Seizure forecasting with ultra long-term EEG signals.

OBJECTIVE: The apparent randomness of seizure occurrence affects greatly the quality of life of persons with epilepsy. Since seizures are often phase-locked to multidien cycles of interictal epileptiform activity, a recent forecasting scheme, exploiting RNS data, is capable of forecasting seizures days in advance. METHODS: We tested the use of a bandpass filter to capture the universal mid-term dynamics enabling both patient-specific and cross-patient forecasting. In a retrospective study, we explored the feasibility of the scheme on three long-term recordings obtained by the NeuroPace RNS System, the NeuroVista intracranial, and the UNEEG subcutaneous devices, respectively. RESULTS: Better-than-chance forecasting was observed in 15 (83 %) of 18 patients, and in 16 (89 %) patients for daily and hourly forecast, respectively. Meaningful forecast up to 30 days could be achieved in 4 (22 %) patients for hourly forecast frequency. The cross-patient performance decreased only marginally and was patient-wise strongly correlated with the patient-specific one. Comparable performance was obtained for NeuroVista and UNEEG data sets. SIGNIFICANCE: The feasibility of cross-patient forecasting supports the universal importance of mid-term dynamics for seizure forecasting, demonstrates promising inter-subject-applicability of the scheme on ultra long-term EEG recordings, and highlights its huge potential for clinical use.

Corrigendum: Abilities and Disabilities-Applying Machine Learning to Disentangle the Role of Intelligence in Diagnosing Autism Spectrum Disorders.

[This corrects the article DOI: 10.3389/fpsyt.2022.826043.].

Abilities and Disabilities-Applying Machine Learning to Disentangle the Role of Intelligence in Diagnosing Autism Spectrum Disorders.

Objective: Although autism spectrum disorder (ASD) is a relatively common, well-known but heterogeneous neuropsychiatric disorder, specific knowledge about characteristics of this heterogeneity is scarce. There is consensus that IQ contributes to this heterogeneity as well as complicates diagnostics and treatment planning. In this study, we assessed the accuracy of the Autism Diagnostic Observation Schedule (ADOS/2) in the whole and IQ-defined subsamples, and analyzed if the ADOS/2 accuracy may be increased by the application of machine learning (ML) algorithms that processed additional information including the IQ level. Methods: The study included 1,084 individuals: 440 individuals with ASD (with a mean IQ level of 3.3 ± 1.5) and 644 individuals without ASD (with a mean IQ level of 3.2 ± 1.2). We applied and analyzed Random Forest (RF) and Decision Tree (DT) to the ADOS/2 data, compared their accuracy to ADOS/2 cutoff algorithms, and examined most relevant items to distinguish between ASD and Non-ASD. In sum, we included 49 individual features, independently of the applied ADOS module. Results: In DT analyses, we observed that for the decision ASD/Non-ASD, solely one to four items are sufficient to differentiate between groups with high accuracy. In addition, in sub-cohorts of individuals with (a) below (IQ level ≥4)/ID and (b) above average intelligence (IQ level ≤ 2), the ADOS/2 cutoff showed reduced accuracy. This reduced accuracy results in (a) a three times higher risk of false-positive diagnoses or (b) a 1.7 higher risk for false-negative diagnoses; both errors could be significantly decreased by the application of the alternative ML algorithms. Conclusions: Using ML algorithms showed that a small set of ADOS/2 items could help clinicians to more accurately detect ASD in clinical practice across all IQ levels and to increase diagnostic accuracy especially in individuals with below and above average IQ level.

Coherent false seizure prediction in epilepsy, coincidence or providence?

OBJECTIVE: Seizure forecasting using machine learning is possible, but the performance is far from ideal, as indicated by many false predictions and low specificity. Here, we examine false and missing alarms of two algorithms on long-term datasets to show that the limitations are less related to classifiers or features, but rather to intrinsic changes in the data. METHODS: We evaluated two algorithms on three datasets by computing the correlation of false predictions and estimating the information transfer between both classification methods. RESULTS: For 9 out of 12 individuals both methods showed a performance better than chance. For all individuals we observed a positive correlation in predictions. For individuals with strong correlation in false predictions we were able to boost the performance of one method by excluding test samples based on the results of the second method. CONCLUSIONS: Substantially different algorithms exhibit a highly consistent performance and a strong coherency in false and missing alarms. Hence, changing the underlying hypothesis of a preictal state of fixed time length prior to each seizure to a proictal state is more helpful than further optimizing classifiers. SIGNIFICANCE: The outcome is significant for the evaluation of seizure prediction algorithms on continuous data.