A Comparison of Signal Processing Techniques for the Extraction of Breathing Rate from the Photoplethysmogram

The photoplethysmogram (PPG) is the pulsatile wave- form produced by the pulse oximeter, which is widely used for monitoring arterial oxygen saturation in patients. Various methods for extracting the breathing rate from the PPG waveform have been compared using a consistent data set, and a novel technique using autoregressive modelling is presented. This novel technique is shown to outperform the existing techniques, with a mean error in breathing rate of 0.04 breaths per minute. ULSE oximetry is frequently used in clinical situations for non-invasive measurement of heart rate and arterial oxygen saturation. It has been suggested that signal processing techniques can be used to extract the breathing rate from the photoplethysmogram (PPG), which is the pulsatile waveform produced by a pulse oximeter at one of its two wavelengths (red and infra-red). If this is possible, it would allow non- invasive measurement of breathing rate using a device (the pulse oximeter) that is already used in many clinical situations, and is known to cause a minimum of distress or inconvenience to the patient. A number of methods for deriving the breathing rate from the PPG have been suggested in the literature. Results from the assessment of these methods using a consistent data set are presented in this paper, allowing comparisons between the methods to be made. A new method using autoregressive (AR) modelling has also been developed, and is shown to perform better than the existing techniques. II. MATERIALS AND METHODS Seven records from the MIMIC database in the Physiobank archive (1) were identified for use in assessing the accuracy of the algorithms for the extraction of breathing rate. These records all contain both the PPG waveform, and a synchronous respiratory waveform (believed to be obtained by nasal ther- mistry) for use as a reference. Two five-minute sections from each of the records were identified for use in the tests, resulting in a dataset consisting of fourteen five-minute sections from seven individuals. Sections were identified by looking for the

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