In this paper, a new algorithm is proposed for artifact removing of sleep electroencephalogram (E... more In this paper, a new algorithm is proposed for artifact removing of sleep electroencephalogram (EEG) with application in sleep stage classification. Rather than other works which used artificial noise, in this study real EEG data contaminated with electro-oculogram (EOG) and electromyogram (EMG) are used for evaluating the proposed artifact removal algorithm's efficiency using classification accuracy. The artifact detection is performed by thresholding the EEG-EOG and EEG-EMG cross correlation coefficients. Then, the segments considered contaminated are denoised by normalized least-mean squares (NLMS) adaptive filtering technique. Using a single EEG channel, four sleep stages consisting of Awake, Stage1 + REM, Stage 2 and Slow Wave Stage (SWS) are classified. A wavelet packet (WP) based feature set together with artificial neural network (ANN) are deployed for sleep stage classification purpose. Simulation results show that artifact removed EEG allows a classification accuracy improvement of around 14%.
Combination approaches provide an interesting
way to improve adaptive filter performance. In this... more Combination approaches provide an interesting way to improve adaptive filter performance. In this paper, we use one of such approaches known as ‘convex combination of two transversal filters’ for speech enhancement. In addition, both fixed and variable step size cases are examined. As our simulation results show, the variable step size version of the algorithm outperforms the other case in a sense of convergence rate and steady-state error.
Sleep quality is one of the most important measures of healthy life, especially considering the h... more Sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using multi-channel recordings like poly-somnographic (PSG) signals is an effective way of assessing sleep quality. However, manual sleep stage classification is time-consuming, tedious and highly subjective. To overcome this, automatic sleep classification was proposed, in which pre-processing, feature extraction and classification are the three main steps. Since the classification accuracy is deeply affected by the features selection, in this paper several feature selection methods as well as rank aggregation methods are compared. Feature selection methods are evaluated by three criteria: accuracy, stability and similarity. For classification two different classifiers (k-nearest neighbor and multilayer feedforward neural network) were utilized. Simulation results show that MRMR-MID achieves highest classification performance while Fisher method provides the most stable rankings.
In this paper, a new algorithm is proposed for artifact removing of sleep electroencephalogram (E... more In this paper, a new algorithm is proposed for artifact removing of sleep electroencephalogram (EEG) with application in sleep stage classification. Rather than other works which used artificial noise, in this study real EEG data contaminated with electro-oculogram (EOG) and electromyogram (EMG) are used for evaluating the proposed artifact removal algorithm's efficiency using classification accuracy. The artifact detection is performed by thresholding the EEG-EOG and EEG-EMG cross correlation coefficients. Then, the segments considered contaminated are denoised by normalized least-mean squares (NLMS) adaptive filtering technique. Using a single EEG channel, four sleep stages consisting of Awake, Stage1 + REM, Stage 2 and Slow Wave Stage (SWS) are classified. A wavelet packet (WP) based feature set together with artificial neural network (ANN) are deployed for sleep stage classification purpose. Simulation results show that artifact removed EEG allows a classification accuracy improvement of around 14%.
Combination approaches provide an interesting
way to improve adaptive filter performance. In this... more Combination approaches provide an interesting way to improve adaptive filter performance. In this paper, we use one of such approaches known as ‘convex combination of two transversal filters’ for speech enhancement. In addition, both fixed and variable step size cases are examined. As our simulation results show, the variable step size version of the algorithm outperforms the other case in a sense of convergence rate and steady-state error.
Sleep quality is one of the most important measures of healthy life, especially considering the h... more Sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using multi-channel recordings like poly-somnographic (PSG) signals is an effective way of assessing sleep quality. However, manual sleep stage classification is time-consuming, tedious and highly subjective. To overcome this, automatic sleep classification was proposed, in which pre-processing, feature extraction and classification are the three main steps. Since the classification accuracy is deeply affected by the features selection, in this paper several feature selection methods as well as rank aggregation methods are compared. Feature selection methods are evaluated by three criteria: accuracy, stability and similarity. For classification two different classifiers (k-nearest neighbor and multilayer feedforward neural network) were utilized. Simulation results show that MRMR-MID achieves highest classification performance while Fisher method provides the most stable rankings.
Uploads
Papers by Ali Abdollahy
way to improve adaptive filter performance. In this
paper, we use one of such approaches known as ‘convex
combination of two transversal filters’ for speech
enhancement. In addition, both fixed and variable step
size cases are examined. As our simulation results show,
the variable step size version of the algorithm
outperforms the other case in a sense of convergence
rate and steady-state error.
way to improve adaptive filter performance. In this
paper, we use one of such approaches known as ‘convex
combination of two transversal filters’ for speech
enhancement. In addition, both fixed and variable step
size cases are examined. As our simulation results show,
the variable step size version of the algorithm
outperforms the other case in a sense of convergence
rate and steady-state error.