Christensen Julie A E, Frandsen Rune, Kempfner Jacob, Arvastson Lars, Christensen Soren R, Jennum Poul, Sorensen Helge B D
DTU Electrical Eng., Ørsteds Plads, bldg. 349, DK-2800 Kgs. Lyngby.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:2941-4. doi: 10.1109/EMBC.2012.6346580.
In this study, polysomnographic left side EOG signals from ten control subjects, ten iRBD patients and ten Parkinson's patients were decomposed in time and frequency using wavelet transformation. A total of 28 features were computed as the means and standard deviations in energy measures from different reconstructed detail subbands across all sleep epochs during a whole night of sleep. A subset of features was chosen based on a cross validated Shrunken Centroids Regularized Discriminant Analysis, where the controls were treated as one group and the patients as another. Classification of the subjects was done by a leave-one-out validation approach using same method, and reached a sensitivity of 95%, a specificity of 70% and an accuracy of 86.7%. It was found that in the optimal subset of features, two hold lower frequencies reflecting the rapid eye movements and two hold higher frequencies reflecting EMG activity. This study demonstrates that both analysis of eye movements during sleep as well as EMG activity measured at the EOG channel hold potential of being biomarkers for Parkinson's disease.
在本研究中，使用小波变换对来自10名对照受试者、10名快速眼动行为障碍（iRBD）患者和10名帕金森病患者的多导睡眠图左侧眼电图（EOG）信号进行了时间和频率分解。在一整晚的睡眠中，计算了28个特征，作为所有睡眠时段不同重构细节子带能量测量的均值和标准差。基于交叉验证的收缩质心正则判别分析选择了一组特征子集，其中将对照受试者作为一组，患者作为另一组。采用相同方法通过留一法验证对受试者进行分类，灵敏度达到95%，特异性达到70%，准确率达到86.7%。研究发现，在特征的最优子集中，两个较低频率反映快速眼动，两个较高频率反映肌电图（EMG）活动。本研究表明，睡眠期间的眼动分析以及EOG通道测量的EMG活动都有可能成为帕金森病的生物标志物。
