Chih En Kuo

A transition-constrained discrete hidden Markov model for automatic sleep staging

BackgroundApproximately one-third of the human lifespan is spent sleeping. To diagnose sleep problems, all-night polysomnographic (PSG) recordings including electroencephalograms (EEGs), electrooculograms (EOGs) and electromyograms (EMGs), are usually acquired from the patient and scored by a well-trained expert according to Rechtschaffen & Kales (R&K) rules. Visual sleep scoring is a time-consuming and subjective process. Therefore, the development of an automatic sleep scoring method is desirable.MethodThe EEG, EOG and EMG signals from twenty subjects were measured. In addition to selecting sleep characteristics based on the 1968 R&K rules, features utilized in other research were collected. Thirteen features were utilized including temporal and spectrum analyses of the EEG, EOG and EMG signals, and a total of 158 hours of sleep data were recorded. Ten subjects were used to train the Discrete Hidden Markov Model (DHMM), and the remaining ten were tested by the trained DHMM for recognition. Furthermore, the 2-fold cross validation was performed during this experiment.ResultsOverall agreement between the expert and the results presented is 85.29%. With the exception of S1, the sensitivities of each stage were more than 81%. The most accurate stage was SWS (94.9%), and the least-accurately classified stage was S1 (<34%). In the majority of cases, S1 was classified as Wake (21%), S2 (33%) or REM sleep (12%), consistent with previous studies. However, the total time of S1 in the 20 all-night sleep recordings was less than 4%.ConclusionThe results of the experiments demonstrate that the proposed method significantly enhances the recognition rate when compared with prior studies.

A rule-based automatic sleep staging method

In this paper, a rule-based automatic sleep staging method was proposed. Twelve features, including temporal and spectrum analyses of the EEG, EOG, and EMG signals, were utilized. Normalization was applied to each feature to reduce the effect of individual variability. A hierarchical decision tree, with fourteen rules, was constructed for sleep stage classification. Finally, a smoothing process considering the temporal contextual information was applied for the continuity. The average accuracy and kappa coefficient of the proposed method applied to the all night polysomnography (PSG) of twenty subjects compared with the manual scorings reached 86.5% and 0.78, respectively. This method can assist the clinical staff reduce the time required for sleep scoring in the future.

A fuzzy inference system for sleep staging

In this paper, a fuzzy inference system for sleep staging was developed. Nine input variables including temporal and spectrum analyses of the EEG, EOG, and EMG signals were extracted and normalization was applied to these variables to reduce the effect of individual variability. A fuzzy inference system contains fourteen fuzzy rules was designed to classify the 30-s sleep epochs as five sleep stages. Finally, a smoothing process was applied to the scoring results for fine-tuning. The average accuracy of the proposed method applied to 16 all-night polysomnography (PSG) recordings compared with the manual scorings can reach 87 %. This method can integrate with various PSG systems for sleep monitoring in clinical or homecare applications.

Combination of Expert Knowledge and a Genetic Fuzzy Inference System for Automatic Sleep Staging

OBJECTIVE In this paper, the genetic fuzzy inference system based on expert knowledge for automatic sleep staging was developed. METHODS Eight features, including temporal and spectrum analyses of the EEG and EMG signals, were utilized as the input variables. The fuzzy rules and the fuzzy sets were constructed based on expert knowledge and the distributions of feature values at different sleep stages. Three experiments were designed to develop and evaluate the proposed system. PSGs of 32 healthy subjects and 16 subjects with insomnia were included in the experiment to develop and evaluate the proposed method. Finally, a complete sleep scoring system integrating two fuzzy inference models with robust performance on various subject groups is developed. RESULTS The overall agreement and kappa coefficient of this integrated system applied to PSG data from 8 subjects with good sleep efficiency, 8 subjects with poor sleep efficiency and 8 subjects with insomnia were 86.44 % and 0.81, respectively. CONCLUSION Due to the high performance of the proposed system, it is expected to integrate the proposed method with various PSG systems for sleep monitoring in clinical or homecare applications in the future. SIGNIFICANCE An automatic sleep staging system integrating knowledge of the experts in scoring of PSG data and the elasticity of fuzzy systems in reasoning and decision making is proposed and the robustness and clinical applicability of the proposed method is demonstrated on data from healthy subjects and subjects with insomnia.Objective: In this paper, the genetic fuzzy inference system based on expert knowledge for automatic sleep staging was developed. Methods: Eight features, including temporal and spectrum analyses of the EEG and EMG signals, were utilized as the input variables. The fuzzy rules and the fuzzy sets were constructed based on expert knowledge and the distributions of feature values at different sleep stages. Three experiments were designed to develop and evaluate the proposed system. PSGs of 32 healthy subjects and 16 subjects with insomnia were included in the experiment to develop and evaluate the proposed method. Finally, a complete sleep scoring system integrating two fuzzy inference models with robust performance on various subject groups is developed. Results: The overall agreement and kappa coefficient of this integrated system applied to PSG data from 8 subjects with good sleep efficiency, 8 subjects with poor sleep efficiency and 8 subjects with insomnia were 86.44% and 0.81, respectively. Conclusion: Due to the high performance of the proposed system, it is expected to integrate the proposed method with various PSG systems for sleep monitoring in clinical or homecare applications in the future. Significance: An automatic sleep staging system integrating knowledge of the experts in scoring of PSG data and the elasticity of fuzzy systems in reasoning and decision making is proposed and the robustness and clinical applicability of the proposed method is demonstrated on data from healthy subjects and subjects with insomnia.

Using off-the-shelf lossy compression for wireless home sleep staging

BACKGROUND Recently, there has been increasing interest in the development of wireless home sleep staging systems that allow the patient to be monitored remotely while remaining in the comfort of their home. However, transmitting large amount of Polysomnography (PSG) data over the Internet is an important issue needed to be considered. In this work, we aim to reduce the amount of PSG data which has to be transmitted or stored, while having as little impact as possible on the information in the signal relevant to classify sleep stages. NEW METHOD We examine the effects of off-the-shelf lossy compression on an all-night PSG dataset from 20 healthy subjects, in the context of automated sleep staging. The popular compression method Set Partitioning in Hierarchical Trees (SPIHT) was used, and a range of compression levels was selected in order to compress the signals with various degrees of loss. In addition, a rule-based automatic sleep staging method was used to automatically classify the sleep stages. RESULTS Considering the criteria of clinical usefulness, the experimental results show that the system can achieve more than 60% energy saving with a high accuracy (>84%) in classifying sleep stages by using a lossy compression algorithm like SPIHT. COMPARISON WITH EXISTING METHOD(S) As far as we know, our study is the first that focuses how much loss can be tolerated in compressing complex multi-channel PSG data for sleep analysis. CONCLUSIONS We demonstrate the feasibility of using lossy SPIHT compression for wireless home sleep staging.

An EOG-based automatic sleep scoring system and its related application in sleep environmental control

Human beings spend approximately one third of their lives sleeping. Conventionally, to evaluate a subjects sleep quality, all-night polysomnogram (PSG) readings are taken and scored by a well-trained expert. Unlike a bulky PSG or EEG recorder on the head, the development of an electrooculogram (EOG)-based automatic sleep-staging system will enable physiological computing systems (PhyCS) to progress toward easy sleep and comfortable monitoring. In this paper, an EOG-based sleep scoring system is proposed. EOG signals are also coupling some of sleep characteristics of EEG signals. Compared to PSG or EEG recordings, EOG has the advantage of easy placement, and can be operated by the user individually at home. The proposed method was found to be more than 83 % accurate when compared with the manual scorings applied to sixteen subjects. In addition to sleep-quality evaluation, the proposed system encompasses adaptive brightness control of light according to online monitoring of the users sleep stages. The experiments show that the EOG-based sleep scoring system is a practicable solution for homecare and sleep monitoring due to the advantages of comfortable recording and accurate sleep staging.

Evaluating the Sleep Quality Using Multiscale Entropy Analysis

Sleep diseases, such as insomnia and obstructive sleep apnea, seriously affect patients’ quality of life. For diagnosis, polysomnographic (PSG) recordings are most usually taken to evaluate the sleep quality and efficiency. However, the large amount of wires connections for conventional PSG often cause sleep interference and not self-applicable. In this study, a complexity-measure-based method for evaluating the sleep quality was proposed. We utilize multiscale entropy (MSE) to analyze the 32 all-night sleep polysomnographic (PSG) recordings from 32 adults. The range of the subjects’ sleep efficiency was from 56% to 97%. Half of the subjects’ sleep efficiencies were equal or higher than to 85% (good sleep) and the other half were lower than 85% (poor sleep). The result shows that the averaged MSE values of poor sleep efficiency group are higher than good sleep efficiency group in each scale factor. This means that the complexity of sleep EEG of poor sleep efficiency group is higher than good sleep efficiency group. This finding may be used to quickly distinguish the subject’ sleep efficiency is good or poor.