Automated spike and seizure detection: Are we ready for implementation?

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 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.

Automated spike detection: Which software package?

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 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.

Automated seizure detection in an EMU setting: Are software packages ready for implementation?

PURPOSE: 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.

[A man with the combination of dry and wet beriberi]. / Een man met de combinatie van droge en natte beriberi.

A 34-year-old alcoholic man had neurological and cardiac symptoms. The patient was admitted to the hospital for acute painful sensory disturbances and severe weakness of the feet. Neurological and electrophysiological investigation revealed axonal sensorimotor polyneuropathy that was most prominent in the legs. Cardiac assessment showed signs and symptoms of heart failure due to a high-output state. Blood analysis showed a low thiamine concentration of 58 nmol/l (lower reference limit: 80). Therefore, a diagnosis of combined wet beriberi with cardiomyopathy and dry beriberi with axonal polyneuropathy was made. The treatment of beriberi is simple and effective and consists of thiamine supplementation in conjunction with diuretic treatment. With this approach, the patient recovered fully. Patients with beriberi have a good prognosis, particularly when the diagnosis is made at an early stage.