Neural network for photoplethysmographic respiratory rate monitoring |
| |
Authors: | Johansson A |
| |
Institution: | Department of Biomedical Engineering, Swedish National Centre of Excellence for Non-invasive Medical Measurements (NIMED), Link?pings Universitet, Link?ping, Sweden. andjo@imt.liu.se |
| |
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). |
| |
Keywords: | Photoplethysmography Optical sensors Pulse oximetry Respiratory rate Ventilation monitoring Neural networks |
本文献已被 PubMed SpringerLink 等数据库收录! |
|