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automated seizure detection algorithm  (Data Sciences International)

 
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    Data Sciences International automated seizure detection algorithm
    Automated Seizure Detection Algorithm, supplied by Data Sciences International, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/automated seizure detection algorithm/product/Data Sciences International
    Average 90 stars, based on 1 article reviews
    automated seizure detection algorithm - by Bioz Stars, 2026-03
    90/100 stars

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    Image Search Results


    Complete subject demographics and results. Subjects in light green are those that had focal-onset seizures during their EMU stay. “Naive Review” results are consensus epileptologist review of the REMI montage without algorithm-detection annotations. “Review with CDSS” results are consensus epileptologist review with algorithm-detection annotations. “Algorithm” results are for the REMI automated seizure detection algorithm. TP, true positive; FN, false negative; FP, false positive; FDR, false detection rate; BL TC, bilateral tonic-clonic.

    Journal: Frontiers in Neurology

    Article Title: Wearable Reduced-Channel EEG System for Remote Seizure Monitoring

    doi: 10.3389/fneur.2021.728484

    Figure Lengend Snippet: Complete subject demographics and results. Subjects in light green are those that had focal-onset seizures during their EMU stay. “Naive Review” results are consensus epileptologist review of the REMI montage without algorithm-detection annotations. “Review with CDSS” results are consensus epileptologist review with algorithm-detection annotations. “Algorithm” results are for the REMI automated seizure detection algorithm. TP, true positive; FN, false negative; FP, false positive; FDR, false detection rate; BL TC, bilateral tonic-clonic.

    Article Snippet: Epitel's automated seizure detection algorithm was able to detect 21 of 24 known focal-onset seizures (out-of-sample), providing a mean sensitivity of 90% across all subjects (100% for 8 out of 10 subjects, —Algorithm, —filled black diamond; ).

    Techniques:

    Percent of overlap between known seizures and true positive events marked by reviewers and the automated algorithm.

    Journal: Frontiers in Neurology

    Article Title: Wearable Reduced-Channel EEG System for Remote Seizure Monitoring

    doi: 10.3389/fneur.2021.728484

    Figure Lengend Snippet: Percent of overlap between known seizures and true positive events marked by reviewers and the automated algorithm.

    Article Snippet: Epitel's automated seizure detection algorithm was able to detect 21 of 24 known focal-onset seizures (out-of-sample), providing a mean sensitivity of 90% across all subjects (100% for 8 out of 10 subjects, —Algorithm, —filled black diamond; ).

    Techniques:

    Sensitivity vs. false detection rate. The sensitivity (%) and false detection rate (FDR—FP/h) are shown for the individual reviewers [green x (R1), square (R2), and circle (R3)], the consensus review (blue star), and the automated algorithm (black diamond). The unfilled markers denote the naive epileptologist review and the filled markers denote review with automated algorithm annotations as clinical decision support software (CDSS). Reviewers R1 (green x), R3 (green circle), and the consensus (blue star) showed improved sensitivity with CDSS at the expense of a higher FDR, while reviewer R2 (green square) had a reduced sensitivity and FDR with CDSS. The REMI automated seizure detection algorithm (black diamond) had a higher sensitivity than any one reviewer or consensus, with a higher false detection rate. The error bars are 95% confidence intervals using a bias-corrected and accelerated method (BCa, N = 1,000).

    Journal: Frontiers in Neurology

    Article Title: Wearable Reduced-Channel EEG System for Remote Seizure Monitoring

    doi: 10.3389/fneur.2021.728484

    Figure Lengend Snippet: Sensitivity vs. false detection rate. The sensitivity (%) and false detection rate (FDR—FP/h) are shown for the individual reviewers [green x (R1), square (R2), and circle (R3)], the consensus review (blue star), and the automated algorithm (black diamond). The unfilled markers denote the naive epileptologist review and the filled markers denote review with automated algorithm annotations as clinical decision support software (CDSS). Reviewers R1 (green x), R3 (green circle), and the consensus (blue star) showed improved sensitivity with CDSS at the expense of a higher FDR, while reviewer R2 (green square) had a reduced sensitivity and FDR with CDSS. The REMI automated seizure detection algorithm (black diamond) had a higher sensitivity than any one reviewer or consensus, with a higher false detection rate. The error bars are 95% confidence intervals using a bias-corrected and accelerated method (BCa, N = 1,000).

    Article Snippet: Epitel's automated seizure detection algorithm was able to detect 21 of 24 known focal-onset seizures (out-of-sample), providing a mean sensitivity of 90% across all subjects (100% for 8 out of 10 subjects, —Algorithm, —filled black diamond; ).

    Techniques: Software