XTRACTIS for Precision Medicine

EEG Signal-Based Detection of Epileptic Seizures

Benchmark vs. Logistic Regression, Random Forests, Boosted Trees & Neural Networks

Design an AI-based decision-making system that accurately makes a rational and explainable medical diagnosis of the epileptic seizure from the patient’s electroencephalogram (EEG) processed signal.

Goals & Benefits

  1. Identify the specific EEG signal parameters significantly characterizing each epileptic seizure and enhance medical knowledge by helping neurologists understand the cause-and-effect relationships between these parameters and the presence of an epileptic condition.
  2. Help the medical profession to make earlier and more personalized decisions through rapid, systematic, and explainable diagnoses.
  3. Avoid many false alarms thanks to transparent and accurate diagnosis.

XTRACTIS-induced Decision System

Intelligible Model, Explainable Decisions
  • The top-model is a decision system composed of 15 rules without chaining.
  • Each rule uses from 2 to 7 predictors among the 8 variables that XTRACTIS automatically identified as significant in the decision process (out of the 24 Potential Predictors).
  • Only a few rules are triggered at a time to compute the decision

It has a good Real Performance (on unknown data).

It computes real-time predictions up to 70,000 decisions/second, offline or online (API).

LoR = Logistic Regression RFo=Random Forests
BT=Boosted Trees
NN=Neural Networks

Detailed results and explanations in full document

Use Case 2025/06 (v2.0)

Powered by XTRACTIS® REVEAL v13.2.52889 (2024/10)

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CONTENTS

  1. Problem Definition
  2. XTRACTIS-induced Decision System
  3. XTRACTIS Process
  4. Top-Model Induction
  5. Explained Predictions for 3 unkown cases
  6. Top-Models Benchmark
  7. Quantitative Metrics