System Identification I

The existing sleep stages classification methods are mainly based on time or frequency features. This paper classifies the sleep stages based on graph domain features from a single-channel electroencephalogram (EEG) signal. First, each epoch (30 s) EEG signal is mapped into a visibility graph (VG) and a horizontal VG (HVG). Second, a difference VG (DVG) is obtained by subtracting the edges set of the HVG from the edges set of the VG to extract essential degree sequences and to detect the gait-related movement artifact recordings. The mean degrees (MDs) and degree distributions (DDs) P (k) on HVGs and DVGs are analyzed epoch-by-epoch from 14,963 segments of EEG signals. Then, the MDs of each DVG and HVG and seven distinguishable DD values of P (k) from each DVG are extracted. Finally, nine extracted features are forwarded to a support vector machine to classify the sleep stages into two, three, four, five, and six states. The accuracy and kappa coefficients of six-state classification are 87.5% and 0.81, respectively. It was found that the MDs of the VGs on the deep sleep stage are higher than those on the awake and light sleep stages, and the MDs of the HVGs are just the reverse.

Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal

A convolutional neural network for sleep stage scoring from raw single-channel EEG

We developed a machine learning methodology for automatic sleep stage scoring. Our time-frequency analysis-based feature extraction is fine-tuned to capture sleep stage-specific signal features as described in the American Academy of Sleep Medicine manual that the human experts follow. We used ensemble learning with an ensemble of stacked sparse autoencoders for classifying the sleep stages. We used class-balanced random sampling across sleep stages for each model in the ensemble to avoid skewed performance in favor of the most represented sleep stages, and addressed the problem of misclassification errors due to class imbalance while significantly improving worst-stage classification. We used an openly available dataset from 20 healthy young adults for evaluation. We used a single channel of EEG from this dataset, which makes our method a suitable candidate for longitudinal monitoring using wearable EEG in real-world settings. Our method has both high overall accuracy (78%, range 75-80%), and high mean [Formula: see text]-score (84%, range 82-86%) and mean accuracy across individual sleep stages (86%, range 84-88%) over all subjects. The performance of our method appears to be uncorrelated with the sleep efficiency and percentage of transitional epochs in each recording.

/pdf/automatic-sleep-stage-scoring-using-time-frequency-analysis-4002uq9vvv.pdf

Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders

An ensemble system for automatic sleep stage classification using single channel EEG signal

In this paper, we propose an automatic sleep-scoring method combining multiscale entropy (MSE) and autoregressive (AR) models for single-channel EEG and to assess the performance of the method comparatively with manual scoring based on full polysomnograms. This is the first time that MSE has ever been applied to sleep scoring. All-night polysomnograms from 20 healthy individuals were scored using the Rechtschaffen and Kales rules. The developed method analyzed the EEG signals of C3-A2 for sleep staging. The results of automatic and manual scorings were compared on an epoch-by-epoch basis. A total of 8480 30-s sleep EEG epochs were measured and used for performance evaluation. The epoch-by-epoch comparison was made by classifying the EEG epochs into five states (Wake/REM/S1/S2/SWS) by the proposed method and manual scoring. The overall sensitivity and kappa coefficient of MSE alone are 76.9% and 0.65, respectively. Moreover, the overall sensitivity and kappa coefficient of our proposed method of integrating MSE, AR models, and a smoothing process can reach the sensitivity level of 88.1% and 0.81, respectively. Our results show that MSE is a useful and representative feature for sleep staging. It has high accuracy and good home-care applicability because a single EEG channel is used for sleep staging.

Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models

Health care researchers have recently developed several home-based sleep monitoring systems and mobile applications that support healthy sleep by monitoring a user’s sleep environment, activities, or sleep quality This study describes the design and evaluation of an electrooculogram (EOG)-based system for an automatic sleep monitoring Compared with polysomnogram or electroencephalogram recordings, EOG has the advantage of easy placement and can be operated by the users themselves We also design an intelligent eye mask that is user friendly for measuring sleep stage and quality Two user experiments were carried out to demonstrate that the proposed system produces valid measurements of sleep stage and sleep quality and has good usability and reliability while not disturbing sleep significantly These findings suggest that our system can also be applied to long-term sleep monitoring or sleep environment control to improve the user’s sleep quality and make sleep more comfortable

Development of an EOG-Based Automatic Sleep-Monitoring Eye Mask

Atrial fibrillation (AF) is the most frequent cardiac arrhythmia seen in clinical practice. Several therapeutical approaches have been developed to terminate the AF and the effects are evaluated by the reduction of the wavelet number after the treatments. Most of the previous studies focus on modeling and analysis of the mechanism, and the characteristic of AF. But no one discusses about the prediction of the result after the drug treatment. This paper is the first study to predict whether the drug treatment for AF is active or not. In this paper, the linear autoregressive model with exogenous inputs (ARX) that models the system output-input relationship by solving linear regression equations with least-squares method was developed and applied to estimate the effects of pharmacological therapy on AF. Recordings (224-site bipolar recordings) of plaque electrode arrays placed on the right and left atria of pigs with sustained AF induced by rapid atrial pacing were used to train and test the ARX models. The cardiac mapping data from 12 pigs treated with intravenous administration of antiarrhythmia drug, propafenone (PPF), or dl-sotalol (STL) were evaluated. The recordings of cardiac activity before the drug treatment were input to the model and the model output reported the estimated wavelet number of atria after the drug treatment. The results show that the predicting accuracy rate corresponding to the PPF and STL treatments was 100% and 92%, respectively. It is expected that the developed ARX model can be further extended to assist the clinical staffs to choose the effective treatments for the AF patients in the future.

https://ir.nctu.edu.tw:443/bitstream/11536/22428/1/000321142500018.pdf

Estimation and Prediction of Drug Therapy on the Termination of Atrial Fibrillation by Autoregressive Model With Exogenous Inputs

http://ieeexplore.ieee.org/iel5/5678855/5685355/05685397.pdf

Estimation and Prediction of Propafenone on The Termination of Atrial Fibrillation by

Atrial fibrillation (AF) is the most frequent cardiac arrhythmia seen in clinical practice. Several therapeutical approaches have been developed to terminate the AF and the effects are evaluated by the reduction of the wavelet number after the treatments. In this paper, the state-space model was developed and applied to estimate the effects of pharmacological therapy on AF. Recordings (224-site bipolar recordings) of plaque electrode arrays placed on the right and left atria of pigs with sustained AF induced by rapid atrial-pacing were used to train and test the state-space models. The cardiac mapping data from five pigs treated with intravenous administration of antiarrhythmia drug, propafenone (PPF), were evaluated. The recordings of cardiac activity before the drug treatment were input to the model and the model output reported the estimated wavelet number of atria after the drug treatment. The results show that the predicting accuracy can reach 90%. It is expected that the developed state-space model can be further extended to assist the clinical staffs to estimate the effects of treatments for the AF patients in the future.

Chin-En Kuo

Papers

Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models

Development of an EOG-Based Automatic Sleep-Monitoring Eye Mask

Estimation and Prediction of Drug Therapy on the Termination of Atrial Fibrillation by Autoregressive Model With Exogenous Inputs

Estimation and Prediction of Propafenone on The Termination of Atrial Fibrillation by

Estimation and prediction of propafenone on the termination of atrial fibrillation by state-space models