Work

SpindleSwin-Net: End-to-end and flexible sleep spindle detection for EEG signals based on wavelet and advanced vision transformer

Year: 2026

Type: article

Abstract: Sleep spindles are key reference signals in electroencephalography (EEG), typically occurring during non-rapid eye movement sleep. This study evaluates an automated detection method based on image-level analysis using an advanced vision transformer. A novel deep network, SpindleSwin-Net, is proposed for end-to-end and flexible sleep spindle detection by integrating the advantages of wavelet decomposition and the Swin Transformer architecture. The Montreal Archive of Sleep Studies (MASS) and DREAMS datasets are utilised. During image transformation, 1.5 s time–frequency maps containing annotated spindles are generated using a designed continuous wavelet transform filter, while randomly sampled EEG segments without annotations are labelled as non-spindles. In the detection stage, all time–frequency maps are input into a dual-branch Swin Transformer network to classify spindle versus non-spindle signals and to predict coordinates. Five-fold cross-validation is applied during training. SpindleSwin-Net is evaluated through both single-dataset and cross-dataset analyses. The method achieves recall, precision, F1-score, and IoU of (E1: 0.71, 0.62, 0.65, 0.66; E2: 0.99, 0.92, 0.95, 0.67) on the MASS dataset and (E3: 0.84, 0.52, 0.63, 0.60; E4: 0.88, 0.69, 0.77, 0.77) on the DREAMS dataset. Cross-dataset experiments demonstrate F1-scores of 0.96 and 0.63 on MASS and DREAMS, respectively. This vision transformer-based approach bypasses the cumbersome steps of conventional signal processing and reduces annotation time, offering potential as a practical tool to alleviate the workload of sleep clinicians.

Source: Biomedical Signal Processing and Control

DOI: 10.1016/j.bspc.2026.111176

Authors: Shao-Jun Xia, Anlan Sun, Jing Bao, Zhi-Nan Wang, Xiao-Ting Li, Ying-Shi Sun

Institutions: Peking University Cancer Hospital, École Centrale de Nantes

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $2,590

Funders: National Natural Science Foundation of China, Natural Science Foundation of Beijing Municipality, Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support