Nocturnal Epilepsy in Adults

The Usefulness of Nap Sleep Recording During Routine Electroencephalography: An Audit Study

OBJECTIVES

A measure to increase the electroencephalogram (EEG) outcome includes a short period of nap sleep during a routine standard EEG with the aim of increasing its sensitivity to interictal abnormalities or provoking seizures. As part of an ongoing auditing of our EEG data, we aimed to investigate the contribution of nap sleep during routine outpatient department based EEGs requested for a variety of reasons.

METHODS

EEG data at the Department of Clinical Physiology at Sultan Qaboos University Hospital, Oman, from July 2006 to December 2007 and from January 2009 to December 2010 (total 42 months) were reviewed. The EEGs were for patients older than 13-years referred for possible epilepsy, blackouts, headache, head trauma, and other non-specified attacks. The recording period was between 20 to 40 minutes. Abnormalities were identified during waking and nap sleep periods.

RESULTS

A total of 2 547 EEGs were reviewed and 744 were abnormal (29.2%). Of those abnormal EEGs, nap sleep was obtained in 258 (34.7%) EEGs, and 39 (15.1%) showed abnormalities during nap sleep. Nineteen out of the 39 (48.7%) EEGs were abnormal during awake and nap sleep; and 20 (51.3%) were abnormal during nap sleep, which represented only 2.7% of the total abnormal EEGs (n = 744).

CONCLUSIONS

The contribution of the short nap sleep to the pickup rate of interictal abnormalities in EEG was minimal. We recommend the EEG service to include one cycle of spontaneous sleep EEG directed at patients with a history suggestive of epilepsy if their awake EEGs are normal.

Long-term home monitoring of hypermotor seizures by patient-worn accelerometers

Long-term home monitoring of epileptic seizures is not feasible with the gold standard of video/electro-encephalography (EEG) monitoring. The authors developed a system and algorithm for nocturnal hypermotor seizure detection in pediatric patients based on an accelerometer (ACM) attached to extremities. Seizure detection is done using normal movement data, meaning that the system can be installed in a new patient's room immediately as prior knowledge on the patient's seizures is not needed for the patient-specific model. In this study, the authors compared video/EEG-based seizure detection with ACM data in seven patients and found a sensitivity of 95.71% and a positive predictive value of 57.84%. The authors focused on hypermotor seizures given the availability of this seizure type in the data, the typical occurrence of these seizures during sleep, i.e., when the measurements were done, and the importance of detection of hypermotor seizures given their often refractory nature and the possible serious consequences. To our knowledge, it is the first detection system focusing on this type of seizure in pediatric patients.