Polysomnography Dataset for Sleep Analysis in Ischemic Stroke Patients

Sleep health is vital for cognitive function and overall well-being, especially in populations such as ischemic stroke patients, where sleep-disordered breathing is highly prevalent. To address the paucity of stroke-specific sleep data, particularly within the Indian context, we present the Polysomnography Dataset for Sleep Analysis in Indian Ischemic Stroke Patients (iSLEEPS). This dataset comprises 100 polysomnography (PSG) recordings from ischemic stroke patients, with detailed annotations of sleep parameters, as well as patient demographics, lesion location, and stroke characteristics. Adhering to ethical guidelines, the iSLEEPS is publicly accessible and designed for a variety of applications, ranging from training machine learning models to in-depth exploration of sleep disorders in stroke patients. We evaluated the dataset using state-of-the-art deep learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Transformer based architectures. The CNN-based model achieved an accuracy of 61.65%, the LSTM-based model 74.70%, and the Transformer-based model 67.44% in automated sleep stage classification. These results highlight the dataset’s utility in training machine learning models and its potential to enhance the sleep architecture in stroke patients. By offering this resource, we aim to bridge the gap in Indian-specific sleep disorder data and foster innovation in the field of sleep medicine.