Sleep Stage Classification Using Time-Frequency Spectra From Consecutive Multi-Time Points
Sleep Stage Classification Using Time-Frequency Spectra From Consecutive Multi-Time Points
复制标题
使用连续多时间点的时频谱进行睡眠阶段分类
作者：
Ziliang Xu;Xuejuan Yang;Jinbo Sun;Peng Liu;W. Qin
睡眠阶段的分类是睡眠研究领域的一个开放挑战使用几个连续30 s时间点的时间频谱作为输入来执行睡眠阶段分类使用单个30 s时间点的时间频谱作为输入的神经网络（CNN）用于比较。 LSTM网络的分类性能要比CNN更好。考虑了来自三个连续30 s的时间点的临时信息，与CNN相比，分类精度为87.4％，Cohen的Kappa至关重要。从数据中考虑了这些临时信息，因此可能更适合睡眠阶段分类。
Sleep stage classification is an open challenge in the field of sleep research. Considering the relatively small size of datasets used by previous studies, in this paper we used the Sleep Heart Health Study dataset from the National Sleep Research Resource database. A long short-term memory (LSTM) network using a time-frequency spectra of several consecutive 30 s time points as an input was used to perform the sleep stage classification. Four classical convolutional neural networks (CNNs) using a time-frequency spectra of a single 30 s time point as an input were used for comparison. Results showed that, when considering the temporal information within the time-frequency spectrum of a single 30 s time point, the LSTM network had a better classification performance than the CNNs. Moreover, when additional temporal information was taken into consideration, the classification performance of the LSTM network gradually increased. It reached its peak when temporal information from three consecutive 30 s time points was considered, with a classification accuracy of 87.4% and a Cohen’s Kappa coefficient of 0.8216. Compared with CNNs, our results indicate that for sleep stage classification, the temporal information within the data or the features extracted from the data should be considered. LSTM networks take this temporal information into account, and thus, may be more suitable for sleep stage classification.
影响因子：
5.6
作者：
S. Quan;B. Howard;C. Iber;C. Iber;J. Kiley;F. Nieto;G. O'Connor;D. Rapoport;S. Redline;
通讯作者：
S. Quan;B. Howard;C. Iber;C. Iber;J. Kiley;F. Nieto;G. O'Connor;D. Rapoport;S. Redline;
影响因子：
5.6
作者：
S. Redline;M. Sanders;Bonnie K. Lind;S. Quan;C. Iber;D. Gottlieb;W. Bonekat;D. Rapoport;Philip L.
通讯作者：
S. Redline;M. Sanders;Bonnie K. Lind;S. Quan;C. Iber;D. Gottlieb;W. Bonekat;D. Rapoport;Philip L.