Quantifying the effects of vagus nerve stimulation on gastric myoelectric activity in ferrets using an interpretable machine learning approach.
Quantifying the effects of vagus nerve stimulation on gastric myoelectric activity in ferrets using an interpretable machine learning approach.
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DOI：
10.1371/journal.pone.0295297
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发表时间：
2023
期刊：
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
迷走神经刺激（VNS）是胃肠道（GI）疾病的潜在治疗选择。本研究旨在了解 VNS 对胃肠道（GI）功能的生理影响，这对于开发针对胃肠道疾病的更有效的适应性闭环 VNS 疗法至关重要。我们的研究中采用了胃电图（EGG），它测量胃电活动（GEA）作为量化胃肠道功能的指标。我们引入了一种记录模式，使我们能够同时诱导电 VNS 和记录 EGG。虽然这种设置创建了一个独特的模型来研究 VNS 对胃肠道功能的影响，并为设计先进的神经调节疗法提供了一个优秀的测试平台，但所得数据存在噪音、异质性，并且需要专门的分析工具。目前的研究旨在制定一种系统且可解释的方法，通过使用信号处理和机器学习技术来量化电 VNS 对雪貂 GEA 的生理影响。我们的分析流程包括预处理步骤、时域和频域特征提取、用于选择特征的投票算法以及模型训练和验证。我们的结果表明，VNS 引起的电生理变化可以通过针对每个分类场景的一组不同特征进行最佳表征。此外，我们的研究结果表明，特征选择的过程增强了分类性能并促进了表示学习。
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.
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DOI：
10.3390/s22228616
发表时间：
2022-11-08
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Jakus G;Sodnik J;Miljković N
通讯作者：
Miljković N
DOI：
10.1016/j.bpa.2005.07.009
发表时间：
2006-03-01
影响因子：
4.8
作者：
Bein, Berthold
通讯作者：
Bein, Berthold
影响因子：
5.1
作者：
Genuer, Robin;Poggi, Jean-Michel;Tuleau-Malot, Christine
通讯作者：
Tuleau-Malot, Christine
DOI：
10.3390/s21020573
发表时间：
2021-01-15
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Peri E;Xu L;Ciccarelli C;Vandenbussche NL;Xu H;Long X;Overeem S;van Dijk JP;Mischi M
通讯作者：
Mischi M
影响因子：
5.5
作者：
GRUNDY, D;SCRATCHERD, T
通讯作者：
SCRATCHERD, T