Health AI Central
Peer-Reviewed Publication
Epilepsy Res2024;208107474.December 1, 2024Journal Article

Necessary for seizure forecasting outcome metrics: Seizure frequency and benchmark model.

Chi-Yuan Chang1, Boyu Zhang2, Robert Moss3, Rosalind Picard4, M Brandon Westover5, Daniel Goldenholz6
1Harvard Medical School, Boston MA, United States; Beth Israel Deaconess Medical Center, Boston, MA, United States. Electronic address: cchang10@bidmc.harvard.edu.
2Massachusetts Institute of Technology, Cambridge, MA, United States; Empatica USA, Cambridge, MA, United States; Brigham and Women's Hospital, Boston, MA, United States. Electronic address: boyuz@media.mit.edu.
3Seizure Tracker LLC, Springfield, VA, United States. Electronic address: rob@seizuretracker.com.
4Massachusetts Institute of Technology, Cambridge, MA, United States; Empatica USA, Cambridge, MA, United States. Electronic address: picard@media.mit.edu.
5Harvard Medical School, Boston MA, United States; Beth Israel Deaconess Medical Center, Boston, MA, United States. Electronic address: bwestove@bidmc.harvard.edu.
6Harvard Medical School, Boston MA, United States; Beth Israel Deaconess Medical Center, Boston, MA, United States. Electronic address: daniel.goldenholz@bidmc.harvard.edu.
PubMed PMC DOI PDF

Abstract

BACKGROUND: This study aims to illustrate the connection between seizure frequency (SF) and performance metrics in seizure forecasting, and to compare the effectiveness of a moving average (MA) model versus the commonly used permutation benchmark. METHODS: Metrics of calibration and discrimination were computed for each dataset, comparing MA and permutation performance across SF values. Three dat…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.