What question did this study set out to answer?

To create a comprehensive dataset for analyzing sleep in ischemic stroke patients and understand sleep disorders' impact on recovery.

February 14, 2026Scientific Data1 citationsOpen Access

Polysomnography Dataset for Sleep Analysis in Ischemic Stroke Patients

SMSuvadeep MaitiQueen Mary University of London SSShivam Kumar SharmaIndian Institute of Technology Hyderabad SMS. MythirayeeNational Institute of Mental Health and Neurosciences

Key Points

To create a comprehensive dataset for analyzing sleep in ischemic stroke patients and understand sleep disorders' impact on recovery.
Data collected from 100 ischemic stroke patients between September 2018 and December 2021 at NIMHANS, India.
Each polysomnography (PSG) recording was manually annotated for sleep stages, respiratory events, limb movements, and oxygen levels.
Automated sleep stage classification was performed using deep learning models.
The dataset shows a high prevalence of sleep-disordered breathing among stroke survivors.
The Long Short-Term Memory model achieved the highest accuracy of 74.70% in sleep stage classification.
The dataset fills a critical research gap in post-stroke sleep disturbances.

Abstract

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.

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