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<p>Sleep architecture and integrity significantly influence neural recovery and cognitive restoration. These are particularly relevant in ischemic stroke survivors where sleep-disordered breathing (SDB)&#xa0;is a common comorbidity. To address the lack of stroke-specific sleep data, we present the Polysomnography Dataset for Sleep Analysis in Ischemic Stroke Patients (iSLEEPS), the first Asian and one of the largest stroke-specific sleep databases. Data collection was carried out between September-2018 and December-2021 at NIMHANS, India. iSLEEPS comprises 100 overnight PSG recordings with comprehensive expert annotations. Each recording includes sleep stages manually scored at 30-second epochs, detailed respiratory events, periodic limb movements, oxygen desaturation episodes, and clinical metrics, as per AASM&#xa0;(2017) guidelines. Our cohort demonstrates a high prevalence of SDB, enabling the investigation of stroke-sleep pathophysiology interactions. To illustrate dataset utility, we implemented automated sleep stage classification using deep learning methods. The Long Short-Term Memory model achieved the highest accuracy (74.70%), followed by Transformer (67.44%) and Convolutional Neural Network (61.65%). This dataset addresses crucial gap in stroke sleep research, supporting comprehensive analysis of post-stroke sleep disturbances.</p>

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Polysomnography Dataset for Sleep Analysis in Ischemic Stroke Patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月11日

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  • Suvadeep Maiti,
  • Shivam Kumar Sharma,
  • S. Mythirayee,
  • Srijithesh Rajendran,
  • Raju S. Bapi

摘要

Sleep architecture and integrity significantly influence neural recovery and cognitive restoration. These are particularly relevant in ischemic stroke survivors where sleep-disordered breathing (SDB) is a common comorbidity. To address the lack of stroke-specific sleep data, we present the Polysomnography Dataset for Sleep Analysis in Ischemic Stroke Patients (iSLEEPS), the first Asian and one of the largest stroke-specific sleep databases. Data collection was carried out between September-2018 and December-2021 at NIMHANS, India. iSLEEPS comprises 100 overnight PSG recordings with comprehensive expert annotations. Each recording includes sleep stages manually scored at 30-second epochs, detailed respiratory events, periodic limb movements, oxygen desaturation episodes, and clinical metrics, as per AASM (2017) guidelines. Our cohort demonstrates a high prevalence of SDB, enabling the investigation of stroke-sleep pathophysiology interactions. To illustrate dataset utility, we implemented automated sleep stage classification using deep learning methods. The Long Short-Term Memory model achieved the highest accuracy (74.70%), followed by Transformer (67.44%) and Convolutional Neural Network (61.65%). This dataset addresses crucial gap in stroke sleep research, supporting comprehensive analysis of post-stroke sleep disturbances.