Jalali Hamidreza, Pouladian Majid, Nasrabadi Ali Motie, Movahed Azin
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Biomedical Engineering Department, Faculty of Engineering, Shahed University, Tehran, Iran.
Heliyon. 2024 Dec 12;11(1):e41147. doi: 10.1016/j.heliyon.2024.e41147. eCollection 2025 Jan 15.
Sleep stages classification one of the essential factors concerning sleep disorder diagnoses, which can contribute to many functional disease treatments or prevent the primary cognitive risks in daily activities. In this study, A novel method of mapping EEG signals to music is proposed to classify sleep stages. A total of 4.752 selected 1-min sleep records extracted from the capsleep database are applied as the statistical population for this assessment. In this process, first, the tempo and scale parameters are extracted from the signal according to the rules of music, and next by applying them and changing the dominant frequency of the pre-processed single-channel EEG signal, a sequence of musical notes is produced. A total of 19 features are extracted from the sequence of notes and fed into feature reduction algorithms; the selected features are applied to a two-stage classification structure: 1) the classification of 5 classes (merging S1 and REM-S2-S3-S4-W) is made with an accuracy of 89.5 % (Cap sleep database), 85.9 % (Sleep-EDF database), 86.5 % (Sleep-EDF expanded database), and 2) the classification of 2 classes (S1 vs. REM) is made with an accuracy of 90.1 % (Cap sleep database),88.9 % (Sleep-EDF database), 90.1 % (Sleep-EDF expanded database). The overall percentage of correct classification for 6 sleep stages are 88.13 %, 84.3 % and 86.1 % for those databases, respectively. The other objective of this study is to present a new single-channel EEG sonification method, The classification accuracy obtained is higher or comparable to contemporary methods. This shows the efficiency of our proposed method.
睡眠阶段分类是睡眠障碍诊断的关键因素之一，它有助于多种功能性疾病的治疗，或预防日常活动中的主要认知风险。在本研究中，提出了一种将脑电信号映射到音乐的新方法来对睡眠阶段进行分类。从capsleep数据库中选取的4752条1分钟睡眠记录被用作本次评估的统计总体。在此过程中，首先根据音乐规则从信号中提取节奏和音阶参数，然后通过应用这些参数并改变预处理后的单通道脑电信号的主频，生成一系列音符。从音符序列中提取了总共19个特征，并将其输入到特征约简算法中；选取的特征被应用于一个两阶段分类结构：1）对5个类别（合并S1和REM - S2 - S3 - S4 - W）进行分类，在Cap睡眠数据库中的准确率为89.5%，在Sleep - EDF数据库中的准确率为85.9%，在Sleep - EDF扩展数据库中的准确率为86.5%；2）对2个类别（S1与REM）进行分类，在Cap睡眠数据库中的准确率为90.1%，在Sleep - EDF数据库中的准确率为88.9%，在Sleep - EDF扩展数据库中的准确率为90.1%。对于这三个数据库，6个睡眠阶段的总体正确分类百分比分别为88.13%、84.3%和86.1%。本研究的另一个目标是提出一种新的单通道脑电信号听觉化方法，所获得的分类准确率高于或与当代方法相当。这表明了我们所提出方法的有效性。
