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A new neural network developed by researchers at the University of Eastern Finland and Kuopio University Hospital enables simple and accurate assessment of the severity of sleep apnea in patients with cerebrovascular disease. The assessment is automated and based on simple nocturnal pulse oximetry, making it easy to screen for sleep apnea in stroke units.
Up to 90% of stroke patients suffer from sleep apnea, according to previous studies conducted at Kuopio University Hospital. If left untreated, sleep apnea can reduce the quality of life and rehabilitation of stroke patients and increase the risk of recurrent stroke vascular events.
“Although screening for sleep apnea is recommended for patients with cerebrovascular disease, it is rarely performed in stroke units due to the complexity of the measuring devices, time-consuming manual analyzes and high costs.” , says researcher Akseli Leino from the University of Eastern Finland.
In the new study, researchers developed a neural network to assess the severity of sleep apnea in patients with acute stroke and transient ischemic attack (TIA) using a simple nocturnal oxygen saturation signal. The Apnea-Hypopnea Index, which represents the number of apnea and hypopnea events per hour, is commonly used in the diagnosis of sleep apnea. When the researchers compared the results of manual scoring with those obtained using the new neural network, the median difference was only 1.45 events per hour. The neural network was also 78% accurate in classifying patients into four different categories based on the severity of sleep apnea (no sleep apnea, mild, moderate, severe). The neural network was able to identify moderate and severe sleep apnea, both requiring treatment, in patients with acute stroke or TIA with a specificity of 96% and a sensitivity of 92%.
“The neural network developed in the study enables easy and cost-effective screening for sleep apnea in patients with cerebrovascular disease in hospital wards and stroke units. The nocturnal oxygen saturation signal can be recorded with a simple finger pulse oximetry measurement, without delay – a consuming manual analysis is required, ”emphasizes medical physicist Katja Myllymaa of Kuopio University Hospital.
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The study was carried out in collaboration between the Department of Clinical Neurophysiology and the Department of Neurology of Kuopio University Hospital and the Department of Applied Physics of the University of Eastern Finland. The study was funded by the Academy of Finland, Business Finland, Kuopio University Hospital, Finnish Cultural Foundation, Kuopio Area Respiratory Foundation, Research Foundation of the Pulmonary Diseases, Finnish Anti-Tuberculosis Association Foundation, the Päivikki & Sakari Sohlberg Foundation, Paulo Foundation and Tampere Tuberculosis Foundation.
For more information, please contact:
Junior researcher Akseli Leino, MSc (Tech), [email protected]
Associate Professor, Medical Physicist Katja Myllymaa, PhD, [email protected]
Research article:
Leino A, Nikkonen S, Kainulainen S, Korkalainen H, Töyräs J, Myllymaa S, Leppänen T, Ylä-Herttuala S, Westeren-Punnonen S, Muraja-Murro A, Jäkälä P, Mervaala E, Myllymaa Le K. Neural network analysis. SpO2 signal allows easy screening for sleep apnea in patients with acute cerebrovascular disease. Sleep med 2020; 79. https: /
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