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Main Title: A fast multimodal ectopic beat detection method applied for blood pressure estimation based on pulse wave velocity measurements in wearable sensors
Author(s): Pflugradt, Maik
Geißdörfer, Kai
Görnig, Matthias
Orglmeister, Reinhold
Type: Article
Language Code: en
Abstract: Automatic detection of ectopic beats has become a thoroughly researched topic, with literature providing manifold proposals typically incorporating morphological analysis of the electrocardiogram (ECG). Although being well understood, its utilization is often neglected, especially in practical monitoring situations like online evaluation of signals acquired in wearable sensors. Continuous blood pressure estimation based on pulse wave velocity considerations is a prominent example, which depends on careful fiducial point extraction and is therefore seriously affected during periods of increased occurring extrasystoles. In the scope of this work, a novel ectopic beat discriminator with low computational complexity has been developed, which takes advantage of multimodal features derived from ECG and pulse wave relating measurements, thereby providing additional information on the underlying cardiac activity. Moreover, the blood pressure estimations’ vulnerability towards ectopic beats is closely examined on records drawn from the Physionet database as well as signals recorded in a small field study conducted in a geriatric facility for the elderly. It turns out that a reliable extrasystole identification is essential to unsupervised blood pressure estimation, having a significant impact on the overall accuracy. The proposed method further convinces by its applicability to battery driven hardware systems with limited processing power and is a favorable choice when access to multimodal signal features is given anyway.
Issue Date: 14-Jan-2017
Date Available: 25-Jan-2017
DDC Class: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Subject(s): ectopic beat detection
pulse wave velocity
blood pressure estimation
pulse arrival time
multimodal signal processing
wearable sensor network
Sponsor/Funder: DFG, TH 662/19-1, Open Access Publizieren 2017 - 2018 / Technische Universität Berlin
Journal Title: Sensors
Publisher: MDPI
Publisher Place: Basel
Volume: 17
Issue: 1
Article Number: 158
Publisher DOI: 10.3390/s17010158
Appears in Collections:FG Elektronik und medizinische Signalverarbeitung » Publications

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