Nocturnal Epileptic Seizures Detection Using Inertial and Muscular Sensors
Bibliographic record
Abstract
This paper presents a lightweight approach for the early detection of nocturnal epileptic seizures through analysis of inertial data and muscle contractions. Our approach uses an overlapping sliding window to derive the variance of data acquired by the MPU 9,250 motion tracking device and single channel surface ElectroMyoGram (sEMG). The Exponentially Weighted Moving Average (EWMA) is used to forecast the current value of the data variance. When the Kullback-Leibler divergence between the forecasted and measured variances deviates from past values, a signal is transmitted to the base station to set the current counter in an alarm window. If the filling ratio of the alarm window is greater than a predefined threshold, an alarm is triggered by the base station. The proposed approach is intended to improve the performance of existing detection systems based on data analysis from Accelerometer. The MPU 9,250 is 9-axis motion tracking and used to detect motor seizures, and it contains a 3-axis Accelerometer, Gyroscope, and Magnetometer. The sEMG is used to detect silent seizures without jerky movements. Our experimental results on a real dataset from an epileptic patient show that our proposed approach is able to increase detection accuracy and reduce the low false alarm rate. Comparison with a Probability Density Function (PDF) further demonstrates the detection efficiency of our approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".