Ambulatory monitoring is of great interest in both clinical and domestic environments. Despite the technological advances, few monitoring solutions are suitable for medical application and diagnosis. Here, we investigate the feasibility of targeting the periauricular area (ear pavilion, ear canal, and the surrounding skin areas) to implement a multimodal system that fulfills the requirements of ergonomics and minimal obstructiveness in the context of epilepsy monitoring. Six physiological signals are selected and explored for their integration in the area of interest and a proof-of-concept prototype integrating the components in a single portable device targeting the selected location is implemented. Results show mixed results where some parameters are highly reliable, and others are impractical or require customized technology to provide clinically relevant information. To enable data acquisition, storage, and processing within the Internet of Medical Things paradigms, wireless body area transceiver integration is also analyzed in terms of coverage/capacity relations, showing feasibility for such device configuration.

A B S T R A C T Background and objective: Despite advances on signal analysis and artificial intelligence, visual inspection is the gold standard in event detection on electroencephalographic recordings. This process requires much time of clinical experts on both annotating and training new experts for this same task. In scenarios where epilepsy is considered, the need for automatic tools is more prominent, as both seizures and interictal events can occur on hours-or days-long recordings. Although other solutions have already been proposed, most of them are not integrated on clinical and basic science environments due to their complexity and required specialization. Here we present a pipeline that arises from coordinated efforts between life-science researchers, clinicians and data scientists to develop an interactive and iterative workflow to train machine-learning tools for the automatic detection of electroencephalographic events in a variety of scenarios.Methods: The approach consists on a series of subsequent steps covering data loading and configuration, event annotation, model training/re-training and event detection. With slight modifications, the combination of these blocks can cope with a variety of scenarios. To illustrate the flexibility and robustness of the approach, three datasets from clinical (patients of Dravet Syndrome) and basic research environments (mice model of the same disease) were evaluated. From them, and in response to researchers' daily needs, four real world examples of interictal event detection and seizure classification tasks were selected and processed.Results: Results show that the current approach was of great aid for event annotation and model development. It was capable of creating custom machine-learning solutions for each scenario with slight adjustments on the analysis protocol, easily accessible to users without programming skills. Final annotator similarity metrics reached values above 80% on all cases of use, reaching 92.3% on interictal event detection on human recordings.Conclusions: The presented framework is easily adaptable to multiple real world scenarios and the interactive and ease-to-use approach makes it manageable to clinical and basic researches without programming skills. Nevertheless, it is conceived so data scientists can optimize it for specific scenarios, improving the knowledge transfer between these fields.

Dravet Syndrome (DS) is an encephalopathy with epilepsy associated with multiple neuropsychiatric comorbidities. In up to 90% of cases, it is caused by functional happloinsufficiency of the SCN1A gene, which encodes the alpha subunit of a voltage-dependent sodium channel (Nav1.1). Preclinical development of new targeted therapies requires accessible animal models which recapitulate the disease at the genetic and clinical levels. Here we describe that a C57BL/6 J knock-in mouse strain carrying a heterozygous, clinically relevant SCN1A mutation (A1783V) presents a full spectrum of DS manifestations. This includes 70% mortality rate during the first 8 weeks of age, reduced threshold for heat-induced seizures (4.7 degrees C lower compared with control littermates), cognitive impairment, motor disturbances, anxiety, hyperactive behavior and defects in the interaction with the environment. In contrast, sociability was relatively preserved. Electrophysiological studies showed spontaneous interictal epileptiform discharges, which increased in a temperature-dependent manner. Seizures were multifocal, with different origins within and across individuals. They showed intra/inter-hemispheric propagation and often resulted in generalized tonic-clonic seizures. F-18-labelled flourodeoxyglucose positron emission tomography (FDG-PET) revealed a global increase in glucose uptake in the brain of Scn1a(WT/A1783V) mice. We conclude that the Scn1a(WT/A1783V) model is a robust research platform for the evaluation of new therapies against DS.