A novel approach to automatic seizure detection using computer vision and independent component analysis.

OBJECTIVE: Epilepsy is a neurological disease that affects ~50 million people worldwide, 30% of which have refractory epilepsy and recurring seizures, which may contribute to higher anxiety levels and poorer quality of life. Seizure detection may contribute to addressing some of the challenges associated with this condition, by providing information to health professionals regarding seizure frequency, type, and/or location in the brain, thereby improving diagnostic accuracy and medication adjustment, and alerting caregivers or emergency services of dangerous seizure episodes. The main focus of this work was the development of an accurate video-based seizure-detection method that ensured unobtrusiveness and privacy preservation, and provided novel approaches to reduce confounds and increase reliability. METHODS: The proposed approach is a video-based seizure-detection method based on optical flow, principal component analysis, independent component analysis, and machine learning classification. This method was tested on a set of 21 tonic-clonic seizure videos (5-30 min each, total of 4 h and 36 min of recordings) from 12 patients using leave-one-subject-out cross-validation. RESULTS: High accuracy levels were observed, namely a sensitivity and specificity of 99.06% ± 1.65% at the equal error rate and an average latency of 37.45 ± 1.31 s. When compared to annotations by health care professionals, the beginning and ending of seizures was detected with an average offset of 9.69 ± 0.97 s. SIGNIFICANCE: The video-based seizure-detection method described herein is highly accurate. Moreover, it is intrinsically privacy preserving, due to the use of optical flow motion quantification. In addition, given our novel independence-based approach, this method is robust to different lighting conditions, partial occlusions of the patient, and other movements in the video frame, thereby setting the base for accurate and unobtrusive seizure detection.

Frequency of post-stroke electroencephalographic epileptiform activity - a systematic review and meta-analysis of observational studies.

INTRODUCTION: Cerebrovascular diseases are the most frequent risk factor for epilepsy in the elderly, and epileptic phenomenon following stroke is known to worsen the prognosis. Although electroencephalography is the gold standard epilepsy biomarker, it is rarely used in post-stroke studies, and the frequency of post-stroke epileptiform activity is still uncertain. PATIENTS AND METHODS: We analysed studies indexed to MEDLINE, Embase, Web of Science, PsycINFO and OpenGrey (up to March 2015), reporting post-stroke electroencephalographic epileptiform activity frequency in adults. Epileptiform activity was classified as ictal (electrographic seizures) and interictal (non-periodic spikes and sharp waves). Data selection, extraction and appraisal were done in duplicate. Random-effects meta-analysis was used to pool frequencies. RESULTS: The pooled frequency of post-stroke ictal and interictal epileptiform activity was 7% (95% CI 3%-12%) and 8% (95% CI 4%-13%), respectively. The use of continuous electroencephalogram was not associated with an increased frequency of electrographic seizures (p = 0.05), nor did the management setting (Intensive Care Unit versus non- Intensive Care Unit, p = 0.31). However, studies with continuous electroencephalogram showed a higher frequency of interictal epileptiform activity (p = 0.01). DISCUSSION: This study provides the best available estimates of the frequency of post-stroke electroencephalographic epileptiform activity. Due to detection bias, it was not possible to correlate clinical and electrographic seizures. CONCLUSION: The frequency of ictal and interictal epileptiform activity in the electroencephalogram was comparable with previous frequency analyses of clinical seizures. The frequency of ictal epileptiform activity did not change with continuous record or clinical setting, while the frequency of interictal epileptiform activity increased with continuous record.