. 2023 Dec 1;18(12):e0295297.
doi: 10.1371/journal.pone.0295297.
eCollection 2023.
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Abstract
Vagus nerve stimulation (VNS) is a potential treatment option for gastrointestinal (GI) diseases. The present study aimed to understand the physiological effects of VNS on gastrointestinal (GI) function, which is crucial for developing more effective adaptive closed-loop VNS therapies for GI diseases. Electrogastrography (EGG), which measures gastric electrical activities (GEAs) as a proxy to quantify GI functions, was employed in our investigation. We introduced a recording schema that allowed us to simultaneously induce electrical VNS and record EGG. While this setup created a unique model for studying the effects of VNS on the GI function and provided an excellent testbed for designing advanced neuromodulation therapies, the resulting data was noisy, heterogeneous, and required specialized analysis tools. The current study aimed at formulating a systematic and interpretable approach to quantify the physiological effects of electrical VNS on GEAs in ferrets by using signal processing and machine learning techniques. Our analysis pipeline included pre-processing steps, feature extraction from both time and frequency domains, a voting algorithm for selecting features, and model training and validation. Our results indicated that the electrophysiological changes induced by VNS were optimally characterized by a distinct set of features for each classification scenario. Additionally, our findings demonstrated that the process of feature selection enhanced classification performance and facilitated representation learning.
Copyright: © 2023 Zeydabadinezhad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Conflict of interest statement
No authors have competing interests.
Figures
Fig 1
Correlation heatmap of the engineered features (See Table 1) a) baseline vs. VNS at 10 Hz b) baseline vs. VNS at 30 Hz. Each cell’s color shows to what extent features are correlated.
Fig 2. Selected features via our feature selection algorithm for baseline vs. VNS at 10 Hz (first scenario) and organized in a descending sequence of significance, as established by the Random Forest classifier.
Error bars represent one standard deviation. ’*’ is used to show the time domain features. SmEn: Sample Entropy, RMS: Root Mean Square, MVN: (Entropy of) Multi-variate Normal, SVDEn: Singular Value Decomposition Entropy, LL: Line Length, DP: Dominant Power.
Fig 3. This illustration compares the AUC for a Random Forest classifier trained using various feature sets.
From left to right: Features from Fig 2, features selected for VNS at 30 Hz, all features, a random selection of features equivalent in number to those in Fig 2, and randomly shuffled labels. The comparison is conducted in the context of the baseline versus VNS at 10 Hz scenario.
Fig 4. Selected features via our feature selection algorithm for baseline vs. VNS at 30 Hz (second scenario) and organized in a descending sequence of significance, as established by the Random Forest classifier.
Error bars represent one standard deviation. ’*’ is used to show the time domain features. PFD: Petrosian Fractal Dimension, PrEn: Permutation Entropy, DF: Dominant Frequency, ApEn: Approximate Entropy, DP: Dominant Power, BP2: Band Power in 8–11 cpm, MF: Median Frequency, LZ: Lempel-Ziv Complexity, SmEn: Sample Entropy, SpEn: Spectral Entropy.
Fig 5. This illustration compares the AUC for a Random Forest classifier trained using various feature sets.
From left to right: Features from Fig 4, features selected for VNS at 10 Hz, all features, a random selection of features equivalent in number to those in Fig 4, and randomly shuffled labels. The comparison is conducted in the context of the baseline versus VNS at 30 Hz scenario.
Fig 6. Electrode placements.
a) show the structure of nerve cuff, b) position of surface electrodes (only ‘gastric 2’ was used in this study) and nerve cuff. c) A ferret’s head connection to the vagus nerve stimulator and EGG recording device.
Fig 7. The analytic pipeline for EGG analysis and biomarker identification.
Fig 8. Feature selection algorithm.
References
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Alvarez WC. The electrogastrogram and what it shows. JAMA: The Journal of the American Medical Association. 1922; doi: 10.1001/jama.1922.02640680020008
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Grants and funding
This work was supported by the National Institutes of Health grants OT2OD030535 (BM) and U18TR002205 (CH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
