Publications by authors named "Chiara Caborni"

Using machine learning to combine wrist accelerometer (ACM) and electrodermal activity (EDA) has been shown effective to detect primarily and secondarily generalized tonic-clonic seizures, here termed as convulsive seizures (CS). A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm. Here we present its performance on pediatric and adult patients in epilepsy monitoring units (EMUs).

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Wearable automated seizure detection devices offer a high potential to improve seizure management, through continuous ambulatory monitoring, accurate seizure counts, and real-time alerts for prompt intervention. More importantly, these devices can be a life-saving help for people with a higher risk of sudden unexpected death in epilepsy (SUDEP), especially in case of generalized tonic-clonic seizures (GTCS). The Embrace and E4 wristbands (Empatica) are the first commercially available multimodal wristbands that were designed to sense the physiological hallmarks of ongoing GTCS: while Embrace only embeds a machine learning-based detection algorithm, both E4 and Embrace devices are equipped with motion (accelerometers, ACC) and electrodermal activity (EDA) sensors and both the devices received medical clearance (E4 from EU CE, Embrace from EU CE and US FDA).

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Objective: Common data elements (CDEs) are currently unavailable for mobile health (mHealth) in epilepsy devices and related applications. As a result, despite expansive growth of new digital services for people with epilepsy, information collected is often not interoperable or directly comparable. We aim to correct this problem through development of industry-wide standards for mHealth epilepsy data.

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Objective: New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive and automated, and provide false alarm rates (FARs) bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors.

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FreeSurfer software package automatically estimates the cerebral cortical thickness. Its use is widely accepted, albeit this tool was validated against histologic measurements in only two post-mortem isolated brain MR scans. Indeed, a comparison between histologic measurements and FreeSurfer estimation from in vivo data was never performed.

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In StereoElectroEncephaloGraphy (SEEG) procedures, intracerebral electrodes are implanted in order to identify the epileptogenic zone in drug-resistant epileptic patients. This paper presents an automatic multitrajectory planner that computes the best trajectory in terms of distance from vessels and guiding screws angle, once the candidate entry and target regions are quickly and roughly defined. The planning process is designed also to spare some brain structures, such as cella media and trigone of the lateral ventricles and brain stem.

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Background: Stereoelectroencephalography (SEEG) methodology, originally developed by Talairach and Bancaud, is progressively gaining popularity for the presurgical invasive evaluation of drug-resistant epilepsies.

Objective: To describe recent SEEG methodological implementations carried out in our center, to evaluate safety, and to analyze in vivo application accuracy in a consecutive series of 500 procedures with a total of 6496 implanted electrodes.

Methods: Four hundred nineteen procedures were performed with the traditional 2-step surgical workflow, which was modified for the subsequent 81 procedures.

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