基于频域分析与统计计算的自动睡眠分期研究

睡眠是人体最重要的生理活动之一,睡眠质量的好坏与我们的生理和心理健康都密切相关。因此,睡眠监测,特别是自动睡眠分期的研究有着重要的价值。本文使用脑电(electroencephalogram,EEG)信号,提出一种基于频谱分析与统计计算的自动睡眠分期算法。首先,利用小波变换实现原始睡眠脑电信号的去噪。然后,分别使用傅里叶变换以及统计分析方法得到频域和时域的多模态特征。最后,构建支持向量机分类器并对EEG信号进行分期研究。以Sleep-EDF公开数据集作为样本测试,本文的自动睡眠分期算法平均分类准确率达到86.82%,与已有的分类算法相比,有显著的提升。     

基于CNN-LSTM网络的睡眠分期研究

睡眠分期是睡眠数据分析的基础,用自动标定方法来替代人工标定方法可以提高效率,结果也更为客观。不管是人工手动标定还是自动标定都是基于多导睡眠图(Polysomnography,PSG)。采用长短时记忆模型(LSTM-RNN)及长短时记忆模型与卷积神经网络(CNN)相结合的模型(CNN-LSTM)对三个通道信号(EEG、EOG、EMG)的组合进行自动睡眠分期研究。通过对9个受试数据进行分析,LSTM-RNN和CNN-LSTM分别达到了81.9%和83.1%的分类准确率。相对于LSTM-RNN模型,结合卷积神经网络的CNN-LSTM模型获得的分期准确率更高,平均准确率提高了1.2%。     

基于多层级卷积融合网络的自动睡眠分期方法

针对原始数据不均衡、多层级特征利用不充分等原因造成的基于脑电(EEG)信号分析的睡眠分期方法精度长期驻足不前的问题,提出了一种多层级卷积融合网络的睡眠分期方法,在以SMOTE算法对原始EEG数据进行均衡化处理,并构建时-空信息特征矩阵对网络的输入量进行二维化处理的基础上,通过搭建和优化不同深度的卷积网络,自动提取并融合多层级、多尺度的EEG信号睡眠特征,实现睡眠分期。实验结果表明：所提方法在Sleep-EDF数据集上的分期精度可达到92.35%,宏F1-score达到84.4%,分期精度最低的N1期F1-score可达到53.3%,睡眠分期性能优于其他深度学习模型。     

基于神经网络的睡眠分期处理算法研究

睡眠质量已成为临床医学和人类生活中普遍关心的问题,为了研究不同睡眠时期大脑的活动,并对整夜睡眠状况及睡眠质量进行分析和评价,为了完成自动识别,提出一种基于神经网络的睡眠分期方法并探讨其应用到实际中的可行性。以国际睡眠分期标准为基础,充分考虑EEG信号的各个特征参数,利用BP神经网络分类器设计了一种睡眠自动分期分析的方法。仿真结果表明,利用改进的BP算法消除了网络训练受样本次序影响的缺陷,总睡眠分期准确率达到79.2%。这种方法及选取的17个睡眠分期参数可为睡眠质量的评价提供途径,可把专家们从冗繁的判读整夜睡眠记录中解脱出来,以进行更深入地分析和研究。     

基于脑电和肌电多特征的自动睡眠分期方法

为实现准确的自动睡眠分期,且满足泛化能力的需求,基于脑电(EEG)和肌电(EMG)多特征,提出一种自动睡眠分期方法。以MIT-BIH多导睡眠数据库中样本的EEG和EMG为分析对象,采用离散小波变换对原始数据进行滤波预处理,提取EEG的α,β,θ,δ节律波和高频成分的能量比,利用样本熵算法提取EEG的非线性特征。将特征参数输入支持向量机分类器中进行样本训练与分类识别。实验结果表明,该方法的分期准确率可以达到92.94%,相比基于EEG的睡眠分期方法平均准确率提高3.96%,交叉验证平均准确率达82.68%,具有较好的泛化能力。     

基于谱带融合的EEG自动睡眠分期算法

由于当前数据集类别不均衡造成基于深度学习的睡眠阶段分期模型性能较差。论文提出了一种基于谱带融合数据增强的自动睡眠分期算法（EEGFusionNet,EFNet）,以解决上述问题提高模型性能。该算法首先对样本数据进行谱带融合的数据增强处理,通过由卷积神经网络与双向门控循环单元提取EEG中的时频特征与时序特征,将特征融合进行睡眠阶段分期。将Sleep EDF数据集中153名健康受试者的EEG信号作为本模型的样本数据进行训练,得到模型的准确率约为84.4%,kappa值为83.8%。与传统的基线模型相比,论文提出的基于谱带融合的睡眠分期模型在准确性和一致性方面具有显著提升。     

基于MSE-PCA的脑电睡眠分期方法研究

针对传统的自动睡眠分期准确率不足问题,提出一种将多尺度熵(MSE)和主成分分析(PCA)联合使用的自动睡眠分期方法。以8例受试者睡眠脑电(EEG)监测数据及专家人工分期结果作为样本,首先使用MSE表征受试者脑电信号不同睡眠期的非线性动力学特征;然后使用PCA的前两个主成分向量代替MSE特征进行降维,实现降低数据冗余的同时保留绝大多数EEG非线性特征;最终将新向量的特征参数输入到反馈神经网络(BPNN)分类器中实现MSE-PCA模型的脑电睡眠状态的自动识别分类。实验结果表明,自动分期准确率可达到87.9%,kappa系数0.77,该方法能提高脑电自动睡眠分期系统的准确率和稳定性。     

基于离散小波变换的脑电信号睡眠分期研究

提出基于脑电信号(EEG)的睡眠分期研究。利用离散小波变换(DWT)的db8小波分解得到的细节分量作为信号新的表达,把各个细节分量能量作为特征,建立带高斯径向基核函数(RBF)的非线性支持向量机(SVM)模型。研究发现,其对睡眠分期研究的方案是可行的,满足模型对泛化能力的要求。     

基于EOG的睡眠分期研究

随着模式识别技术的发展与应用,睡眠自动分期方法正在逐渐取代手动分期研究。文章使用深度置信网络(Deep Belief Network,DBN)和长短时记忆递归神经网络(Long Short-Term Memory Recurrent Neural Network,LSTM-RNN)这两种方法对眼电(Electrooculogram,EOG)通道的数据进行睡眠自动分期。LSTM-RNN方法(平均准确率83.4%)相对DBN(平均准确率75.6%)在基于眼电信号的睡眠分期问题上取得了更好的效果。     

基于蚁群优选的半监督主动协同睡眠分期方法研究

基于脑电信号（EEG）的睡眠分期对于睡眠疾病的检测、预防和治疗有着重要的意义。传统的分期方法在使用EEG信号进行分期时存在冗余特征、标记样本需求量高这两方面的不足,影响了该方法的应用范围。为了提高该方法的适用范围,采用一种蚁群算法与半监督学习算法结合的睡眠分期算法（ACOTSS）,根据对称KL距离筛选低置信度样本,利用主动学习策略和协同训练进行不同置信度样本的标注以提升低标注样本下的分类正确率。采用麻省理工的公开数据集验证算法的分期效果,结果表明ACOTSS算法在保证分期精度的同时,比ALKLSS和LS-SVM的分期性能分别提升了16.83%和8.59%,证明该算法在低标记样本下具备可行性。     

Automatic determination of sleep stage through bio-neurological signals contaminated with artifacts by a conditional probability of the knowledge base

In this study, an automatic sleep-stage determination system with the capacity for artifact detection was developed. The methodology was based on the conditional probability of the knowledge base of an expert visual inspection. Expert visual inspection was the manual scoring of sleep stages and artifacts by a qualified clinician. The knowledge base consisted of probability density functions of characteristic parameters for stages and artifacts. Automatic sleep-stage determination and artifact detection were carried out based on a value of conditional probability. The total overnight bioneurological signals under the usual recording conditions with the artifacts of four subjects were analyzed. The results of automatic sleep-stage determination showed a close agreement with the expert visual inspections. In addition, an artifact can be detected at the same time by using the same method. With the capacity for artifact detection, the proposed automatic sleep-stage determination system can be adapted for real clinical applications. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

An approach for automatic sleep stage scoring and apnea-hypopnea detection

In this article we present an application of data mining to the medical domain sleep research, an approach for automatic sleep stage scoring and apnea-hypopnea detection. By several combined techniques (Fourier and wavelet transform, derivative dynamic time warping, and waveform recognition), our approach extracts meaningful features (frequencies and special patterns like k-complexes and sleep spindles) from physiological recordings containing EEG, ECG, EOG and EMG data. Based on these pieces of information, an ensemble of decision trees is constructed using the principle of bagging, which classifies sleep epochs in their sleep stages according to the rules by Rechtschaffen and Kales and annotates occurrences of apnea-hypopnea (total or partial cessation of respiration). After that, casebased reasoning is applied in order to improve quality. We tested and evaluated our approach on several large public databases from PhysioBank, which showed an overall accuracy of 95.2% for sleep stage scoring and 94.5% for classifying minutes as apneic or non-apneic.     

Classification of sleep stages using class-dependent sequential feature selection and artificial neural network

Several studies have been conducted for automatic classification of sleep stages to ease time-consuming manual scoring process that can involve a high degree of experience and subjectivity. But none of them has found a practical usage in medical area so far because of their under acceptable success rates. In this study, a different classification scheme is proposed to increase the success rate in automatic sleep stage scoring in which sleep stages were classified as Awake, Non-REM1, Non-REM2, Non-REM3 and REM stages. Using EEG, EMG and EOG recordings of five healthy subjects, a modified version of sequential feature selection method was applied to the sleep epochs in class by class basis and different artificial neural network (ANN) architectures were trained for each class. That is to say, sleep stages were classified with five ANN architectures each of which uses different features and different network parameters for classification. The highest classification accuracy was obtained for REM sleep as 95.13 % in addition to the lowest classification accuracy of 86.42 % for Non-REM3 sleep. The overall accuracy, on the other hand, was recorded as 90.93 %, which is a comparatively good result when the other studies using all stages are taken into account.     

Detecting rapid eye movement sleep using a single EEG signal channel

Sleep stage scoring is generally determined in a polysomnographic (PSG) study where technologists use electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals to determine the sleep stages. Such a process is time consuming and labor intensive. To reduce the workload and to improve the sleep stage scoring performance of sleep experts, this paper introduces an intelligent rapid eye movement (REM) sleep detection method that requires only a single EEG channel. The proposed approach distinguishes itself from previous automatic sleep staging methods by introducing two sets of auxiliary features to help resolve the difficulties caused by interpersonal EEG signal differences. In addition to adopting conventional time and frequency domain features, two empirical rules are introduced to enhance REM detection performance based on sleep being a continuous process. The approach was tested with 779,661 epochs obtained from 947 overnight PSG studies. The REM sleep detection results show a kappa coefficient at 0.752, an accuracy level of 0.930, a sensitivity score of 0.814, and a positive predictive value of 0.775. The results also show that the performance of the approach varies with the ratio of REM sleep and the severity of sleep apnea of the subjects. The experimental results also show that it is possible to improve the performance of an automatic sleep staging method by tailoring it to subgroups of persons that have similar sleep architecture and clinical characteristics.     

An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder. The traditional diagnosis methods of the disorder are cumbersome and expensive. The ability to automatically identify OSA from electrocardiogram (ECG) recordings is important for clinical diagnosis and treatment. In this study, we proposed an expert system based on discrete wavelet transform (DWT), fast-Fourier transform (FFT) and least squares support vector machine (LS-SVM) for the automatic recognition of patients with OSA from nocturnal ECG recordings. Thirty ECG recordings collected from normal subjects and subjects with sleep apnea, each of approximately 8 h in duration, were used throughout the study. The proposed OSA recognition system comprises three stages. In the first stage, an algorithm based on DWT was used to analyze ECG recordings for the detection of heart rate variability (HRV) and ECG-derived respiration (EDR) changes. In the second stage, an FFT based power spectral density (PSD) method was used for feature extraction from HRV and EDR changes. Then, a hill-climbing feature selection algorithm was used to identify the best features that improve classification performance. In the third stage, the obtained features were used as input patterns of the LS-SVM classifier. Using the cross-validation method, the accuracy of the developed system was found to be 100% for using a subset of selected combination of HRV and EDR features. The results confirmed that the proposed expert system has potential for recognition of patients with suspected OSA by using ECG recordings.     

Neural Network Model: Application to Automatic Analysis of Human Sleep

We describe an approach to automatic all-night sleep analysis based on neural network models and simulated on a digital computer. First, automatic sleep stage scoring was performed using a multilayer feedforward network. Second, supervision of the automatic decision was achieved using ambiguity rejection and artifact rejection. Then, numerical analysis of sleep was carried out using all-night spectral analysis for the background activity of the EEG and sleep pattern detectors for the transient activity. Computerized analysis of sleep recordings may be considered as an essential tool to describe the sleep process and to reflect the dynamical organization of human sleep.     

Automated sleep stage scoring using hybrid rule- and case-based reasoning.

We propose an automated method for sleep stage scoring using hybrid rule- and case-based reasoning. The system first performs rule-based sleep stage scoring, according to the Rechtschaffen and Kale's sleep-scoring rule (1968), and then supplements the scoring with case-based reasoning. This method comprises signal processing unit, rule-based scoring unit, and case-based scoring unit. We applied this methodology to three recordings of normal sleep and three recordings of obstructive sleep apnea (OSA). Average agreement rate in normal recordings was 87.5% and case-based scoring enhanced the agreement rate by 5.6%. This architecture showed several advantages over the other analytical approaches in sleep scoring: high performance on sleep disordered recordings, the explanation facility, and the learning ability. The results suggest that combination of rule-based reasoning and case-based reasoning is promising for an automated sleep scoring and it is also considered to be a good model of the cognitive scoring process.     

基于CNN-BiLSTM的自动睡眠分期算法

睡眠分期是睡眠数据分析的基础,针对目前睡眠分期存在的依赖人工提取、人工判别效率低、自动睡眠分期准确率不高等问题,本文研究模型是基于卷积神经网络和双向长短时记忆神经网络2个深度学习神经网络相结合的,利用脑电信号来进行自动睡眠分期的模型方法.算法能提取得到原始脑电信号的梅尔频谱,利用卷积神经网络和双向长短时记忆神经网络进行...     

Automatic classification of sleep stages based on the time-frequency image of EEG signals

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner–Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals.     

Complex networks approach for EEG signal sleep stages classification

Sleep stage scoring is a challenging task. Most of existing sleep stage classification approaches rely on analysing electroencephalography (EEG) signals in time or frequency domain. A novel technique for EEG sleep stages classification is proposed in this paper. The statistical features and the similarities of complex networks are used to classify single channel EEG signals into six sleep stages. Firstly, each EEG segment of 30 s is divided into 75 sub-segments, and then different statistical features are extracted from each sub-segment. In this paper, feature extraction is important to reduce dimensionality of EEG data and the processing time in classification stage. Secondly, each vector of the extracted features, which represents one EEG segment, is transferred into a complex network. Thirdly, the similarity properties of the complex networks are extracted and classified into one of the six sleep stages using a k-means classifier. For further investigation, in the statistical features extraction phase two statistical features sets are tested and ranked based on the performance of the complex networks. To investigate the classification ability of complex networks combined with k-means, the extracted statistical features were also forwarded to a k-means and a support vector machine (SVM) for comparison. We also compare the proposed method with other existing methods in the literature. The experimental results show that the proposed method attains better classification results and a reasonable execution time compared with the SVM, k-means and the other existing methods. The research results in this paper indicate that the proposed method can assist neurologists and sleep specialists in diagnosing and monitoring sleep disorders.