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PURPOSE: Existing 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. METHODS: The 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. RESULTS: Using 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. CONCLUSIONS: This 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.

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Introduction: Continuous neurologic assessment in the pediatric intensive care unit is challenging. Current electroencephalography (EEG) guidelines support monitoring status epilepticus, vasospasm detection, and cardiac arrest prognostication, but the scope of brain dysfunction in critically ill patients is larger. We explore quantitative EEG in pediatric intensive care unit patients with neurologic emergencies to identify quantitative EEG changes preceding clinical detection. Methods: From 2017 to 2020, we identified pediatric intensive care unit patients at a single quaternary children's hospital with EEG recording near or during acute neurologic deterioration. Quantitative EEG analysis was performed using Persyst P14 (Persyst Development Corporation). Included features were fast Fourier transform, asymmetry, and rhythmicity spectrograms, "from-baseline" patient-specific versions of the above features, and quantitative suppression ratio. Timing of quantitative EEG changes was determined by expert review and prespecified quantitative EEG alert thresholds. Clinical detection of neurologic deterioration was defined pre hoc and determined through electronic medical record documentation of examination change or intervention. Results: Ten patients were identified, age 23 months to 27 years, and 50% were female. Of 10 patients, 6 died, 1 had new morbidity, and 3 had good recovery; the most common cause of death was cerebral edema and herniation. The fastest changes were on "from-baseline" fast Fourier transform spectrograms, whereas persistent changes on asymmetry spectrograms and suppression ratio were most associated with morbidity and mortality. Median time from first quantitative EEG change to clinical detection was 332 minutes (interquartile range: 201-456 minutes). Conclusion: Quantitative EEG is potentially useful in earlier detection of neurologic deterioration in critically ill pediatric intensive care unit patients. Further work is required to quantify the predictive value, measure improvement in outcome, and automate the process.

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PURPOSE: To compare the seizure detection performance of three expert humans and two computer algorithms in a large set of epilepsy monitoring unit EEG recordings. METHODS: One hundred twenty prolonged EEGs, 100 containing clinically reported EEG-evident seizures, were evaluated. Seizures were marked by the experts and algorithms. Pairwise sensitivity and false-positive rates were calculated for each human-human and algorithm-human pair. Differences in human pairwise performance were calculated and compared with the range of algorithm versus human performance differences as a type of statistical modified Turing test. RESULTS: A total of 411 individual seizure events were marked by the experts in 2,805 hours of EEG. Mean, pairwise human sensitivities and false-positive rates were 84.9%, 73.7%, and 72.5%, and 1.0, 0.4, and 1.0/day, respectively. Only the Persyst 14 algorithm was comparable with humans-78.2% and 1.0/day. Evaluation of pairwise differences in sensitivity and false-positive rate demonstrated that Persyst 14 met statistical noninferiority criteria compared with the expert humans. CONCLUSIONS: Evaluating typical prolonged EEG recordings, human experts had a modest level of agreement in seizure marking and low false-positive rates. The Persyst 14 algorithm was statistically noninferior to the humans. For the first time, a seizure detection algorithm and human experts performed similarly.

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PURPOSE: Our objective was to use semiautomatic methods for calculating the spike-wave index (SWI) in electrical status epilepticus in slow-wave sleep (ESES) and to determine whether this calculation is noninferior to human experts (HEs). METHODS: Each HE marked identical 300-second epochs for all spikes and calculated the SWI in sleep EEGs of patients diagnosed with ESES. Persyst 13 was used to mark spikes (high sensitivity setting) in the same 300-second epochs marked by HEs. The spike-wave index was calculated. Pairwise HE differences and pairwise Persyst 13 (P13)-HE differences for the SWI were calculated. Bootstrap resampling (BCa, N = 3,000) was performed to better estimate mean differences and their 95% confidence bounds between HE and P13-HE pairs. Potential noninferiority of P13 to HEs was tested by comparing the 95% confidence bounds of the mean differences between pairs for the SWI. RESULTS: Twenty EEG records were analyzed. Each HE marked 100 minutes of EEG. HEs 1, 2, 3, and 4 marked 10,075, 8,635, 9,710, and 9,898 spikes, respectively. The highest and lowest 95% confidence bound of the mean difference in the SWI between HE pairs was: High: 10.3%; Low: -10.2%. Highest and lowest 95% confidence bound of the mean difference in the SWI between P13 and HE pairings was as follows: high, 9.5% and low, -6.7%. The lack of a difference between P13 and HEs supports that the algorithm is not inferior to HEs. CONCLUSIONS: Persyst 13 is noninferior to HEs in calculating the SWI in ESES, thus suggesting that an automated approach to SWI calculation may be a useful clinical tool.

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OBJECTIVE: Compare the spike detection performance of three skilled humans and three computer algorithms. METHODS: 40 prolonged EEGs, 35 containing reported spikes, were evaluated. Spikes and sharp waves were marked by the humans and algorithms. Pairwise sensitivity and false positive rates were calculated for each human-human and algorithm-human pair. Differences in human pairwise performance were calculated and compared to the range of algorithm versus human performance differences as a type of statistical Turing test. RESULTS: 5474 individual spike events were marked by the humans. Mean, pairwise human sensitivities and false positive rates were 40.0%, 42.1%, and 51.5%, and 0.80, 0.97, and 1.99/min. Only the Persyst 13 (P13) algorithm was comparable to humans - 43.9% and 1.65/min. Evaluation of pairwise differences in sensitivity and false positive rate demonstrated that P13 met statistical noninferiority criteria compared to the humans. CONCLUSION: Humans had only a fair level of agreement in spike marking. The P13 algorithm was statistically noninferior to the humans. SIGNIFICANCE: This was the first time that a spike detection algorithm and humans performed similarly. The performance comparison methodology utilized here is generally applicable to problems in which skilled human performance is the desired standard and no external gold standard exists.

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OBJECTIVE: To evaluate an automated seizure detection (ASD) algorithm in EEGs with periodic and other challenging patterns. METHODS: Selected EEGs recorded in patients over 1year old were classified into four groups: A. Periodic lateralized epileptiform discharges (PLEDs) with intermixed electrical seizures. B. PLEDs without seizures. C. Electrical seizures and no PLEDs. D. No PLEDs or seizures. Recordings were analyzed by the Persyst P12 software, and compared to the raw EEG, interpreted by two experienced neurophysiologists; Positive percent agreement (PPA) and false-positive rates/hour (FPR) were calculated. RESULTS: We assessed 98 recordings (Group A=21 patients; B=29, C=17, D=31). Total duration was 82.7h (median: 1h); containing 268 seizures. The software detected 204 (=76.1%) seizures; all ictal events were captured in 29/38 (76.3%) patients; in only in 3 (7.7%) no seizures were detected. Median PPA was 100% (range 0-100; interquartile range 50-100), and the median FPR 0/h (range 0-75.8; interquartile range 0-4.5); however, lower performances were seen in the groups containing periodic discharges. CONCLUSION: This analysis provides data regarding the yield of the ASD in a particularly difficult subset of EEG recordings, showing that periodic discharges may bias the results. SIGNIFICANCE: Ongoing refinements in this technique might enhance its utility and lead to a more extensive application.

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In the analysis of epileptic electroencephalographic (EEG) and magnetoencephalography (MEG) data, spike separation is diagnostically important because localization of epileptic focus often depends on accurate extraction of spiky activity from the raw data. In this paper, we present a method to automatically extract spikes using the wavelet transform combined with morphological filtering based on a circular structuring element. Our experimental results have shown that this method is highly effective in spike separation. Comparisons with the wavelet, bandpass filter, empirical mode decomposition (EMD), and independent component analysis (ICA) methods show that the new method is more effective in estimating both spike amplitudes and locations.

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Continuous EEG monitoring (CEEG) is a powerful tool for evaluating cerebral function in obtunded and comatose critically ill patients. The ongoing analysis of CEEG data is a major task because of the volume of data generated during monitoring and the need for near real-time interpretation of a patient's EEG patterns. Advances in digital EEG data acquisition, computer processing, data transmission, and data display have made CEEG monitoring in the intensive care unit technically feasible. A variety of quantitative EEG tools such as Fourier analysis and amplitude-integrated EEG, and other methods of data analysis such as computerized seizure detection, increasingly allow for focused review of EEG epochs of potential interest. These tools reduce the tremendous time burdens that accompany analysis of the complete CEEG data stream, and allow bedside personnel and nonexpert staff to potentially recognize significant EEG changes in a timely fashion. This article uses literature review and clinical case examples to illustrate techniques for the display and analysis of intensive care unit CEEG recordings. Areas requiring further research and development are discussed.

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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.

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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 (approximately 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.

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PURPOSE: Determining the existence of syndrome-specific genetic factors in epilepsy is essential for phenotype definition in genetic linkage studies, and informs research on basic mechanisms. Analysis of concordance of epilepsy syndromes in families has been used to assess shared versus distinct genetic influences on generalized epilepsy (GE) and localization-related epilepsy (LRE). However, it is unclear how the results should be interpreted in relation to specific genetic hypotheses. METHODS: To assess evidence for distinct genetic influences on GE and LRE, we examined concordance of GE and LRE in 63 families containing multiple individuals with idiopathic or cryptogenic epilepsy, drawn from the Epilepsy Family Study of Columbia University. To control for the number of concordant families expected by chance, we used a permutation test to compare the observed number with the number expected from the distribution of individuals with GE and LRE in the study families. RESULTS: Of the families, 62% were concordant for epilepsy type, and 38% were discordant. In all analyses, the proportion of concordant families was significantly greater than expected. CONCLUSIONS: This suggests that some genetic influences predispose specifically to either GE or LRE. Because of the ascertainment bias resulting from the selection of families containing multiple individuals with epilepsy, we could not test whether there are also shared genetic influences on these two epilepsy subtypes. Population-based studies will be needed to explore these results further.

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Diffusion-weighted imaging (DWI) is sensitive for the detection of acute ischemic stroke. However, a negative DWI study of the brain does not always exclude a patient from the possibility of acute cerebral ischemia. The authors report 1 case in which the patient presented with a fixed ischemic neurological deficit (National Institute of Health Stroke Scale score = 22) that included global aphasia, right hemiparesis, and a right visual field neglect. The initial DWI of the brain within 27 hours of symptom onset was negative. The deficit persisted, and a repeat magnetic resonance imaging study 7 days later showed a large area of restricted diffusion involving the gray matter of the entire left middle cerebral artery and anterior cerebral artery distribution, indicating a large area of cortical stroke.

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Continuous EEG (CEEG) monitoring allows uninterrupted assessment of cerebral cortical activity with good spatial resolution and excellent temporal resolution. Thus, this procedure provides a means of constantly assessing brain function in critically ill obtunded and comatose patients. Recent advances in digital EEG acquisition, storage, quantitative analysis, and transmission have made CEEG monitoring in the intensive care unit (ICU) technically feasible and useful. This article summarizes the indications and methodology of CEEG monitoring in the ICU, and discusses the role of some quantitative EEG analysis techniques in near real-time remote observation of CEEG recordings. Clinical examples of CEEG use, including monitoring of status epilepticus, assessment of ongoing therapy for treatment of seizures in critically ill patients, and monitoring for cerebral ischemia, are presented. Areas requiring further development of CEEG monitoring techniques and indications are discussed.

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