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Proceedings Paper

Neural network for photoplethysmographic respiratory rate monitoring
Author(s): Anders Johansson
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Paper Abstract

The photoplethysmographic signal (PPG) includes respiratory components seen as frequency modulation of the heart rate (respiratory sinus arrhythmia, RSA), amplitude modulation of the cardiac pulse, and respiratory induced intensity variations (RIIV) in the PPG baseline. The aim of this study was to evaluate the accuracy of these components in determining respiratory rate, and to combine the components in a neural network for improved accuracy. The primary goal is to design a PPG ventilation monitoring system. PPG signals were recorded from 15 healthy subjects. From these signals, the systolic waveform, diastolic waveform, respiratory sinus arrhythmia, pulse amplitude and RIIV were extracted. By using simple algorithms, the rates of false positive and false negative detection of breaths were calculated for each of the five components in a separate analysis. Furthermore, a simple neural network (NN) was tried out in a combined pattern recognition approach. In the separate analysis, the error rates (sum of false positives and false negatives) ranged from 9.7% (pulse amplitude) to 14.5% (systolic waveform). The corresponding value of the NN analysis was 9.5-9.6%.

Paper Details

Date Published: 24 October 2001
PDF: 10 pages
Proc. SPIE 4434, Hybrid and Novel Imaging and New Optical Instrumentation for Biomedical Applications, (24 October 2001); doi: 10.1117/12.446666
Show Author Affiliations
Anders Johansson, Linkoeping Univ. (Sweden)


Published in SPIE Proceedings Vol. 4434:
Hybrid and Novel Imaging and New Optical Instrumentation for Biomedical Applications
Albert-Claude Boccara; Alexander A. Oraevsky, Editor(s)

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