arXiv:2607. 22776v1 Announce Type: new Abstract: Repetitive transcranial magnetic stimulation (rTMS) is a non invasive therapy for Major Depressive Disorder (MDD).
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
The study introduces two fusion techniques—montage and blending—to combine EEG-derived time‑frequency images for predicting response to repetitive Transcranial Magnetic Stimulation (rTMS) in depression. Using a lightweight custom CNN, the authors evaluated the methods on two datasets (15 and 46 patients) under both segment‑level and subject‑disjoint cross‑validation. While segment‑level validation yielded high accuracies (up to 99.90 % on the primary dataset), subject‑disjoint validation showed poor performance, with AUC values ranging from 0.31 to 0.54 and a best subject‑level result of 0.874 ± 0.183 AUC.
arXiv:2606. 02166v1 Announce Type: new Abstract: Epilepsy is one of the most common neurological disorders globally, characterized by recurring seizures and significantly impacting the quality of life.
arXiv:2507. 12645v1 Announce Type: cross Abstract: The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient assessment, especially in synchronous monitoring.
The paper presents a transformer-based framework for detecting schizophrenia from EEG signals. It converts preprocessed EEG recordings into spectrograms via Short-Time Fourier Transform and classifies them using both traditional machine learning algorithms and deep learning models, including CNN-Transformer hybrids. Subject-level data partitioning ensures reliable evaluation, yielding an AUC-ROC of 88.41% for the CNN-Transformer model and 92.88% for the CNN + Squeeze and Excitation + Transformer model on an independent test set.
arXiv:2608. 06122v1 Announce Type: cross Abstract: Inspired by recent evidence that transformer architectures benefit from Self-PreTraining (SPT) on long-context benchmarks, we investigate whether similar gains extend to multimodal, multivariate, and even simple univariate medical time series.
arXiv:2606. 00180v1 Announce Type: cross Abstract: Deep learning-based Major Depressive Disorder (MDD) detection using Electroencephalography (EEG) is fundamentally constrained by the "small-sample dilemma.