Abstract
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.