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PurposeExisting automated seizure detection algorithms report sensitivities between 43% and 77% and specificities between 56% and 90%. The algorithms suffer from false alarms when applied to neonatal EEG because of the high degree of nurse handling and rhythmic patting used to soothe neonates. Computer vision technology that quantifies movement in real time could distinguish artifactual motion and improve automated neonatal seizure detection algorithms.MethodsThe authors used video EEG recordings from 43 neonates undergoing monitoring for seizures as part of the NEOLEV2 clinical trial. The Persyst neonatal automated seizure detection algorithm ran in real time during study EEG acquisitions. Computer vision algorithms were applied to extract detailed accounts of artifactual movement of the neonate or people near the neonate though dense optical flow estimation.ResultsUsing the methods mentioned above, 197 periods of patting activity were identified and quantified, of which 45 generated false-positive automated seizure detection events. A binary patting detection algorithm was trained with a subset of 470 event videos. This supervised detection algorithm was applied to a testing subset of 187 event videos with 8 false-positive events, which resulted in a 24% reduction in false-positive automated seizure detections and a 50% reduction in false-positive events caused by neonatal care patting, while maintaining 11 of 12 true-positive seizure detection events.ConclusionsThis work presents a novel approach to improving automated seizure detection algorithms used during neonatal video EEG monitoring. This artifact detection mechanism can improve the ability of a seizure detector algorithm to distinguish between artifact and true seizure activity.

Objective: The aim of this study is to evaluate an improved seizure detection algorithm and to compare with two other algorithms and human experts.Methods: 672 seizures from 426 epilepsy patients were examined with the (new) Reveal algorithm which utilizes 3 methods, novel in their application to seizure detection: Matching Pursuit, small neural network-rules and a new connected-object hierarchical clustering algorithm.Results: Reveal had a sensitivity of 76% with a false positive rate of 0.11/h. Two other algorithms (Sensa and CNet) were tested and had sensitivities of 35.4 and 48.2% and false positive rates of 0.11/h and 0.75/h, respectively.Conclusions: This study validates the Reveal algorithm, and shows it to compare favorably with other methods.Significance: Improved seizure detection can improve patient care in both the epilepsy monitoring unit and the intensive care unit. [Copyright &y& Elsevier]

Objective: The description and application of a new, overlap-integral comparison method and the quantification of human vs. human accuracies that can be used as goals for algorithms.Methods: Four human experts marked ten 8 h electroencephalography (EEG) records from seizure patients. The seizures varied in origin and type, including complex partial, generalized absence, secondarily generalized and primary generalized tonic–clonic. The traditional any-overlap comparison method is used in addition to the overlap-integral method, which is sensitive to the correct placement of the seizure endpoints.Results: The number of events marked by each reader ranged from 57 to 77. The average any-overlap sensitivity and false positives per hour rate are 0.92 and 0.117. The average overlap-integral correlation, sensitivity and specificity are 0.80, 0.82 and 0.9926. As expected, the correspondence between readers is high, but confounding issues resulted in overlap-integral sensitivities less than 0.5 for 10% of the records. Seven percent of the any-overlap sensitivities are less than 0.5. A comparison of the methods by record shows that the overlap-integral specificity and the any-overlap false positive rate measure different features.Conclusions: There was little variation between readers and they were essentially interchangeable. High seizure rate (many per hour), short seizure durations (<10 s) and long seizure durations (∼10 min) with ambiguous offsets can complicate the analysis and result in poor correlation. There may be any number of unmarked events in rigorously marked records and it may be preferable to use records from non-epilepsy patients to compute the false positive rate. The any-overlap and overlap-integral comparison methods are complementary.Significance: Correlation between expert human readers can be low on some records, which will complicate testing of seizure detection algorithms. [Copyright &y& Elsevier]