Introduction: Visual sleep-stage scoring is a time-consuming technique that cannot extract the nonlinear characteristics of electroencephalogram (EEG). This article presents a novel method for sleep-stage differentiation based on sonification of sleep-EEG signals using wavelet transform and recurrent neural network (RNN). Methods: Two RNNs were designed and trained separately based on a database of classical guitar pieces and Kurdish tanbur Makams using a long short-term memory model. Moreover, discrete wavelet transform and wavelet packet decomposition were used to determine the association between the EEG signals and musical pitches. Continuous wavelet transform was applied to extract musical beat-based features from the EEG. Then, the pretrained RNN was used to generate music. To test the proposed model, 11 sleep EEGs were mapped onto the guitar and tanbur frequency intervals and presented to the pretrained RNN. Next, the generated music was randomly presented to 2 neurologists. Results: The proposed model classified the sleep stages with an accuracy of >81% for tanbur and more than 93% for guitar musical pieces. The inter-rater reliability measured by Cohen’s kappa coefficient (κ) revealed good reliability for both tanbur (κ = 0.64, p < 0.001) and guitar musical pieces (κ = 0.85, p < 0.001). Conclusion: The present EEG sonification method leads to valid sleep staging by clinicians. The method could be used on various EEG databases for classification, differentiation, diagnosis, and treatment purposes. Real-time EEG sonification can be used as a feedback tool for replanning of neurophysiological functions for the management of many neurological and psychiatric disorders in the future.

中文翻译：

---

基于小波变换和递归神经网络的睡眠脑电信号超声处理的睡眠阶段分类新方法

简介：视觉睡眠阶段评分是一项耗时的技术，无法提取脑电图 (EEG) 的非线性特征。本文提出了一种基于使用小波变换和递归神经网络 (RNN) 对睡眠 EEG 信号进行超声处理的睡眠阶段区分的新方法。方法：基于古典吉他乐曲和库尔德语 tanbur Makams 的数据库，使用长短期记忆模型分别设计和训练了两个 RNN。此外，离散小波变换和小波包分解被用来确定脑电信号和音高之间的关联。应用连续小波变换从脑电图中提取基于音乐节拍的特征。然后，使用预训练的 RNN 生成音乐。为了测试所提出的模型，11 个睡眠脑电图被映射到吉他和 tanbur 频率间隔上，并呈现给预训练的 RNN。接下来，生成的音乐被随机呈现给 2 位神经学家。结果：所提出的模型对睡眠阶段进行分类，tanbur 的准确率超过 81%，吉他乐曲的准确率超过 93%。由 Cohen 的 kappa 系数 (κ) 衡量的评分者间可靠性显示 tanbur (κ = 0.64, p < 0.001) 和吉他音乐作品 (κ = 0.85, p < 0.001) 具有良好的可靠性。结论：目前的 EEG 超声方法导致临床医生有效的睡眠分期。该方法可用于各种 EEG 数据库，用于分类、鉴别、诊断和治疗目的。