title: A Fast Multimodal Ectopic Beat Detection Method Applied for Blood Pressure Estimation Based on Pulse Wave Velocity Measurements in Wearable Sensors.
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.
authors.affiliation: Chair of Electronics and Medical Signal Processing, TU Berlin, 10623 Berlin, Germany. Maik.Pflugradt@googlemail.com.
authors.forename: Maik
authors.initials: M
authors.lastname: Pflugradt
authors.affiliation: Chair of Electronics and Medical Signal Processing, TU Berlin, 10623 Berlin, Germany. Kai.Geissdoerfer@mailbox.tu-berlin.de.
authors.forename: Kai
authors.initials: K
authors.lastname: Geissdoerfer
authors.affiliation: Staedtisches Klinikum Dresden-Neustadt, 01159 Dresden, Germany. Matthias.Goernig@khdn.de.
authors.forename: Matthias
authors.initials: M
authors.lastname: Goernig
authors.affiliation: Chair of Electronics and Medical Signal Processing, TU Berlin, 10623 Berlin, Germany. Reinhold.Orglmeister@tu-berlin.de.
authors.forename: Reinhold
authors.initials: R
authors.lastname: Orglmeister
mesh_terms: D000465:Algorithms; D001794:Blood Pressure; D001795:Blood Pressure Determination; D004562:Electrocardiography; D006801:Humans; D063177:Pulse Wave Analysis; D012815:Signal Processing, Computer-Assisted; D000076251:Wearable Electronic Devices
keywords: blood pressure estimation; ectopic beat detection; pulse arrival time, multimodal signal processing; pulse wave velocity; wearable sensor network