背景:睡眠纺锤体已成为评估认知能力和相关疾病的有价值的生物标志物,强调其在临床研究中检测的重要性。然而,使用固定模板的基于模板匹配的算法可能无法完全适应不同持续时间的主轴。此外,受图像多尺度特征提取的启发,使用多尺度特征提取方法可以更好地适应不同频率和持续时间的主轴。
方法:因此,本研究提出了一种新颖的基于弹性时间窗和空间金字塔池化(SPP)的自动主轴检测算法,用于提取多尺度特征。该算法利用弹性时间窗对脑电图(EEG)信号进行分割,实现跨多个尺度的特征提取。这种方法可以适应不同EEG时期纺锤体持续时间和极化定位的显着变化。此外,空间金字塔池化被集成到深度可分离卷积(DSC)网络中,以在不同尺度上对分段主轴信号特征执行多尺度池化。
结果:与现有模板匹配算法相比,该算法的主轴波偏振定位更符合实际情况。在公开数据集DREAMS上进行的实验结果表明,该算法的平均准确率达到95.75%,平均阴性预测值(NPV)为96.55%,表明其先进的性能。
结论:通过彻底的消融实验验证了每个模块的有效性。更重要的是,当面对不同实验对象的变化时,该算法具有较强的鲁棒性。这一特点使算法在识别睡眠纺锤时更加准确,有望帮助专家自动检测睡眠脑电图信号中的纺锤,减少人工检测的工作量和时间,提高效率。
BACKGROUND: Sleep spindles have emerged as valuable biomarkers for assessing cognitive abilities and related disorders, underscoring the importance of their detection in clinical research. However, template matching-based algorithms using fixed templates may not be able to fully adapt to spindles of different durations. Moreover, inspired by the multiscale feature extraction of images, the use of multiscale feature extraction methods can be used to better adapt to spindles of different frequencies and durations.
METHODS: Therefore, this study proposes a novel automatic spindle detection algorithm based on elastic time windows and spatial pyramid pooling (SPP) for extracting multiscale features. The algorithm utilizes elastic time windows to segment electroencephalogram (EEG) signals, enabling the extraction of features across multiple scales. This approach accommodates significant variations in spindle duration and polarization positioning during different EEG epochs. Additionally, spatial pyramid pooling is integrated into a depthwise separable convolutional (DSC) network to perform multiscale pooling on the segmented spindle signal features at different scales.
RESULTS: Compared with existing template matching algorithms, this algorithm\'s spindle wave polarization positioning is more consistent with the real situation. Experimental results conducted on the public dataset DREAMS show that the average accuracy of this algorithm reaches 95.75%, with an average negative predictive value (NPV) of 96.55%, indicating its advanced performance.
CONCLUSIONS: The effectiveness of each module was verified through thorough ablation experiments. More importantly, the algorithm shows strong robustness when faced with changes in different experimental subjects. This feature makes the algorithm more accurate at identifying sleep spindles and is expected to help experts automatically detect spindles in sleep EEG signals, reduce the workload and time of manual detection, and improve efficiency.