Abstract
This article introduces the sliding discrete period transform (DPT), a novel algorithm designed for processing physiological signals, specifically photoplethysmogram (PPG) signals from pulse oximeters. The algorithm employs period domain analysis with sinusoidal basis functions, addressing challenges like random noise and nonstationary data. Implemented as a sliding transform in MATLAB , the DPT combines autocorrelation and ensemble averaging. Details will be provided on an algorithm developed and implemented on an Analog Devices’ MAX30101 device and compared to a Masimo oximeter with Signal Extraction Technology (SET).
Introduction
Signals of physiological origin can be corrupted by noise and motion artifacts, and often share the same pass band as the signals themselves. Biological signals are quasi-stationary and have periods and amplitudes that can change over time. Simple filtering of data is not possible with such signals. One popular way for extracting information is to use another signal, which is temporally linked to the data, acting as the timeframe for ensemble averaging. Although ensemble aver- aging has been effectively applied to oximetry signals using an external cardiac trigger from an ECG source, in many instances an ECG source may be unavailable. In this work, a successful effort was made to process signals without an ECG trig- ger, yet yielding similar results.
Initially, an algorithm was developed to conduct a form of autocorrelation and ensemble averaging. However, it was soon discovered that ensemble averaging in the time domain was unnecessary, since all pertinent information could be found in the period domain data itself. Heart rate and blood oxygen saturation could be calculated directly from the results generated by the sliding discrete period transform (DPT).
This work was started with a review of the discrete Fourier transform (DFT) since it can generate the frequency spectrum of a signal that can then be used to deter- mine its period. Another goal of the research was to sample the data with a very high degree of resolution. Obtaining high resolution with the DFT requires collecting a large number of data samples. Because biological signals are quasi- stationary, collecting a large number of samples using the DFT often results in spectral smearing. What was needed was an algorithm that had high resolution, but would only require a small number of samples compared to the DFT. Since the intention was to use the algorithm on real-time data of undetermined length, a sliding form of the transform similar to a sliding DFT was used.
Read the full article here
