Machine learning prediction of emesis and gastrointestinal state in ferrets

Although electrogastrography (EGG) could be a critical tool in the diagnosis of patients with gastrointestinal (GI) disease, it remains under-utilized. The lack of spatial and temporal resolution using current EGG methods presents a significant roadblock to more widespread usage. Human and preclinical studies have shown that GI myoelectric electrodes can record signals containing significantly more information than can be derived from abdominal surface electrodes. The current study sought to assess the efficacy of multi-electrode arrays, surgically implanted on the serosal surface of the GI tract, from gastric fundus-to-duodenum, in recording myoelectric signals. It also examines the potential for machine learning algorithms to predict functional states, such as retching and emesis, from GI signal features. Studies were performed using ferrets, a gold standard model for emesis testing. Our results include simultaneous recordings from up to six GI recording sites in both anesthetized and chronically implanted free-moving ferrets. Testing conditions to produce different gastric states included gastric distension, intragastric infusion of emetine (a prototypical emetic agent), and feeding. Despite the observed variability in GI signals, machine learning algorithms, including k-nearest neighbors and support vector machines, were able to detect the state of the stomach with high overall accuracy (>75%). The present study is the first demonstration of machine learning algorithms to detect the physiological state of the stomach and onset of retching, which could provide a methodology to diagnose GI diseases and symptoms such as nausea and vomiting.

尽管胃电图（electrogastrography, EGG）本可成为胃肠道（gastrointestinal, GI）疾病患者诊断的关键工具，但其临床应用仍未得到充分推广。当前EGG检测方法存在时空分辨率不足的缺陷，这成为制约其广泛普及的重大阻碍。人体与临床前研究均证实，胃肠道肌电电极所记录的信号所含信息量远高于腹部表面电极所能获取的信号。本研究旨在评估手术植入胃肠道浆膜表面、范围覆盖胃底至十二指肠的多电极阵列（multi-electrode arrays）记录肌电信号（myoelectric signals）的效能，同时探讨基于胃肠道信号特征，利用机器学习算法预测干呕（retching）与呕吐（emesis）等胃功能状态的潜力。本研究以雪貂作为呕吐检测的金标准模型（gold standard model）开展实验，获取了麻醉状态以及慢性植入电极的自由活动雪貂体内最多6个胃肠道记录位点的同步信号。用于诱导不同胃功能状态的实验条件包括胃扩张、胃内灌注依米丁（emetine，一种典型催吐剂）以及进食操作。尽管胃肠道信号存在显著个体差异，但包括K近邻（k-nearest neighbors）与支持向量机（support vector machines）在内的机器学习算法，仍能以超过75%的整体准确率识别胃功能状态。本研究首次证实了可通过机器学习算法检测胃生理状态与干呕发作，该成果可为恶心、呕吐等胃肠道疾病及相关症状的临床诊断提供可行的方法学路径。