A Gaussian Model-Based Probabilistic Approach for Pulse Transit Time Estimation

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In this paper, we propose a new probabilistic approach to pulse transit time (PTT) estimation using a Gaussian distribution model. It is motivated basically by the hypothesis that PTTs normalized by RR intervals follow the Gaussian distribution. To verify the hypothesis, we demonstrate the effects of arterial compliance on the normalized PTTs using the Moens-Korteweg equation. Furthermore, we observe a Gaussian distribution of the normalized PTTs on real data. In order to estimate the PTT using the hypothesis, we first assumed that R-waves in the electrocardiogram (ECG) can be correctly identified. The R-waves limit searching ranges to detect pulse peaks in the photoplethysmogram (PPG) and to synchronize the results with cardiac beats-i.e., the peaks of the PPG are extracted within the corresponding RR interval of the ECG as pulse peak candidates. Their probabilities of being the actual pulse peak are then calculated using a Gaussian probability function. The parameters of the Gaussian function are automatically updated when a new pulse peak is identified. This update makes the probability function adaptive to variations of cardiac cycles. Finally, the pulse peak is identified as the candidate with the highest probability. The proposed approach is tested on a database where ECG and PPG waveforms are collected simultaneously during the submaximal bicycle ergometer exercise test. The results are promising, suggesting that the method provides a simple but more accurate PTT estimation in real applications.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2016-01
Language
English
Article Type
Article
Keywords

DIGITAL VOLUME PULSE; ARTERIAL WAVE-FORMS; VELOCITY; ALGORITHMS

Citation

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, v.20, no.1, pp.128 - 134

ISSN
2168-2194
DOI
10.1109/JBHI.2014.2372047
URI
http://hdl.handle.net/10203/208687
Appears in Collection
EE-Journal Papers(저널논문)
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