committee. The guidelines of the Declaration of Helsinki were followed. In total 30 patients with multiple comorbidities were monitored during routine postoperative treatment. 15 subjects had sinus rhythm (SR) and 15 had AF during the recording. The average duration of each recording was 1.5 hours. The monitoring included standard ECG as a reference and a wrist-worn PPG monitor with green and infrared light sources.
As IBIs extracted from the PPG signals are highly sensitive to motion artefacts, IBI reliability was automatically evaluated using PPG waveform and acceleration signals before AF detection. Based on the achieved results, the ANN algorithm demonstrated excellent performance at recognizing AF from SR, using wrist PPG data.