Sudden unexpected death in epilepsy, periictal physiology, and the SUDEP-7 Inventory.

Sudden unexpected death in epilepsy (SUDEP) is a significant public health burden. Epidemiological studies have identified clinical SUDEP risk factors across large populations, but the means to apply this information to individual patients are lacking. The SUDEP-7 Inventory was developed as a marker of clinical SUDEP risk and has been used in studies of potential SUDEP biomarkers. We retrospectively reviewed clinical data from 16 patients dying of SUDEP and 48 matched living epilepsy controls to determine whether individuals succumbing to SUDEP could be distinguished from living epilepsy controls using the revised SUDEP-7 Inventory, the absolute number of clinical risk factors as identified by an International League Against Epilepsy meta-analysis (ILAE score), and physiological characteristics previously associated with SUDEP risk. Mean revised SUDEP-7 Inventory score was 3.3 ± 2.0 in SUDEP cases and 3.8 ± 2.3 in controls (P = 0.39). Mean ILAE score was 2.4 ± 1.1 in SUDEP cases and 2.6 ± 1.4 in controls (P = 0.62). There were no significant differences in interictal heart rate variability (measured by the root mean square of differences of successive RR intervals), periictal cardiorespiratory dysfunction, and postictal generalized electroencephalographic suppression between the groups. This demonstrates that a reliable instrument for individual SUDEP risk stratification is lacking and highlights the need for improved understanding of SUDEP pathophysiology and individual risk determination.

Sleep-disordered breathing, neuroendocrine function, and clinical SUDEP risk in patients with epilepsy.

INTRODUCTION: Sudden unexpected death in epilepsy (SUDEP) is a major contributor to epilepsy-related mortality. It is associated with nocturnal seizures and centrally mediated postictal cardiorespiratory dysfunction (CRD), but mechanisms and contributors remain poorly understood. METHODS: We performed a prospective, cross-sectional, observational pilot study in the Columbia University Medical Center (CUMC) adult epilepsy monitoring unit (EMU) to explore relationships between periictal CRD, sleep-disordered breathing (SDB), neuroendocrine function, and clinical SUDEP risk. Thirty patients (twenty women, ten men) underwent video-electroencephalogram (EEG) with electrocardiogram (EKG) and digital pulse oximetry, inpatient or outpatient polysomnography (PSG), and comprehensive laboratory evaluation of sex steroid hormones. Sudden unexpected death in epilepsy risk was defined as Low (0-2) or High (≥3) using the revised SUDEP-7 Inventory. Sleep-disordered breathing was defined using standard criteria. Neuroendocrine dysfunction was defined as ≥1 laboratory abnormality. RESULTS: Cardiorespiratory dysfunction occurred more frequently in high-risk patients (60% vs. 27%, p = 0.018). Endocrine dysfunction was seen in 35% of patients, more in men (p = 0.018). Sleep-disordered breathing was found in 88% of fully scoreable PSGs. CONCLUSIONS: There was no significant relationship between CRD, SDB, and neuroendocrine status, though all PSGs in those with high SUDEP risk or neuroendocrine dysfunction revealed SDB. Larger studies are needed to further elucidate relationships between CRD, SDB, neuroendocrine factors, and SUDEP.

Forensic Inspection of Sensitive User Data and Artifacts from Smartwatch Wearable Devices.

Wearable devices allow users the ability to leave mobile phones behind while remaining connected to the digital world; however, this creates challenges in the examination, acquisition, identification, and analysis of probative data. This preliminary research aims to provide an enhanced understanding of where sensitive user data and forensic artifacts are stored on smartwatch wearable devices, both through utilization as a connected and standalone device. It also provides a methodology for the forensically sound acquisition of data from a standalone smartwatch wearable device. The results identify significant amounts of data on the Samsung™ Gear S3 Frontier, greater than that stored on the companion mobile phone. An Apple Watch® Series 3 manual examination method which produces native screenshots was identified; however, the companion mobile phone was found to store the greatest amount of data. As a result of this research, a data extraction tool for the Samsung™ Gear S3 Frontier was created.