
Proceedings Paper
A new algorithm for segmentation of cardiac quiescent phases and cardiac time intervals using seismocardiographyFormat | Member Price | Non-Member Price |
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Paper Abstract
Systolic time intervals (STI) have significant diagnostic values for a clinical assessment of the left ventricle in adults.
This study was conducted to explore the feasibility of using seismocardiography (SCG) to measure the systolic timings
of the cardiac cycle accurately. An algorithm was developed for the automatic localization of the cardiac events (e.g. the
opening and closing moments of the aortic and mitral valves). Synchronously acquired SCG and electrocardiography
(ECG) enabled an accurate beat to beat estimation of the electromechanical systole (QS2), pre-ejection period (PEP)
index and left ventricular ejection time (LVET) index. The performance of the algorithm was evaluated on a healthy test
group with no evidence of cardiovascular disease (CVD). STI values were corrected based on Weissler’s regression
method in order to assess the correlation between the heart rate and STIs. One can see from the results that STIs correlate
poorly with the heart rate (HR) on this test group. An algorithm was developed to visualize the quiescent phases of the
cardiac cycle. A color map displaying the magnitude of SCG accelerations for multiple heartbeats visualizes the average
cardiac motions and thereby helps to identify quiescent phases. High correlation between the heart rate and the duration
of the cardiac quiescent phases was observed.
Paper Details
Date Published: 4 March 2015
PDF: 7 pages
Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94432K (4 March 2015); doi: 10.1117/12.2179346
Published in SPIE Proceedings Vol. 9443:
Sixth International Conference on Graphic and Image Processing (ICGIP 2014)
Yulin Wang; Xudong Jiang; David Zhang, Editor(s)
PDF: 7 pages
Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94432K (4 March 2015); doi: 10.1117/12.2179346
Show Author Affiliations
Mojtaba Jafari Tadi, Univ. of Turku (Finland)
Tero Koivisto, Univ. of Turku (Finland)
Mikko Pänkäälä, Univ. of Turku (Finland)
Ari Paasio, Univ. of Turku (Finland)
Tero Koivisto, Univ. of Turku (Finland)
Mikko Pänkäälä, Univ. of Turku (Finland)
Ari Paasio, Univ. of Turku (Finland)
Timo Knuutila, Univ. of Turku (Finland)
Mika Teräs, Univ. of Turku (Finland)
Pekka Hänninen, Univ. of Turku (Finland)
Mika Teräs, Univ. of Turku (Finland)
Pekka Hänninen, Univ. of Turku (Finland)
Published in SPIE Proceedings Vol. 9443:
Sixth International Conference on Graphic and Image Processing (ICGIP 2014)
Yulin Wang; Xudong Jiang; David Zhang, Editor(s)
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