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dc.creatorRezaei Yousefi, Zeinab
dc.date.accessioned2018-05-23T11:22:24Z
dc.date.available2018-05-23T11:22:24Z
dc.identifier.urihttp://dspace.cc.tut.fi/dpub/handle/123456789/26158
dc.description.abstractAtrial 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.en
dc.format.extent66en
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.rightsThis publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
dc.titleAtrial Fibrillation Detection from Photoplethysmography Data Using Artificial Neural Networksen
dc.identifier.urnURN:NBN:fi:tty-201805241825
dc.contributor.laitosElektroniikka ja tietoliikennetekniikka – Electronics and Communications Engineeringen
dc.contributor.tiedekuntaTieto- ja sähkötekniikan tiedekunta – Faculty of Computing and Electrical Engineeringen
dc.contributor.yliopistoTampereen teknillinen yliopisto - Tampere University of Technology
dc.programmeElectrical Engineeringen
dc.date.published2018-06-06
dc.permissionPermission granteden
dc.contributor.thesisadvisorParak, Jakub
dc.contributor.degreesupervisorVehkaoja, Antti
dc.type.ontasotDiplomityö - Master's thesis


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