Machine learning prediction of emesis and gastrointestinal state in ferrets

Abstract

Although electrogastrography (EGG) could be a critical tool in the diagnosis and treatment 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 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 (>80%). The present study is the first demonstration of machine learning algorithms to detect the physiological state of the stomach and onset of retching and could provide insight into methodologies to treat GI diseases and control symptoms such as nausea and vomiting.

Selective stimulation of the ferret abdominal vagus nerve with multi-contact nerve cuff electrodes

“…Moreover, it is not well documented but isoflurane is reported to affect gastric myoelectric activity 34 . There are also substantial physiological differences between animals, which we have observed in in gastric myoelectric activity in awake and anesthetized ferrets 35 . In this study, we quantified selectivity via CAP recordings in one set of ferrets and measured changes in GI myoelectric activity with similar stimulation parameters in a different set of animals.…”

“…In the additional three ferrets, an identical procedure was used to implant a four-contact nerve-cuff electrode around the ventral abdominal vagus nerve and four planar electrodes, each with four contacts (Micro Leads, Inc. Somerville, MA), were sutured to the ventral gastric surface and the duodenum. The locations these planar electrodes on the ventral gastric surface was similar to our prior study 35 .…”

“…We have previously shown that GI myoelectric activity can be used to identify the physiological state (normal, distended, pre-retch) of the stomach 35 . For this study, we analyzed data from a subset of those experiments to demonstrate the selective effect of VNS on GI myoelectric activity.…”

“…For every planar electrode, the waveform recorded on each of the four contacts was averaged to generate a single GI myoelectric waveform for that electrode. Analysis methods for GI myoelectric activity were adopted from our prior study 35 . Briefly, each planar-averaged GI myoelectric signal was filtered using a low-pass Butterworth filter with a 2.5 Hz (150 cpm, 4 th order) cutoff.…”

Anesthesia suppresses gastric myoelectric power in the ferret

“…There is a critical need to determine the value of these signals for clinical application and to assess stability of these measures in different testing conditions. One significant testing variable has been the common use of general anesthesia during tests of myoelectric function in preclinical, as well as human studies [e.g., [3][4][5]. Indeed, some experiments would be difficult, if not impossible, without the use of general anesthesia, which suppresses awake behavioral movements and permits surgical access to the gastrointestinal (GI) tract.…”