Wavelet Scattering Transform for Interpretable Schizophrenia Biomarker Discovery and Classification from Resting-State EEG

• URL: http://arxiv.org/abs/2607.05282v1
• Date: Mon, 06 Jul 2026 16:27:26 GMT
• Title: Wavelet Scattering Transform for Interpretable Schizophrenia Biomarker Discovery and Classification from Resting-State EEG
• Abstract summary: Schizophrenia is a debilitating neuropsychiatric disorder characterized by profound cortical network dysregulation.<n>Existing automated classification pipelines rely predominantly on static power spectral density features inherently blind to amplitude modulation dynamics and cross-frequency coupling.<n>This study introduces a mathematically principled diagnostic framework integrating the multi-order Wavelet Scattering Transform(WST), strict Leave One Subject Out (LOSO) cross-validation, and SHAP explainability for simultaneous EEG classification and biomarker discovery.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 inflate reported performance. This study introduces a mathematically principled diagnostic framework integrating the multi-order Wavelet Scattering Transform(WST), strict Leave One Subject Out (LOSO) cross-validation, and SHAP explainability for simultaneous EEG classification and biomarker discovery. Hierarchical WST coefficients capturing multi-scale amplitude modulation structure were extracted from resting state multichannel EEG. Subject-level ANOVA with Benjamini Hochberg false discovery rate correction identified significant biomarkers, with Random Forest and SVM classifiers evaluated under strict LOSO cross validation and subject-level majority voting. Second-order scattering coefficients encoding cross frequency coupling dominated the discriminative biomarker set, with gamma-band features most prevalent, demonstrating that temporal amplitude modulation constitutes the primary electrophysiological signature of schizophrenia. Electrode P3 was identified as the single most discriminative site. Under rigorous subject independent evaluation, the Random Forest achieved 90.48% accuracy (AUC = 0.9339; sensitivity = 95.56%). The proposed WST framework establishes a rigorous, interpretable standard for EEG-driven psychiatric biomarker discovery that can also be applicable in the detection of schizophrenia subtypes in the future.

Related papers

• Schizophrenia Detection from EEG Signals: A Transformer Framework with Spectrogram Representation [0.0]
  We propose an EEG-based Schizophrenia classification framework that transforms pre-processed EEG recordings into time-frequency representations.<n>To ensure reliable evaluation, all data partitions are performed at the subject level.<n> Experimental results demonstrate that the proposed approach achieves competitive classification performance.
  arXiv  Detail & Related papers  (2026-09-12T15:58:55Z)
• Echo-DM: Ultrasound Marker Removal via Conditional Latent Diffusion and Region-Aware Fusion [50.02433280054874]
  markers can introduce shortcut bias in downstream automated analysis.<n>We present Echo-DM, a framework for ultrasound marker removal via conditional latent diffusion and region-aware fusion.<n>Experiments on Echo-PAIR, a large-scale paired clinical ultrasound dataset, demonstrate superior marker removal and strong anatomical fidelity.
  arXiv  Detail & Related papers  (2026-06-08T11:56:33Z)
• Stain-Aware Wavelet Regularization for Instant Adversarial Purification in Histopathology [12.803436797824268]
  We propose Stain-Aware Wavelet Regularization (SAWR) to disentangle adversarial perturbations from diagnostic structural information.<n>When integrated into an instant purification framework, SAWR improves adversarial robustness by up to 10.69% over the baseline approach.
  arXiv  Detail & Related papers  (2026-06-07T17:32:24Z)
• Towards Trustworthy Depression Estimation via Disentangled Evidential Learning [50.22167852149165]
  We propose EviDep, an evidential learning framework that jointly quantifies depression severity.<n>EviDep enforces strict information integrity to guarantee robust evidence synthesis.<n>It achieves state-of-the-art predictive accuracy and superior uncertainty calibration, delivering a robust fail-safe mechanism for trustworthy clinical screening.
  arXiv  Detail & Related papers  (2026-04-17T13:27:11Z)
• Suppressing Prior-Comparison Hallucinations in Radiology Report Generation via Semantically Decoupled Latent Steering [94.37535002230504]
  We develop a training-free, inference-time control framework termed Semantically Decoupled Latent Steering.<n>Our approach constructs a semantic-free intervention vector via large language model (LLM)-driven semantic decomposition.<n>We show that our approach significantly reduces the probability of historical hallucinations.
  arXiv  Detail & Related papers  (2026-02-27T04:49:01Z)
• Variational Mixture of Graph Neural Experts for Alzheimer's Disease Biomarker Recognition in EEG Brain Networks [6.727599092628398]
  Existing EEG-based methods are limited by full-band frequency analysis.<n>We propose a variational mixture of graph neural experts (VMoGE) that integrates frequency-specific biomarker identification.<n>VMoGE achieves superior performance with AUC improvements of +4% to +10% over state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-10-13T20:37:18Z)
• Microelectrode Signal Dynamics as Biomarkers of Subthalamic Nucleus Entry on Deep Brain Stimulation: A Nonlinear Feature Approach [0.0]
  We propose a framework that leverages nonlinear dynamics and entropy-based metrics to classify neural activity recorded inside versus outside the STN.<n>Our results highlight the potential of nonlinear and entropy signal descriptors in supporting real-time, data-driven decision-making during DBS surgeries.
  arXiv  Detail & Related papers  (2025-06-14T23:23:26Z)
• Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review [4.799269666410891]
  We review how GNN and model explainability techniques have been applied to fMRI datasets for disorder prediction tasks.<n>We identify 65 studies using GNNs that reported potential fMRI biomarkers for psychiatric disorders.<n>We suggest establishing new standards that are based on objective evaluation metrics to determine the robustness of potential biomarkers.
  arXiv  Detail & Related papers  (2024-05-01T15:29:55Z)
• Power Spectral Density-Based Resting-State EEG Classification of First-Episode Psychosis [1.3416169841532526]
  We show the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incorporating multiple frequency bands should be more efficient in associating potential EEG biomarkers with First-Episode Psychosis (FEP) A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper.
  arXiv  Detail & Related papers  (2022-11-23T00:28:41Z)
• Discriminative Singular Spectrum Classifier with Applications on Bioacoustic Signal Recognition [67.4171845020675]
  We present a bioacoustic signal classifier equipped with a discriminative mechanism to extract useful features for analysis and classification efficiently. Unlike current bioacoustic recognition methods, which are task-oriented, the proposed model relies on transforming the input signals into vector subspaces. The validity of the proposed method is verified using three challenging bioacoustic datasets containing anuran, bee, and mosquito species.
  arXiv  Detail & Related papers  (2021-03-18T11:01:21Z)
• Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation [72.0276067144762]
  We discuss and define the two common types of label noise in medical images. We propose an uncertainty estimation-based framework to handle these two label noise amid the medical image classification task.
  arXiv  Detail & Related papers  (2021-02-28T14:56:45Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.