Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100444
Title: Automatic identification of systolic time intervals in seismocardiogram
Authors: Shafiq, Ghufran
Tatinati, Sivanagaraja
Ang, Wei Tech
Veluvolu, Kalyana C.
Keywords: Cardiovascular Diseases
Electrocardiography
DRNTU::Engineering::Mechanical engineering
Issue Date: 2016
Source: Shafiq, G., Tatinati, S., Ang, W. T., & Veluvolu, K. C. (2016). Automatic identification of systolic time intervals in seismocardiogram. Scientific Reports, 6, 37524-. doi:10.1038/srep37524
Series/Report no.: Scientific Reports
Abstract: Continuous and non-invasive monitoring of hemodynamic parameters through unobtrusive wearable sensors can potentially aid in early detection of cardiac abnormalities, and provides a viable solution for long-term follow-up of patients with chronic cardiovascular diseases without disrupting the daily life activities. Electrocardiogram (ECG) and siesmocardiogram (SCG) signals can be readily acquired from light-weight electrodes and accelerometers respectively, which can be employed to derive systolic time intervals (STI). For this purpose, automated and accurate annotation of the relevant peaks in these signals is required, which is challenging due to the inter-subject morphological variability and noise prone nature of SCG signal. In this paper, an approach is proposed to automatically annotate the desired peaks in SCG signal that are related to STI by utilizing the information of peak detected in the sliding template to narrow-down the search for the desired peak in actual SCG signal. Experimental validation of this approach performed in conventional/controlled supine and realistic/challenging seated conditions, containing over 5600 heart beat cycles shows good performance and robustness of the proposed approach in noisy conditions. Automated measurement of STI in wearable configuration can provide a quantified cardiac health index for long-term monitoring of patients, elderly people at risk and health-enthusiasts.
URI: https://hdl.handle.net/10356/100444
http://hdl.handle.net/10220/46512
DOI: 10.1038/srep37524
Rights: © 2016 The Authors (Nature Publishing Group). This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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