Electrical and Electronics Eng. Department, Ondokuz Mayıs University, Samsun, Turkey.
J Med Syst. 2011 Aug;35(4):457-61. doi: 10.1007/s10916-009-9381-7. Epub 2009 Sep 30.
In the present study, the Singular Spectrum Analysis (SSA) is applied to sleep EEG segments collected from healthy volunteers and patients diagnosed by either psycho physiological insomnia or paradoxical insomnia. Then, the resulting singular spectra computed for both C3 and C4 recordings are assigned as the features to the Artificial Neural Network (ANN) architectures for EEG classification in diagnose. In tests, singular spectrum of particular sleep stages such as awake, REM, stage1 and stage2, are considered. Three clinical groups are successfully classified by using one hidden layer ANN architecture with respect to their singular spectra. The results show that the SSA can be applied to sleep EEG series to support the clinical findings in insomnia if ten trials are available for the specific sleep stages. In conclusion, the SSA can detect the oscillatory variations on sleep EEG. Therefore, different sleep stages meet different singular spectra. In addition, different healthy conditions generate different singular spectra for each sleep stage. In summary, the SSA can be proposed for EEG discrimination to support the clinical findings for psycho-psychological disorders.
在本研究中，奇异谱分析（SSA）应用于从健康志愿者和经心理生理性失眠或矛盾性失眠诊断的患者中收集的睡眠 EEG 段。然后，为 C3 和 C4 记录计算的所得奇异谱被分配为用于 EEG 分类诊断的人工神经网络（ANN）架构的特征。在测试中，考虑了特定睡眠阶段（如清醒、REM、阶段 1 和阶段 2）的奇异谱。使用具有一个隐藏层的 ANN 架构，可以成功地对三个临床组进行分类，以获得其奇异谱。结果表明，如果有十个特定睡眠阶段的试验，SSA 可以应用于睡眠 EEG 序列，以支持失眠的临床发现。总之，SSA 可以检测睡眠 EEG 上的振荡变化。因此，不同的睡眠阶段具有不同的奇异谱。此外，不同的健康状况会为每个睡眠阶段产生不同的奇异谱。总之，SSA 可用于 EEG 判别以支持心理障碍的临床发现。
