Determining the Spike-Wave Index Using Automated Detection Software.

PURPOSE: The spike-wave index (SWI) is a key feature in the diagnosis of electrical status epilepticus during slow-wave sleep. Estimating the SWI manually is time-consuming and is subject to interrater and intrarater variability. Use of automated detection software would save time. Thereby, this software will consistently detect a certain EEG phenomenon as epileptiform and is not influenced by human factors. To determine noninferiority in calculating the SWI, we compared the performance of a commercially available spike detection algorithm (P13 software, Persyst Development Corporation, San Diego, CA) with human expert consensus. METHODS: The authors identified all prolonged EEG recordings for the diagnosis or follow-up of electrical status epilepticus during slow-wave sleep carried out from January to December 2018 at an epilepsy tertiary referral center. The SWI during the first 10 minutes of sleep was estimated by consensus of two human experts. This was compared with the SWI calculated by the automated spike detection algorithm using the three available sensitivity settings: "low," "medium," and "high." In the software, these sensitivity settings are denoted as perception values. RESULTS: Forty-eight EEG recordings from 44 individuals were analyzed. The SWIs estimated by human experts did not differ from the SWIs calculated by the automated spike detection algorithm in the "low" perception mode (P = 0.67). The SWIs calculated in the "medium" and "high" perception settings were, however, significantly higher than the human expert estimated SWIs (both P < 0.001). CONCLUSIONS: Automated spike detection (P13) is a useful tool in determining SWI, especially when using the "low" sensitivity setting. Using such automated detection tools may save time, especially when reviewing larger epochs.

Using sampled visual EEG review in combination with automated detection software at the EMU.

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 study we aimed to show non inferiority for electroclinical diagnosis using sampled review in combination with EEG analysis softreferware (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 detection software 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.

Timing of first event in inpatient long-term video-EEG monitoring for diagnostic purposes.

BACKGROUND: Long-term video-EEG monitoring (LTM) aims to record the habitual event and is a useful diagnostic tool for neurological paroxysmal clinical events. In our epilepsy monitoring unit (EMU) setting, admissions are usually planned to last up to five days. We ascertained time taken for the recording of a first event and determined correlations between different clinical characteristics and timings. METHODS: We retrospectively reviewed diagnostic and classification LTM recording performed at a tertiary epilepsy centre. RESULTS: Sixty-three recordings were reviewed. Most subjects (89%) had events at least once a week prior to admission. In 40 (63%) a habitual event was recorded, mostly (93%) within the first two days. No events were recorded on day four or five. A few characteristics were associated with a trend for events occurring earlier (events more than once a week vs less than once a week, motor symptoms compared with aura or dyscognitive events, and reduction of antiepileptic drugs versus no reduction). CONCLUSIONS: Our finding suggests that, for diagnostic event recording in people with epilepsy or PNEA, a maximum recording time of three days is sufficient in two thirds of them, if event frequency is at least once a week. In the remaining third, prolonged recording up to five days did not result in capturing a clinical event. For these individuals, shorter admission could be planned, for example for 2days rather than 5days.

Value of video monitoring for nocturnal seizure detection in a residential setting.

OBJECTIVE: Following a sudden death at a residential care unit, the Dutch Health and Care Inspectorate advised intensification of the use of video monitoring (VM) at the unit. We assessed whether VM resulted in increased identification of seizures that required clinical intervention. METHODS: The unit provides care for 340 individuals with refractory epilepsy and severe learning disabilities. Acoustic detection systems (ADSs) cover all individuals; 37 people also have a bed motion sensor (BMS) and 46 people with possible nocturnal seizures are now monitored by VM. During a 6-month period, in all cases of a suspected seizure we asked the caregivers to specify which device alerted them and to indicate whether this led to an intervention. Staff costs of VM were estimated using payroll information. RESULTS: We identified 1,208 seizures in 37 individuals: 4 had no nocturnal seizures and 393 (33%) seizures were seen only on video. In 169 (14%) of 1,208 seizures an intervention was made and this included 39 (10%) of 393 seizures seen only on video. When compared to seizures observed with an ADS or BMS, seizures seen only on video were more often tonic seizures (71% vs. 22%, p < 0.001) and occurred mostly in the beginning or at the end of the night (40% vs. 26%, p < 0.001). The extra staff costs of monitoring was 7,035 euro per seizure seen only on video and leading to an intervention. SIGNIFICANCE: VM facilitates nocturnal surveillance, but the costs are high. This underscores the need for development of reliable seizure detection devices.