Dyadic aggregated autoregressive (DASAR) model for time-frequency representation of biomedical signals

05/13/2021
by   Marco A. Pinto-Orellana, et al.
0

This paper introduces a new time-frequency representation method for biomedical signals: the dyadic aggregated autoregressive (DASAR) model. Signals, such as electroencephalograms (EEGs) and functional near-infrared spectroscopy (fNIRS), exhibit physiological information through time-evolving spectrum components at specific frequency intervals: 0-50 Hz (EEG) or 0-150 mHz (fNIRS). Spectrotemporal features in signals are conventionally estimated using short-time Fourier transform (STFT) and wavelet transform (WT). However, both methods may not offer the most robust or compact representation despite their widespread use in biomedical contexts. The presented method, DASAR, improves precise frequency identification and tracking of interpretable frequency components with a parsimonious set of parameters. DASAR achieves these characteristics by assuming that the biomedical time-varying spectrum comprises several independent stochastic oscillators with (piecewise) time-varying frequencies. Local stationarity can be assumed within dyadic subdivisions of the recordings, while the stochastic oscillators can be modeled with an aggregation of second-order autoregressive models (ASAR). DASAR can provide a more accurate representation of the (highly contrasted) EEG and fNIRS frequency ranges by increasing the estimation accuracy in user-defined spectrum region of interest (SROI). A mental arithmetic experiment on a hybrid EEG-fNIRS was conducted to assess the efficiency of the method. Our proposed technique, STFT, and WT were applied on both biomedical signals to discover potential oscillators that improve the discrimination between the task condition and its baseline. The results show that DASAR provided the highest spectrum differentiation and it was the only method that could identify Mayer waves as narrow-band artifacts at 97.4-97.5 mHz.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

01/07/2013

Time-Frequency Representation of Microseismic Signals using the Synchrosqueezing Transform

Resonance frequencies can provide useful information on the deformation ...
12/29/2018

Adaptive Short-time Fourier Transform and Synchrosqueezing Transform for Non-stationary Signal Separation

The synchrosqueezing transform, a kind of reassignment method, aims to s...
05/13/2021

The Complex-Pole Filter Representation (COFRE) for spectral modeling of fNIRS signals

The complex-pole frequency representation (COFRE) is introduced in this ...
11/27/2018

Recycling cardiogenic artifacts in impedance pneumography

Purpose: We want to capture as much information as possible from biomedi...
05/06/2020

Similarity and delay between two non-narrow-band time signals

Correlation coefficient is usually used to measure the correlation degre...
10/04/2020

A Separation Method for Multicomponent Nonstationary Signals with Crossover Instantaneous Frequencies

In nature and engineering world, the acquired signals are usually affect...
04/30/2019

Estimating the Frequency of a Clustered Signal

We consider the problem of locating a signal whose frequencies are "off ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.