Automatic Annotation of Seismocardiogram With High-Frequency Precordial Accelerations

• 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
• DOI: 10.1109/JBHI.2014.2360156

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.