Automatic Annotation of Seismocardiogram With High-Frequency Precordial Accelerations

Seismocardiogram (SCG) is the low-frequency vibrations signal recorded from the chest using accelerometers. Peaks on dorsoventral and sternal SCG correspond to specific cardiac events. Prior research work has shown the potential of extracting such peaks for various types of monitoring and diagnosis...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics Vol. 19; no. 4; pp. 1428 - 1434
Main Authors: Khosrow-khavar, Farzad, Tavakolian, Kouhyar, Blaber, Andrew P., Zanetti, John M., Fazel-Rezai, Reza, Menon, Carlo
Format: Journal Article
Language:English
Published: United States IEEE 01.07.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Acceleration
Accelerometers
Accelerometry - methods
Accuracy
Adolescent
Adult
Algorithms
Annotations
Computer aided instruction
Cooperative work
Databases, Factual
Datasets
Educational institutions
Electrocardiography
Envelopes
Female
Heart rate
High-frequency accelerometer (HFACC)
Humans
Informatics
Male
Mathematical analysis
Phonocardiogram (PCG)
Phonocardiography - methods
Seismocardiogram (SCG)
Signal Processing, Computer-Assisted
Young Adult
seismocardiogram (SCG)
High-frequency accelerometer (HFACC)
phonocardiogram (PCG)
ISSN:2168-2194, 2168-2208, 2168-2208
Online Access:Get full text
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Summary:Seismocardiogram (SCG) is the low-frequency vibrations signal recorded from the chest using accelerometers. Peaks on dorsoventral and sternal SCG correspond to specific cardiac events. Prior research work has shown the potential of extracting such peaks for various types of monitoring and diagnosis applications. However, annotation of these peaks is not a trivial task and complicated in some subjects. In this paper, an automated method is proposed to annotate these peaks. The high-frequency accelerations obtained from the same accelerometer, used to record SCG with, were used to facilitate the annotation of the SCG. Algorithms were developed for detection of isovolumic moment (IM) and aortic valve closure (AC) points of SCG. Four different envelope calculation methods were used: cardiac sound characteristic waveform (CSCW), Shannon, absolute, and Hilbert. The algorithms were evaluated based on a dataset including 18 subjects undergoing lower body negative pressure and were further tested with another dataset, which included 67 subjects. These datasets had been previously manually annotated. The algorithm based on CSCW envelope calculation produced the highest detection accuracy for both IM and AC. The overall CSCW algorithm detection accuracy for the test dataset was 98.7% and 99.1% for the IM and AC points, respectively.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2014.2360156