Objective: Automated detection of spikes and seizures has been a subject of research for several decades now. There have been important advances, yet automated detection in EMU (Epilepsy Monitoring... Show moreObjective: Automated detection of spikes and seizures has been a subject of research for several decades now. There have been important advances, yet automated detection in EMU (Epilepsy Monitoring Unit) settings has not been accepted as standard practice. We intend to implement this software at our EMU and so carried out a qualitative study to identify factors that hinder ('barriers') and facilitate ('enablers') implementation.Method: Twenty-two semi-structured interviews were conducted with 14 technicians and neurologists involved in recording and reporting EEGs and eight neurologists who receive EEG reports in the outpatient department. The study was reported according to the Consolidated Criteria for Reporting Qualitative Studies (COREQ).Results: We identified 14 barriers and 14 enablers for future implementation. Most barriers were reported by technicians. The most prominent barrier was lack of trust in the software, especially regarding seizure detection and false positive results. Additionally, technicians feared losing their EEG review skills or their jobs. Most commonly reported enablers included potential efficiency in the EEG workflow, the opportunity for quantification of EEG findings and the willingness to try the software. Conclusions: This study provides insight into the perspectives of users and offers recommendations for implementing automated spike and seizure detection in EMUs. Show less
Purpose: We assessed whether automated detection software, combined with live observation, enabled reliable seizure detection using three commercial software packages: Persyst, Encevis and BESA.... Show morePurpose: We assessed whether automated detection software, combined with live observation, enabled reliable seizure detection using three commercial software packages: Persyst, Encevis and BESA. Methods: Two hundred and eighty-six prolonged EEG records of individuals aged 16-86 years, collected between August 2019 and January 2020, were retrospectively processed using all three packages. The reference standard included all seizures mentioned in the clinical report supplemented with true detections made by the software and not previously detected by clinical physiologists. Sensitivity was measured for offline review by clinical physiologists and software seizure detection, both in combination with live monitoring in an EMU setting, for all three software packages at record and seizure level. Results: The database contained 249 seizures in 64 records. The sensitivity of seizure detection was 98% for Encevis and Persyst, and 95% for BESA, when a positive results was defined as detection at least one of the seizures occurring within an individual record. When positivity was defined as recognition of all seizures, sensitivity was 93% for Persyst, 88% for Encevis and 84% for BESA. Clinical physiologists' review had a sensitivity of 100% at record level and 98% at seizure level. The median false positive rate per record was 1.7 for Persyst, 2.4 for BESA and 5.5 for Encevis per 24 h. Conclusion: Automated seizure detection software does not perform as well as technicians do. However, it can be used in an EMU setting when the user is aware of its weaknesses. This assessment gives future users helpful insight into these strengths and weaknesses. The Persyst software performs best. Show less
Purpose: We assessed three commercial automated spike detection software packages (Persyst, Encevis and BESA) to see which had the best performance. Methods: Thirty prolonged EEG records from... Show morePurpose: We assessed three commercial automated spike detection software packages (Persyst, Encevis and BESA) to see which had the best performance. Methods: Thirty prolonged EEG records from people aged at least 16 years were collected and 30-minute representative epochs were selected. Interictal epileptiform discharges (IEDs) were marked by three human experts and by all three software packages. For each 30-minutes selection and for each 10-second epoch we measured whether or not IEDs had occurred. We defined the gold standard as the combined detections of the experts. Kappa scores, sensitivity and specificity were estimated for each software package. Results: Sensitivity for Persyst in the default setting was 95% for 30-minute selections and 82% for 10-second epochs. Sensitivity for Encevis was 86% (30-minute selections) and 61% (10-second epochs). The specificity for both packages was 88% for 30-minute selections and 96%-99% for the 10-second epochs. Interrater agreement between Persyst and Encevis and the experts was similar than between experts (0.67-0.83 versus 0.63-0.67). Sensitivity for BESA was 40% and specificity 100%. Interrater agreement (0.25) was low. Conclusions: IED detection by the Persyst automated software is better than the Encevis and BESA packages, and similar to human review, when reviewing 30-minute selections and 10-second epochs. This findings may help prospective users choose a software package. Show less
Purpose: The spike–wave index (SWI) is a key feature in thediagnosis of electrical status epilepticus during slow-wave sleep.Estimating the SWI manually is time-consuming and is subject... Show morePurpose: The spike–wave index (SWI) is a key feature in thediagnosis of electrical status epilepticus during slow-wave sleep.Estimating the SWI manually is time-consuming and is subject tointerrater and intrarater variability. Use of automated detectionsoftware would save time. Thereby, this software willconsistently detect a certain EEG phenomenon as epileptiformand is not influenced by human factors. To determinenoninferiority in calculating the SWI, we compared theperformance of a commercially available spike detectionalgorithm (P13 software, Persyst Development Corporation,San Diego, CA) with human expert consensus.Methods: The authors identified all prolonged EEG recordingsfor the diagnosis or follow-up of electrical status epilepticusduring slow-wave sleep carried out from January to December2018 at an epilepsy tertiary referral center. The SWI during thefirst 10 minutes of sleep was estimated by consensus of twohuman experts. This was compared with the SWI calculated bythe automated spike detection algorithm using the threeavailable sensitivity settings: “low,” “medium,” and “high.” In thesoftware, these sensitivity settings are denoted as perceptionvalues.Results: Forty-eight EEG recordings from 44 individuals wereanalyzed. The SWIs estimated by human experts did not differfrom the SWIs calculated by the automated spike detectionalgorithm in the “low” perception mode (P ¼ 0.67). The SWIscalculated in the “medium” and “high” perception settings were,however, significantly higher than the human expert estimatedSWIs (both P , 0.001).Conclusions: Automated spike detection (P13) is a useful tool indetermining SWI, especially when using the “low” sensitivitysetting. Using such automated detection tools may save time,especially when reviewing larger epochs. Show less
Purpose: Complete visual review of prolonged video-EEG recordings at an EMU (Epilepsy Monitoring Unit) is time consuming and can cause problems in times of paucity of educated personnel. In this... Show morePurpose: Complete visual review of prolonged video-EEG recordings at an EMU (Epilepsy Monitoring Unit) is time consuming and can cause problems in times of paucity of educated personnel. In this study we aimed to show non inferiority for electroclinical diagnosis using sampled review in combination with EEG analysissoftware (P13 software, Persyst Corporation), in comparison to complete visual review.Method: Fifty prolonged video-EEG recordings in adults were prospectively evaluated using sampled visual EEG review in combination with automated detection software of the complete EEG record. Visually assessed samples consisted of one hour during wakefulness, one hour during sleep, half an hour of wakefulness after wake-up and all clinical events marked by the individual and/or nurses. The final electro-clinical diagnosis of this new review approach was compared with the electro-clinical diagnosis after complete visual review as presently used.Results: The electro-clinical diagnosis based on sampled visual review combined with automated detectionsoftware did not differ from the diagnosis based on complete visual review. Furthermore, the detection software was able to detect all records containing epileptiform abnormalities and epileptic seizures.Conclusion: Sampled visual review in combination with automated detection using Persyst 13 is non-inferior to complete visual review for electroclinical diagnosis of prolonged video-EEG at an EMU setting, which makes this approach promising. Show less
Lende, M. van der; Cox, F.M.E.; Visser, G.H.; Sander, J.W.; Thijs, R.D. 2016
Sporadische inclusion body myositis (IBM) is een van de meest voor voorkomende verworven spierziekte die ontstaat na het 50e levensjaar. In dit proefschrift worden de klinische aspecten van... Show moreSporadische inclusion body myositis (IBM) is een van de meest voor voorkomende verworven spierziekte die ontstaat na het 50e levensjaar. In dit proefschrift worden de klinische aspecten van sporadische IBM beschreven. Uit de studie met betrekking tot het natuurlijk beloop blijkt dat de ziekte niet levensverkortend is, maar dat de doodsoorzaken bij sporadische IBM wel verschillen ten opzichte van een voor de leeftijd gecorrigeerde populatie. De aard en frequentie van slikstoornissen worden beschreven, alsmede de potentiele betrokkenheid van het hart. Door middel van MRI's van skeletspieren is een voor sporadische IBM specifiek patroon van afwijkingen beschreven. Ten slotte is aangetoond dat TREX1 mutaties geen rol spelen in het ontstaan van de ziekte. Show less
