In Vivo Validation of a Less Invasive Gastrostimulator.

Gastrointestinal stimulator implants have recently shown promising results in helping obese patients lose weight. However, to place the implant, the patient currently needs to undergo an invasive surgical procedure. We report a less invasive procedure to stimulate the stomach with a gastrostimulator. After attempting fully endoscopic implantation, we more recently focused on a single incision percutaneous procedure. In both cases, the challenges in electronic design of the implant are largely similar. This article covers the work achieved to meet these and details the in vivo validation of a gastrostimulator aimed to be endoscopically placed and anchored to the stomach.

Design and Implementation of a Less Invasive Gastrostimulator.

Gastrointestinal stimulator implants have recently shown positive results in helping obese patients lose weight. However, to place the implant, the patient currently needs to undergo an invasive surgical procedure. Our team is aiming for a less invasive procedure to stimulate the stomach with a gastrostimulator. Attempts covered fully endoscopic implantation and, more recently, we have focussed on a single incision laparoscopic procedure. Whatever the chosen implantation solution, the electronic design of the implant system shares many challenges. This paper covers the work achieved to meet these.

Silicone rubber encapsulation for an endoscopically implantable gastrostimulator.

Gastrointestinal stimulator implants have recently shown positive results in treating obesity. However, the implantation currently requires an invasive surgical procedure. Endoscopy could be used to place the gastric stimulator in the stomach, hence avoiding the riskier surgery. The implant then needs to go through the oesophagus and be located inside the stomach, which imposes new design constraints, such as miniaturization and protecting the electronic circuit against the highly acidic environment of the stomach. We propose to protect the implant by encapsulation with silicone rubber. This paper lists the advantages of this method compared to the more usual approach of a hermetic enclosure and then presents a method to evaluate the underwater adhesive stability of six adhesive/substrate couples, using repeated lap-shear tests and an elevated temperature to accelerate the ageing process. The results for different adhesive/substrate couples tested, presented on probability plots, show that FR4 and alumina substrates with MED4-4220 silicone rubber are suitable for a first implantable prototype. We then compare these with the predicted lifetimes of bonds between historical standard silicone rubber DC3140 and different substrates and describe the encapsulation of our gastrostimulator.

Cluster-based spike detection algorithm adapts to interpatient and intrapatient variation in spike morphology.

Visual quantification of interictal epileptiform activity is time consuming and requires a high level of expert's vigilance. This is especially true for overnight recordings of patient suffering from epileptic encephalopathy with continuous spike and waves during slow-wave sleep (CSWS) as they can show tens of thousands of spikes. Automatic spike detection would be attractive for this condition, but available algorithms have methodological limitations related to variation in spike morphology both between patients and within a single recording. We propose a fully automated method of interictal spike detection that adapts to interpatient and intrapatient variation in spike morphology. The algorithm works in five steps. (1) Spikes are detected using parameters suitable for highly sensitive detection. (2) Detected spikes are separated into clusters. (3) The number of clusters is automatically adjusted. (4) Centroids are used as templates for more specific spike detections, therefore adapting to the types of spike morphology. (5) Detected spikes are summed. The algorithm was evaluated on EEG samples from 20 children suffering from epilepsy with CSWS. When compared to the manual scoring of 3 EEG experts (3 records), the algorithm demonstrated similar performance since sensitivity and selectivity were 0.3% higher and 0.4% lower, respectively. The algorithm showed little difference compared to the manual scoring of another expert for the spike-and-wave index evaluation in 17 additional records (the mean absolute difference was 3.8%). This algorithm is therefore efficient for the count of interictal spikes and determination of a spike-and-wave index.

Quantification of motion artifact rejection due to active electrodes and driven-right-leg circuit in spike detection algorithms.

Identification of spikes in the electroencephalogram (EEG) plays an important role during the diagnosis of neurological disorders such as epilepsy. Automatic spike detection (ASD) is attractive because it reduces the diagnostic time and improves objectivity of the scoring. Unfortunately, automatic detection is sometimes confounded by artifacts, particularly motion artifacts which can be frequent in ambulatory recording, in the ICU, when recording from restless patients or children, etc. EEG systems have recently been improved by using active electrodes and driven-right-leg circuits (DRL) to reduce motion artifacts. However, the performances of ASD algorithms, both with unimproved and improved EEG systems, are difficult to quantify in patients because of poor reproducibility of the results. In this paper, a test set-up was used to evaluate the performance of active electrodes and DRL and assess if they can be complemented or substituted by a spike detection algorithm in avoiding motion artifact. Results show that motion artifacts can largely degrade spike detection when a traditional EEG system is used, whereas an EEG fitted with active electrodes and a DRL allows high quality detection. When using a traditional EEG, the choice of a spike detection algorithm has a large influence on detection quality.

Detection of obstructive apnea events in sleeping infants from thoracoabdominal movements.

The aim of the study was to determine whether in infants, the evaluation of thoracoabdominal movements alone, with no measurement of airflow, could be used to identify obstructive sleep apnea events (OA). Two different methods were used: first, we initially quantified thoracoabdominal asynchrony. Although 79.3% of OAs showed a significant increase of thoracoabdominal asynchrony, only 10.9% of the events scored by the identification of phase opposition were true OAs. Next, we developed two artificial neural networks (ANNs) as classifiers for the study of the thoracoabdominal signals. The first network was trained to locate obstructive and central apnea events. It correctly detected 75% of the OAs; however, only 6.2% of the detected events were true OAs. When a second network was used, OAs could not be discriminated from other portions of the signals showing similar phase characteristics. It was concluded that the information available in uncalibrated signals of thoracic and abdominal respiratory movements was insufficient to unambiguously detect OA events in sleeping infants.