基于注意力的CNN-BiLSTM用于时空宽场钙成像数据的睡眠状态分类
Attention-Based CNN-BiLSTM for Sleep State Classification of Spatiotemporal Wide-Field Calcium Imaging Data
AI总结：
本文提出一种结合CNN与带注意力机制BiLSTM的混合网络，用于从小鼠宽场钙成像数据中自动分类清醒、NREM和REM睡眠状态，准确率达84%，kappa为0.67，性能与人工EEG/EMG评分相当。
摘要：
背景：使用基因编码钙指示剂的宽场钙成像（WFCI）能够对小鼠的神经元活动进行时空记录。当应用于睡眠研究时，WFCI数据通常借助辅助的EEG和EMG记录被人工评分为清醒、非快速眼动（NREM）和快速眼动（REM）等睡眠状态。然而，这一过程耗时、具有侵入性，并且常常存在评分者间和评分者自身信度较低的问题。因此，需要一种能够对时空WFCI数据进行自动睡眠状态分类的方法。新方法：提出了一种混合网络架构，该架构由用于提取图像帧空间特征的卷积神经网络（CNN）和带有注意力机制的双向长短期记忆网络（BiLSTM）组成，用于识别不同时间点之间的时间依赖性，从而将WFCI数据分类为清醒、NREM和REM睡眠状态。结果：睡眠状态的分类准确率为84%，Cohen's kappa为0.64。梯度加权类激活图显示，在将WFCI数据分类为NREM睡眠时，皮层额叶区域具有更高的重要性，而后部区域对清醒状态的识别贡献最大。注意力分数表明，所提出的网络以状态特异性的方式关注短期和长期的时间依赖性。与现有方法的比较：在3小时的WFCI记录上，CNN-BiLSTM实现了0.67的kappa值，与基于人类EEG/EMG评分所对应的0.65的kappa值相当。结论：CNN-BiLSTM能够有效地从时空WFCI数据中分类睡眠状态，并将推动WFCI在睡眠研究中的更广泛应用。
英文摘要：
Background: Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming, invasive and often suffers from low inter- and intra-rater reliability. Therefore, an automated sleep state classification method that operates on spatiotemporal WFCI data is desired. New Method: A hybrid network architecture consisting of a convolutional neural network (CNN) to extract spatial features of image frames and a bidirectional long short-term memory network (BiLSTM) with attention mechanism to identify temporal dependencies among different time points was proposed to classify WFCI data into states of wakefulness, NREM and REM sleep. Results: Sleep states were classified with an accuracy of 84% and Cohen's kappa of 0.64. Gradient-weighted class activation maps revealed that the frontal region of the cortex carries more importance when classifying WFCI data into NREM sleep while posterior area contributes most to the identification of wakefulness. The attention scores indicated that the proposed network focuses on short- and long-range temporal dependency in a state-specific manner. Comparison with Existing Method: On a 3-hour WFCI recording, the CNN-BiLSTM achieved a kappa of 0.67, comparable to a kappa of 0.65 corresponding to the human EEG/EMG-based scoring. Conclusions: The CNN-BiLSTM effectively classifies sleep states from spatiotemporal WFCI data and will enable broader application of WFCI in sleep.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
