Open AccessCuffless Blood Prediction with Fingertip Pulse Wave
Author(s) -
Weize Song,
Sun Xiao-yan,
Chuhan Hu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1544/1/012137
Subject(s) - waveform , hilbert–huang transform , pulse wave , signal (programming language) , pulse (music) , pulse wave analysis , preprocessor , computer science , acoustics , blood pressure , pattern recognition (psychology) , artificial intelligence , physics , medicine , telecommunications , radar , white noise , detector , jitter , programming language
Cuffless method for blood pressure measurement is an important methods for continuous health status monitoring. A pulse wave is a periodic time-series signal that reflects a non-linear, non-stationary change in the pulse signal over time. Traditional ways of pulse wave based blood pressure assessment rely on feature extraction from pulse signals, which are usually signal quality dependent and lack of consistence among studies. In this paper, a method of blood pressure measurement of using continuous pulse waveform and long-term and short-term memory network is proposed, which avoids the process of manually extracting waveform features. Experiments were performed with both pulse wave signals and the arterial blood pressure signals form the MIMIC database. Empirical mode decomposition was applied for signal preprocessing, and the time series of the pulse wave was analyzed to establish a Long Short-Term Memory neural network for blood pressure assessment. An average prediction accuracy of 83.2% was achieved.