Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture.
Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture.
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DOI：
10.1371/journal.pone.0289076
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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：
功能性和运动相关的胃肠道（GI）疾病影响近40%的人口。已经提出GI肌电活动的紊乱在这些疾病中起重要作用。在诊断和治疗中使用这些信号的一个重要障碍是GI肌电特征和功能之间缺乏一致的关系。该问题的一个潜在原因是使用了任意分类标准，例如胃动过速和胃动过缓频带中的功率百分比。在这里，我们使用深度神经网络架构对来自自由移动的雪貂的GI肌电信号进行自动特征提取。对于每只动物，我们在基线对照和持续1小时的喂养条件下进行记录。数据在1维残差卷积网络上进行训练，然后是完全连接的层，并基于S形输出进行决策。对于这个2类问题，准确性为90%，灵敏度（喂养检测）为90%，特异性（基线检测）为89%。相比之下，使用手工制作的特征（例如，SVM、随机森林和逻辑回归）的准确性为54%至82%，灵敏度为46%至84%，特异性为66%至80%。这些结果表明，自动特征提取和深度神经网络可能有助于评估GI功能，以将基线与活跃的功能性GI状态（如进食）进行比较。在未来的测试中，目前的方法可用于确定正常和疾病相关的GI肌电模式，以诊断和评估GI疾病患者。
Functional and motility-related gastrointestinal (GI) disorders affect nearly 40% percent of the population. Disturbances of GI myoelectric activity have been proposed to play a significant role in these disorders. A significant barrier to usage of these signals in diagnosis and treatment is the lack of consistent relationships between GI myoelectric features and function. A potential cause of this issue is the use of arbitrary classification criteria, such as percentage of power in tachygastric and bradygastric frequency bands. Here we applied automatic feature extraction using a deep neural network architecture on GI myoelectric signals from free-moving ferrets. For each animal, we recorded during baseline control and feeding conditions lasting for 1 h. Data were trained on a 1-dimensional residual convolutional network, followed by a fully connected layer, with a decision based on a sigmoidal output. For this 2-class problem, accuracy was 90%, sensitivity (feeding detection) was 90%, and specificity (baseline detection) was 89%. By comparison, approaches using hand-crafted features (e.g., SVM, random forest, and logistic regression) produced an accuracy from 54% to 82%, sensitivity from 46% to 84% and specificity from 66% to 80%. These results suggest that automatic feature extraction and deep neural networks could be useful to assess GI function for comparing baseline to an active functional GI state, such as feeding. In future testing, the current approach could be applied to determine normal and disease-related GI myoelectric patterns to diagnosis and assess patients with GI disease.
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