Wavelet Scattering Transform for Interpretable Schizophrenia Biomarker Discovery and Classification from Resting-State EEG
arXiv:2607.05282v1 Announce Type: cross Abstract: Schizophrenia is a debilitating neuropsychiatric disorder characterized by profound cortical network dysregulation, for which objective, clinically translatable EEG based biomarkers remain underdeveloped. Existing automated classification pipelines rely predominantly on static power spectral density features inherently blind to amplitude modulation dynamics and cross-frequency coupling, phenomena central to schizophrenia pathophysiology, while adopting epoch level cross validation strategies that introduce temporal data leakage, artificially in
Advances in AI and signal processing techniques are enabling more sophisticated analysis of complex biological data, pushing the boundaries of biomarker discovery for neurological disorders.
This development represents a significant step towards objective, interpretable, and clinically translatable diagnostics for schizophrenia, moving beyond static power spectral density features to dynamic brain activity.
The ability to accurately identify amplitude modulation dynamics and cross-frequency coupling in EEG signals could lead to earlier and more precise diagnosis and personalized treatment approaches for schizophrenia.
- · Neuroscience researchers
- · Pharmaceutical companies developing neurological treatments
- · Patients with schizophrenia
- · AI in healthcare sector
- · Traditional subjective diagnostic methods
- · Healthcare systems reliant on imprecise diagnostics
Improved diagnostic accuracy and treatment efficacy for schizophrenia patients.
Potential for similar advanced signal processing techniques to be applied to other complex neurological and neuropsychiatric conditions.
Reduced societal burden of schizophrenia through earlier intervention and more effective management, potentially impacting long-term care costs and patient outcomes.
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Read at arXiv cs.AI