Combined approach for automatic and robust calculation of dominant frequency of electrogastrogram

TL;DR

This paper introduces a new combined method integrating FFT, Welch's spectral density estimation, and autocorrelation for automatic, accurate, and noise-robust detection of dominant frequency in electrogastrogram recordings, outperforming existing methods.

Contribution

The study presents a novel combined approach for dominant frequency detection in EGG that is more accurate and noise-resistant than traditional methods like FFT, autocorrelation, or Welch's alone.

Findings

The combined method significantly outperforms FFT in noisy conditions.

Optimal window length for Welch's method is N/4 of the waveform.

The approach is effective on publicly available EGG data from healthy subjects.

Abstract

We present a novel method for automatic and robust detection of dominant frequency (DF) in the electrogastrogram (EGG). Our new approach combines Fast Fourier Transform (FFT), Welch's method for spectral density estimation, and autocorrelation. The proposed combined method as well as other separate procedures were tested on a freely available dataset consisted of EGG recordings in 20 healthy individuals. DF was calculated in relation (1) to the fasting and postprandial states, (2) to the three recording locations, and (3) to the subjects' body mass index. For the estimation of algorithms performance in the presence of noise, we created a synthetic dataset by adding white Gaussian noise to the artifact-free EGG waveform in one subject. The individual algorithms and novel combined approach were evaluated in relation to the signal-to-noise ratio (SNR) in range from -40 dB to 20 dB. Our results showed that the novel combined method significantly outperformed the commonly used approach for DF calculation - FFT in noise presence when compared to the benchmark data being was manually corrected by an expert. The novel method outperformed autocorrelation and Welch's method in accuracy. Additionally, we presented a method for optimal window width selection when using Welch's spectrogram that showed that for DF detection, window length of N/4 (300 s), where N is the length of EGG waveform in samples, performed the best when compared to the benchmark data. The combined approach proved efficient for automatic and robust calculation of dominant frequency on openly available EGG dataset recorded in healthy individuals and is promising approach for DF detection.