Running title: Wearable multimodal motor seizure detectors Onorati et al. 2 Summary Objective:New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive, automated and provide false alarm rates bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors. Methods:Hand-annotated video-electroencephalography seizure events were collected from 69 patients at 6 clinical sites. Three different wristbands were used to record electrodermal activity (EDA) and accelerometer (ACM) signals, obtaining 5,928 hours of data, including 55 convulsive epileptic seizures (6 focal tonic-clonic seizures and 49 focal-to-bilateral-tonicclonic seizures) from 22 patients. Recordings were analyzed off-line to train and test two new machine learning classifiers and a published EDA and ACM-based classifier. Moreover, wristband data were analyzed to estimate seizure-motion duration and autonomic responses. Results:The two novel classifiers consistently outperformed the previous detector. The most efficient (Classifier III) yielded sensitivity of 94.55%, and false alarm rate (FAR) of 0.2 events/day.No nocturnal seizures were missed. Most patients had less than 1 false alarm every 4 days with FAR below their seizure frequency. When increasing the sensitivity to 100% (no missed seizures) the FAR is up to 13 times lower than the previous detector. Furthermore, all detections occurred before the seizure ended, providing reasonable latency (median: 29.3 s, range: 14.8-151 s). Automatically estimated seizure durations were correlated with true durations, enabling reliable annotations. Finally, EDA measurements confirmed the presence of post-ictal autonomic dysfunction, exhibiting a significant rise in 73% of the convulsive seizures. Onorati et al. 3 Significance:The proposed multimodal wrist-worn convulsive seizure detectors provide seizure counts that are more accurate than previous automated detectors and typical patient self-reports, while maintaining a tolerable FAR for ambulatory monitoring. Furthermore, the multimodal system provides an objective description of motor behaviour and autonomic dysfunction, aimed at enriching seizure characterization, with potential utility for SUDEP warning.
BackgroundClinical evidence in coronary surgery is usually derived from retrospective, single institutional series. This may introduce significant biases in the analysis of critical issues in the treatment of these patients. In order to avoid such methodological limitations, we planned a European multicenter, prospective study on coronary artery bypass grafting, the E-CABG registry.DesignThe E-CABG registry is a multicenter study and its data are prospectively collected from 13 centers of cardiac surgery in university and community hospitals located in six European countries (England, Italy, Finland, France, Germany, Sweden). Data on major and minor immediate postoperative adverse events will be collected. Data on late all-cause mortality, stroke, myocardial infarction and repeat revascularization will be collected during a 10-year follow-up period. These investigators provided a score from 0 to 10 for any major postoperative adverse events and their rounded medians were used to stratify the severity of these complications in four grades. The sum of these scores for each complication/intervention occurring after coronary artery bypass grafting will be used as an additive score for further stratification of the prognostic importance of these events.DiscussionThe E-CABG registry is expected to provide valuable data for identification of risk factors and treatment strategies associated with suboptimal outcome. These information may improve the safety and durability of coronary artery bypass grafting. The proposed classification of postoperative complications may become a valuable research tool to stratify the impact of such complications on the outcome of these patients and evaluate the burden of resources needed for their treatment.Clinical Trials numberNCT02319083
Female sex is a risk factor for mortality after aortic valve replacement, for major vascular complications after TAVI, and for transfusions after both approaches.
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). The aim of this contribution is to provide updated evidence of the effectiveness of GTCS detection and monitoring relying on the combination of ACM and EDA sensors.A machine learning algorithm able to recognize ACC and EDA signatures of GTCS-like events has been developed on E4 data, labeled using gold-standard video-EEG examined by epileptologists in clinical centers, and has undergone continuous improvement. While keeping an elevated sensitivity to GTCS (92-100%), algorithm improvements and growing data availability led to lower false alarm rate (FAR) from the initial ~2 down to 0.2-1 false alarms per day, as showed by retrospective and prospective analyses in inpatient settings. Algorithm adjustment to better discriminate real-life physical activities from GTCS, has brought the initial FAR of ~6 on outpatient real life settings, down to values comparable to best-case clinical settings (FAR<0.5), with comparable sensitivity. Moreover, using multimodal sensing, it has been possible not only to detect GTCS but also to quantify seizure-induced autonomic dysfunction, based on automatic features of abnormal motion and EDA.The latter biosignal correlates with the duration of post-ictal generalized EEG suppression, a biomarker observed in 100% of monitored SUDEP cases.
Special Article responsible in the event of any contradiction, discrepancy and/or ambiguity between the EACTS, EACTA and EBCP Guidelines and any other official recommendations or guidelines issued by the relevant public health authorities, in particular in relation to good use of healthcare or therapeutic strategies. Health professionals are encouraged to take the EACTS, EACTA and EBCP Guidelines fully into account when exercising their clinical judgement as well as in the determination and the implementation of preventive, diagnostic or therapeutic medical strategies; however, the EACTS, EACTA and EBCP Guidelines do not, in any way whatsoever, override the individual responsibility of health professionals to make appropriate and accurate decisions in consideration of each patient's health condition and, where appropriate and/or necessary, in consultation with that patient and the patient's care provider. Nor do the EACTS, EACTA and EBCP Guidelines exempt health professionals from giving full and careful consideration to the relevant official, updated recommendations or guidelines issued by the competent public health authorities, in order to manage each patient's case in light of the scientifically accepted data pursuant to their respective ethical and professional obligations. It is also the health professional's responsibility to verify the applicable rules and regulations relating to drugs and medical devices at the time of prescription. The article has been co-published with permission in the British Journal of Anaesthesia, the European Journal of Cardio-Thoracic Surgery and the Interactive CardioVascular and Thoracic Surgery.
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