Measurement of gastric oscillations from finger photoplethysmographic signal using autoregressive model

Abstract

In this article we investigate the presence of gastric motility (GM) oscillation in the lower frequency range of photoplethysmography (PP G) signal. The objective of this article is to analyze the lower frequency oscillations of PPG signal using the novel autoregressive (AR) model and studying the presence of gastric motility (GM) rhythm in finger PPG. We acquired finger PPG and GM (measured using Electrogastrogram, EGG) signals at the sampling rate of 100 Hz simultaneously from 10 healthy subjects for 30 minutes duration in fasting and postprandial states. A lower frequency oscillation (� 0.05 Hz) called slow wave was extracted from PPG signal which looks similar to the slow wave of GM in both shape and frequency in the range (0 -0.1953) Hz using discrete wavelet transform (DWT). In a previous study, we demonstrated that there is a good cross-correlation exists between slow waves on both PPG and EGG signals [Annals of Biomedical Engg. DOl: 1O.1007/s10439-01O-0113-4]. Here in this study, power spectral density (P SD) of PPG and EGG slow waves were estimated using AR spectral estimation method. Dominant frequencies of PPG and EGG slow wave were estimated and the results imply that finger PPG signal reveals GM related information.

A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition

“…4,5 Many techniques have been used by researchers for the purpose of extracting surface EGG with a clinically acceptable signal-to-noise ratio. Classical Fourier domain approaches, [6][7][8][9] like bandpass filters (BPFs) and autoregressive models, have an inherent limitation on their applicability to nonstationary EGG signals whose frequency, amplitude, and phase widely vary across measurement timings and subjects. 5 To circumvent this problem, researchers 10,11 have used wavelets to extract gastric signals amidst severe contaminations from biological artifacts and random noise.…”

Análisis espectral a través de bancos de filtros aplicado al pre-procesamiento para la umbralización de señales de pulso oximetría

“…La curva SPO2, contenida en la señal PPG, se ha consolidado como un insumo para determinar variables cardiovasculares [8] y estimar el comportamiento de la señal electrocardiográfica [9]. También se ha convertido en una herramienta para estimar otras variables fisiológicas como el ritmo respiratorio [10], [11], oscilaciones gástricas [12], eventos en resucitación cardiopulmonar [13] y variaciones en condiciones de ejercicio [14]. La curva SPO2 requiere de diversas estrategias computaciones para ser extraída de la señal PPG, como es el caso del análisis espectral [15].…”