Department of Physiology, Medical School, Atatürk University, 25240, Erzurum, Turkey.
Department of Computer Engineering, Faculty of Engineering, Atatürk University, Erzurum, Turkey.
Phys Eng Sci Med. 2024 Sep;47(3):1073-1085. doi: 10.1007/s13246-024-01428-7. Epub 2024 May 31.
This paper aims to present a model called SPINDILOMETER, which we propose to be integrated into polysomnography (PSG) devices for researchers focused on electrophysiological signals in PSG, physicians, and technicians practicing sleep in clinics, by examining the methods of the sleep electroencephalogram (EEG) signal analysis in recent years. For this purpose, an assist diagnostic model for PSG has been developed that measures the number and density of sleep spindles by analyzing EEG signals in PSG. EEG signals of 72 volunteers, 51 males and 21 females (age; 51.7 ± 3.42 years and body mass index; 37.6 ± 4.21) diagnosed with sleep-disordered breathing by PSG were analyzed by machine learning methods. The number and density of sleep spindles were compared between the classical method (EEG monitoring with the naked eye in PSG) ('method with naked eye') and the model (SPINDILOMETER). A strong positive correlation was found between 'method with naked eye' and SPINDILOMETER results (correlation coefficient: 0.987), and this correlation was statistically significant (p = 0.000). Confusion matrix (accuracy (94.61%), sensitivity (94.61%), specificity (96.60%)), and ROC analysis (AUC: 0.95) were performed to prove the adequacy of SPINDILOMETER (p = 0.000). In conclusion SPINDILOMETER can be included in PSG analysis performed in sleep laboratories. At the same time, this model provides diagnostic convenience to the physician in understanding the neurological events associated with sleep spindles and sheds light on research for thalamocortical regions in the fields of neurophysiology and electrophysiology.
本文旨在介绍一种名为 SPINDILOMETER 的模型，我们建议将其集成到多导睡眠图 (PSG) 设备中，供专注于 PSG 中的电生理信号的研究人员、医生和在诊所中进行睡眠实践的技术人员使用，方法是研究近年来睡眠脑电图 (EEG) 信号分析的方法。为此，开发了一种用于 PSG 的辅助诊断模型，通过分析 PSG 中的 EEG 信号来测量睡眠纺锤波的数量和密度。对 72 名志愿者的 EEG 信号进行了分析，其中 51 名男性和 21 名女性（年龄：51.7±3.42 岁，体重指数：37.6±4.21），这些志愿者均通过 PSG 诊断为睡眠呼吸障碍。使用机器学习方法对这些志愿者的 EEG 信号进行了分析。将睡眠纺锤波的数量和密度与经典方法（PSG 中的 EEG 监测）（“裸眼方法”）和模型（SPINDILOMETER）进行了比较。“裸眼方法”和 SPINDILOMETER 结果之间存在很强的正相关（相关系数：0.987），并且这种相关性具有统计学意义（p=0.000）。通过混淆矩阵（准确率（94.61%）、敏感度（94.61%）、特异性（96.60%））和 ROC 分析（AUC：0.95）来证明 SPINDILOMETER 的充分性（p=0.000）。总之，SPINDILOMETER 可以包含在睡眠实验室中进行的 PSG 分析中。同时，该模型为医生提供了诊断便利，有助于了解与睡眠纺锤波相关的神经事件，并为神经生理学和电生理学领域的丘脑皮质区域的研究提供了启示。
