Neural Network for Photoplethysmographic Respiratory Rate Monitoring

Overview

Journal Med Biol Eng Comput

Publisher Springer

Specialties Biomedical Engineering
Medical Informatics

Date 2003 Jun 14

PMID 12803287

Citations 17

Authors

A Johansson

Affiliations

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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 wave-form, 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).

Citing Articles

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