A Stochastic Coding Method of EEG Signals for Sleep Stage Classification
A Stochastic Coding Method of EEG Signals for Sleep Stage Classification
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DOI：
10.1109/socc56010.2022.9908121
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Guangxian Zhu;Huijian. Wang;Yirong Kan;Zheng Chen;Ming Huang;M. Altaf-Ul-Amin;N. Ono;Shigehiko Kanaya;Renyuan Zhang;Y. Nakashima
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文献类型：
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作者：
Guangxian Zhu;Huijian. Wang;Yirong Kan;Zheng Chen;Ming Huang;M. Altaf-Ul-Amin;N. Ono;Shigehiko Kanaya;Renyuan Zhang;Y. Nakashima
记录脑神经元的激发动作电位（尖峰），也称为尖峰序列，被认为是神经系统中信息传输的主要模式。脑电描记术（EEG）是对脑电锋电位序列最直接的采样方法。然而，由于神经元固有的生物学特性，如尖峰随机性、时序动态性和噪声包容性，在EEG相关的生理识别任务（如睡眠分期、癫痫检测等）中存在挑战。传统的脑电特征工程倾向于确定性的统计分析和推理，往往忽略了神经元的生物学特性。在本文中，我们提出了一种创新的非确定性编码方法的脑电信号，以提高性能的睡眠阶段分类任务。通过局部归一化，概率采样，和窗口投影的EEG信号，我们离散化的连续信号，并将它们送入后续的分类模型。该编码方法在公开数据集和典型的脑电深度学习模型上进行了测试。我们的建议取得了有竞争力的睡眠分期结果。Wake、N1、N2、N3和REM阶段的精度分别为0.95、0.84、0.92、0.98和0.85。我们的研究表明，脑电的非确定性编码在生物医学器件中具有进一步应用的潜力。
The recording of fired action potentials (spikes) of brain neurons, also known as spike trains, is considered to be the primary mode of information transmission in the nervous system. Electroencephalography (EEG) is the most direct sampling method for spike trains. However, due to the inherent biological properties of neurons such as spike randomness, timing dynamic, and noisy containment, there are challenges in EEG-related physiological identification tasks (such as sleep staging, epilepsy detection, etc.). Traditional feature engineering of EEG has a tendency toward deterministic statistical analysis and inference, which often ignores the biological properties of neurons. In this paper, we propose an innovative non-deterministic coding method of EEG signals for improving the performance of sleep stage classification tasks. By local normalization, probabilistic sampling, and window projection on the EEG signals, we discretize the continuous signals and feed them into a subsequent classification model. The coding method is tested on the public datasets and typical deep learning models for EEG. Our proposal achieved competitive sleep staging results. The precision of 0.95, 0.84, 0.92, 0.98, and 0.85 were obtained in the Wake, N1, N2, N3, and REM stages, respectively. Our research shows that the non- deterministic coding of EEG has potential for further application in biomedical devices.