NDLI: Neural network for photoplethysmographic respiratory rate monitoring

Content Provider	Springer Nature Link
Author	Johansson, A.
Copyright Year	2003
Abstract	The reflection mode photoplethysmographic (PPG) signal was studied with the aim of determining respiratory rate. The PPG signal includes respiratory synchronous components, seen as frequency modulation of the heart rate (respiratory sinus arrhythmia), amplitude modulation of the cardiac pulse and respiratory-induced intensity variations (RIIVs) in the PPG baseline. PPG signals were recorded from the foreheads of 15 healthy subjects. From these signals, the systolic wavefrm diastolic waveform, respiratory sinus arrhythmia, pulse amplitude and RIIVs were extracted. Using basic algorithms, the rates of false positive and false negative detection of breaths were calculated separately for each of the five components. Furthermore, a neural network was assessed in a combined pattern recognition approach. The error rates (sum of false positive and false negative breath detections) for the basic algorithms ranged from 9.7% (pulse amplitude) to 14.5% (systolic waveform). The corresponding values for the neural network analysis were 9.5–9.6%. These results suggest the use of a combined PPG system for simultaneous monitoring of respiratory rate and arterial oxygen saturation (pulse oximetry).
Starting Page	242
Ending Page	248
Page Count	7
File Format	PDF
ISSN	01400118
Journal	Medical and Biological Engineering and Computing
Volume Number	41
Issue Number	3
e-ISSN	17410444
Language	English
Publisher	Springer-Verlag
Publisher Date	2003-01-01
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Photoplethysmography Optical sensors Pulse oximetry Respiratory rate Ventilation monitoring Neural networks Human Physiology Computer Applications Neurosciences Imaging Radiology Biomedical Engineering
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering Computer Science Applications

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