Electrocardiogram (ECG)-based seizure detection using supervised machine-learning.

Background: We conducted a pilot study utilizing automatic delineation of electrocardiogram (ECG) and machine learning that considered all components of the ECG complex for seizure detection. The primary outcome was to assess the feasibility of this method. The secondary outcome was to identify the most effective machine learning algorithm.

Heart rate variability-based detection of epileptic seizures: Machine learning analysis and characterization of discriminant metrics.

Objective: We aimed to determine the most suitable cardiac metrics and machine learning algorithms (MLa) for an electrocardiography-based seizure detection device.

Methods: In a multicenter, prospective study of adult inpatients, with limited physical activity, 24-hour video-electroencephalogram recordings including ≥ 1 seizure were analyzed. Heart Rate (HR) and Heart Rate Variability (HRV) metrics were calculated continuously from the corresponding electrocardiogram.

Specialized Large Language Model Outperforms Neurologists at Complex Diagnosis in Blinded Case-Based Evaluation.

: Artificial intelligence (AI), particularly large language models (LLMs), has demonstrated versatility in various applications but faces challenges in specialized domains like neurology. This study evaluates a specialized LLM's capability and trustworthiness in complex neurological diagnosis, comparing its performance to neurologists in simulated clinical settings. : We deployed GPT-4 Turbo (OpenAI, San Francisco, CA, US) through Neura (Sciense, New York, NY, US), an AI infrastructure with a dual-database architecture integrating "long-term memory" and "short-term memory" components on a curated neurological corpus.

Non-convulsive status epilepticus associated with Whipple's disease.

A 69-year-old woman developed non-convulsive status epilepticus during inpatient investigation for abdominal pain. Initial detailed investigations did not identify the cause of seizures, but a jejunal biopsy and PCR testing in various fluids led to the diagnosis of Whipple's disease with neurological involvement. The seizures were controlled but she subsequently had moderate cognitive impairment.

Assessment of an under-mattress sensor as a seizure detection tool in an adult epilepsy monitoring unit.

Objective: Because of SUDEP (Sudden and unexpected death in epilepsy) and other direct consequences of generalized tonic-clonic seizures, the use of efficient seizure detection tool may be helpful for patients, relatives and caregivers. We aimed to evaluate an under-mattress detection tool (EMFIT®) in real-life hospital conditions, in particular its sensitivity and false alarm rate (FAR), as well as its impact on patient care.

Methods: We carried out a retrospective study on a cohort of patients with epilepsy admitted between September 2017 and June 2021 to Amiens University Hospital for a video-EEG of at least 24 h, during which at least one epileptic seizure was recorded.

A pain like no other: From cluster to headache to Eagle's syndrome, a case report.

Background: Eagle's syndrome, also called stylohyoid complex syndrome, is a rare syndrome pathology characterised by latero-cervical pain radiating to the face, linked to an abnormal enlargement of the styloid or calcification of the stylo-hyoid ligament.

Case: We report here the case of a young man of 25 suffering from cluster headache resistant to treatments, revealing Eagle's syndrome.

Conclusion: Only surgery led to a real improvement of his condition.