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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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0.3%0.5%0.8%0.1% · Sep 202519922001200920172026
4 results for “Spike-and-wave”
Spike-and-wave epileptiform discharge pattern detection based on Kendall's Tau-b coefficientstat.ML

New method detects spike-and-wave epileptiform discharges using Kendall's Tau-b.

problem Detecting spike-and-wave epileptiform discharges in EEG signals.
method Proposes a new method based on Kendall's Tau-b coefficient.
result High Specificity and rule in (SpPIn) for spike-and-wave discharge detection.
epilepsyKendall's Tau-bspike-and-wavedischarge detectionbiomedical engineering
2019-11-29abs ↗pdf ↗
Study on spike-and-wave detection in epileptic signals using t-location-scale distribution and the K-nearest neighbors classifierstat.AP

Paper proposes a k-NN classifier for detecting spike-and-wave seizures in EEG.

problem Early detection of epileptic seizures in EEG signals.
method Uses t-location-scale distribution and k-nearest neighbors classifier.
result Demonstrates improved classification accuracy, sensitivity, and specificity on real data.
epilepsyEEGk-nearest neighborst-location-scalespike-and-wave
2024-05-21abs ↗pdf ↗
A novel spike-and-wave automatic detection in EEG signalseess.SP

Novel method detects spike-and-wave patterns in EEG signals.

problem Manual classification of spike-and-wave discharges in EEG signals is time-consuming and error-prone.
method The method divides EEG signals into time segments, applies Morlet 1-D decomposition, extracts scale, variance, and median from wavelet coefficients, and uses a k-NN classifier.
result The proposed method achieved 100% accuracy in detecting spike-and-wave patterns.
EEG signalsSpike-and-waveAutomatic detectionk-NN classifierMorlet decomposition
2019-12-15abs ↗pdf ↗
Hybrid Pipeline SWD Detection in Long-Term EEG Signalseess.SP

Hybrid pipeline detects spike-and-wave discharges in long-term EEG recordings.

problem Manual identification of spike-and-wave discharges in long-term EEG recordings is labour-intensive and error-prone.
method A hybrid pipeline that combines analytical features with a shallow ANN for SWD detection.
result The method correctly detected 384 out of 392 annotated SWD events, achieving high sensitivity and specificity.
absence epilepsyEEGSWD detectionartificial neural networklong-term monitoring
2025-09-22abs ↗pdf ↗