首页|Drexel University Researcher Provides New Study Findings on Machine Learning (Au tomated Seizure Detection Based on State-Space Model Identification)
Drexel University Researcher Provides New Study Findings on Machine Learning (Au tomated Seizure Detection Based on State-Space Model Identification)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting from Philadelphia, Pennsylv ania, by NewsRx journalists, research stated, "In this study, we developed a mac hine learning model for automated seizure detection using system identification techniques on EEG recordings." The news journalists obtained a quote from the research from Drexel University: "System identification builds mathematical models from a time series signal and uses a small number of parameters to represent the entirety of time domain signa l epochs. Such parameters were used as features for the classifiers in our study . We analyzed 69 seizure and 55 non-seizure recordings and an additional 10 cont inuous recordings from Thomas Jefferson University Hospital, alongside a larger dataset from the CHB-MIT database. By dividing EEGs into epochs (1 s, 2 s, 5 s, and 10 s) and employing fifth-order state-space dynamic systems for feature extr action, we tested various classifiers, with the decision tree and 1 s epochs ach ieving the highest performance: 96.0% accuracy, 92.7% sensitivity, and 97.6% specificity based on the Jefferson dataset. Moreover, as the epoch length increased, the accuracy dropped to 94.9% , with a decrease in sensitivity to 91.5% and specificity to 96.7% . Accuracy for the CHB-MIT dataset was 94.1%, with 87.6 % sensitivity and 97.5% specificity."
Drexel UniversityPhiladelphiaPennsyl vaniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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