arXiv:2605.22858v1 Announce Type: cross Abstract: Diagnosing epilepsy is challenging when routine EEGs lack interictal epileptiform discharges (IEDs). Intermittent photic stimulation (IPS) and hyperventilation (HV) can increase diagnostic yield, but their interpretation is subjective. We propose a reproducible pipeline that classifies EEG recordings acquired during stimulation procedures, using machine-learning features spanning temporal, spectral, wavelet, and connectivity domains, and a stacked ensemble to combine complementary feature sets. Performance is evaluated with leave-one-subject-ou
The increasing availability of advanced machine learning techniques and medical datasets allows for more sophisticated analysis of complex biosignals like EEG for diagnostic purposes.
This development can significantly improve the accuracy and objectivity of epilepsy diagnosis, reducing diagnostic delays and improving patient outcomes.
Epilepsy diagnosis, particularly in challenging cases, could become more automated, standardized, and less reliant on subjective interpretation.
- · Epilepsy patients
- · Healthcare providers
- · Medical AI companies
- · EEG device manufacturers
- · Traditional subjective EEG interpreters
Improved early diagnosis of epilepsy leads to more timely and effective treatment.
Reduced healthcare costs associated with misdiagnosis or delayed treatment of epilepsy.
Enhanced AI diagnostic tools could set a precedent for similar ML applications in other neurological disorders, accelerating medical AI adoption.
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Read at arXiv cs.LG