Scholarlink

<p>Infantile Epileptic Spasms Syndrome (IESS) represents a severe form of developmental epileptic encephalopathy in infancy, characterized by clusters of spasms and hypsarrhythmia patterns on electroencephalogram (EEG), which often lead to long-term neurodevelopmental impairments if not diagnosed promptly. The inherent non-stationarity and polymorphic complexity of EEG signals complicate interpretation, resulting in time-consuming and error-prone diagnostics that hinder timely therapeutic interventions. To address these challenges, we propose SinTransNet, an innovative EEG-based deep learning framework that combines multi-band signal decomposition, adaptive sinusoidal convolutions, and Transformer-based attention mechanism. This architecture decomposes EEG into five key frequency bands (<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\delta\)</EquationSource></InlineEquation>, <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(\theta\)</EquationSource></InlineEquation>, <InlineEquation ID="IEq3"><EquationSource Format="TEX">\(\alpha\)</EquationSource></InlineEquation>, <InlineEquation ID="IEq4"><EquationSource Format="TEX">\(\beta\)</EquationSource></InlineEquation>, <InlineEquation ID="IEq5"><EquationSource Format="TEX">\(\gamma\)</EquationSource></InlineEquation>) to isolate oscillatory features such as spike-and-wave complexes, employs sinusoidal convolutions for frequency-adaptive feature extraction, and utilizes Transformer attention to capture inter-band correlations and long-range dependencies essential for accurate IESS detection. Evaluated on a proprietary dataset comprising 129 EEG recordings with 1,941 epileptic spasm events, SinTransNet demonstrates superior performance with average accuracy of 85.69%, sensitivity of 80.55%, and specificity of 90.76%. By providing an automated, efficient tool for IESS identification, SinTransNet holds promise for enhancing clinical workflows and supporting early interventions in pediatric neurology.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

SinTransNet: an EEG-based deep learning framework for infantile epileptic spasms syndrome detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月24日

查看全文
  • Junyuan Feng,
  • Zhenzhen Liu,
  • Linlin Shen,
  • Xiaoling Luo,
  • Yan Chen,
  • Lin Li,
  • Tian Zhang

摘要

Infantile Epileptic Spasms Syndrome (IESS) represents a severe form of developmental epileptic encephalopathy in infancy, characterized by clusters of spasms and hypsarrhythmia patterns on electroencephalogram (EEG), which often lead to long-term neurodevelopmental impairments if not diagnosed promptly. The inherent non-stationarity and polymorphic complexity of EEG signals complicate interpretation, resulting in time-consuming and error-prone diagnostics that hinder timely therapeutic interventions. To address these challenges, we propose SinTransNet, an innovative EEG-based deep learning framework that combines multi-band signal decomposition, adaptive sinusoidal convolutions, and Transformer-based attention mechanism. This architecture decomposes EEG into five key frequency bands (\(\delta\), \(\theta\), \(\alpha\), \(\beta\), \(\gamma\)) to isolate oscillatory features such as spike-and-wave complexes, employs sinusoidal convolutions for frequency-adaptive feature extraction, and utilizes Transformer attention to capture inter-band correlations and long-range dependencies essential for accurate IESS detection. Evaluated on a proprietary dataset comprising 129 EEG recordings with 1,941 epileptic spasm events, SinTransNet demonstrates superior performance with average accuracy of 85.69%, sensitivity of 80.55%, and specificity of 90.76%. By providing an automated, efficient tool for IESS identification, SinTransNet holds promise for enhancing clinical workflows and supporting early interventions in pediatric neurology.