An adaptive filtering approach using supervised SSA for identification of sleep stages from EEG

Purpose: Sleep is a complex physiological state and an indicator of the changes in the brain function similar to those occurring in many psychiatric and neurological conditions. Since visual sleep scoring consuming process, automatic sleep staging methods, also called scoring, hold promise in diagnosing alterations in the sleep process and the sleep EEG more effectively. Method: In this paper, a supervised approach for sleep scoring from single channel EEG signals is proposed. First, a supervised singular spectrum analysis (SSA) which is a subspace based method is used to extract the desired signal for each stage of sleep. Then, two recursive least squares (RLS) adaptive filters are trained and used to identify first and deep sleep stages. Result: The proposed system which can be considered as a filter bank for separating multiple signal subbands is tested using real EEG where the results verify the accuracy of the proposed method. Conclusion: The overall result show the effectiveness of algorithm for detection of sleep stages from EEG signals often characterised by a sharp increase in delta and a rapid decrease in alpha as sleep deepens

Improving time–frequency domain sleep EEG classification via singular spectrum analysis

Background: Manual sleep scoring is deemed to be tedious and time consuming. Even among automatic methods such as Time-Frequency (T-F) representations, there is still room for more improvement. New method: To optimise the efficiency of T-F domain analysis of sleep electroencephalography (EEG) a novel approach for automatically identifying the brain waves, sleep spindles, and K-complexes from the sleep EEG signals is proposed. The proposed method is based on singular spectrum analysis (SSA). The single-channel EEG signal (C3-A2) is initially decomposed and then the desired components are automatically separated. In addition, the noise is removed to enhance the discrimination ability of features. The obtained T-F features after preprocessing stage are classified using a multi-class support vector machines (SVM) and used for the identification of four sleep stages over three sleep types. Furthermore, to emphasize on the usefulness of the proposed method the automatically-determined spindles are parameterised to discriminate three sleep types. Result: The four sleep stages are classified through SVM twice: with and without preprocessing stage. The mean accuracy, sensitivity, and specificity for before the preprocessing stage are: 71.5 ± 0.11%, 56.1 ± 0.09% and 86.8 ± 0.04% respectively. However, these values increase significantly to 83.6 ± 0.07%, 70.6 ± 0.14% and 90.8 ± 0.03% after applying SSA. Comparison with existing method: The new T-F representation has been compared with the existing benchmarks. Our results prove that, the proposed method well outperforms the previous methods in terms of identification and representation of sleep stages. Conclusion: Experimental results confirm the performance improvement in terms of classification rate and also representative T-F domain

Information reliability in complex multitask networks

The emergence of distributed and complex networks has altered the field of information and data processing in the past few years. In distributed networks, the connected neighboring nodes can cooperate and share information with each other in order to solve particular tasks. However, in many applications the agents might be reluctant to share their true data with all their neighbors due to privacy and security constraints. In this paper, we study the performance of multitask distributed networks where sharing genuine information is subject to a cost. We formulate an information credibility model which results in the probability of sharing genuine information at each time instant according to the cost. Each agent then shares its true information with only a subset of its neighbors while sending fabricated data to the rest according to this probability. This behavior can affect the performance of the whole network in an adverse manner especially in cases where the cost is high. To overcome this problem, we propose an adaptive reputation protocol which enables the agents to evaluate the behavior of their neighbors over time and select the most reputable subset of neighbors to share genuine information with. We provide an extensive simulation-based analysis to compare the performance of the proposed method with several other distributed learning strategies. The results show that the proposed method outperforms the other learning strategies and enables the network to have a superior performance especially when the cost of sharing genuine information is high

Performance analysis of incremental LMS over flat fading channels

We study the effect of fading in the communication channels between sensor nodes on the performance of the incremental least mean square (ILMS) algorithm, and derive steady state performance metrics, including the mean-square deviation (MSD), excess mean-square error (EMSE) and mean-square error (MSE). We obtain conditions for mean convergence of the ILMS algorithm, and show that in the presence of fading channels, the ILMS algorithm is asymptotically biased. Furthermore, the dynamic range for mean stability depends only on the mean channel gain, and under simplifying technical assumptions, we show that the MSD, EMSE and MSE are non-decreasing functions of the channel gain variances, with mean-square convergence to the steady states possible only if the channel gain variances are limited. We derive sufficient conditions to ensure mean-square convergence, and verify our results through simulations

Quaternion singular spectrum analysis of electroencephalogram With application in sleep analysis

A novel quaternion-valued singular spectrum analysis (SSA) is introduced for multichannel analysis of electroencephalogram (EEG). The analysis of EEG typically requires the decomposition of data channels into meaningful components despite the notoriously noisy nature of EEG - which is the aim of SSA. However, the singular value decomposition involved in SSA implies the strict orthogonality of the decomposed components, which may not reflect accurately the sources which exhibit similar neural activities. To allow for the modelling of such co-channel coupling, the quaternion domain is considered for the first time to formulate the SSA using the augmented statistics. As an application, we demonstrate how the augmented quaternion-valued SSA (AQSSA) can be used to extract the sources, even at a signal-to-noise ratio as low as -10 dB. To illustrate the usefulness of our quaternion-valued SSA in a rehabilitation setting, we employ the proposed SSA for sleep analysis to extract statistical descriptors for five-stage classification (Awake, N1, N2, N3 and REM). The level of agreement using these descriptors was 74% as quantified by the Cohen's kappa

Incorporating negentropy in saliency-based search free car number plate localization

License plate localization algorithms aim to detect license plates within the scene. In this paper, a new algorithm is discussed where the necessary conditions are imposed into the saliency detection equations. Measures of distance between probability distributions such as negentropy finds the candidate license plates in the image and the Bayesian methodology exploits the a priori information to estimate the highest probability for each candidate. The proposed algorithm has been tested for three datasets, consisting of gray-scale and color images. A detection accuracy of 96% and an average execution time of 80 ms for the first dataset are the marked outcomes. The proposed method outperforms most of the state-of-the-art techniques and it is suitable to use in real-time ALPR applications

Robust search-free car number plate localization incorporating hierarchical saliency

There are two major shortcomings associated with presently implemented automatic license plate recognition (ALPR) systems: first, processing images with complex background is time-consuming and second, the results are not sufficiently accurate. To overcome these problems and also to achieve a robust recognition of multiple car number plates, saliency detection based on the ALPR system is used in this paper and also an improved and more effective definition of saliency is presented. In this new approach, the notion of the directionality of the edges using Gabor filtering and the detection of the patterns of numbers using L1 -norm have been added to the traditional saliency detection method. The proposed algorithm was tested on 660 images; some consisting of two or more cars. A detection accuracy of 94.77% and an average execution time of 40 ms for 600 × 800 images are the marked outcomes. The proposed SB-ALPR method outperforms most of the state of the art techniques in terms of execution time and accuracy, and can be used in real-time applications. Also, unlike some recently introduced saliency-based ALPR methods, our two-stage saliency detection approach exploits smaller numbers of sample sizes to reduce the computation cost

Tensor based singular spectrum analysis for automatic scoring of sleep EEG

A new supervised approach for decomposition of single channel signal mixtures is introduced in this paper. The performance of the traditional singular spectrum analysis (SSA) algorithm is significantly improved by applying tensor decomposition instead of traditional singular value decomposition (SVD). As another contribution to this subspace analysis method, the inherent frequency diversity of the data has been effectively exploited to highlight the subspace of interest. As an important application, sleep EEG has been analysed and the stages of sleep for the subjects in normal condition, with sleep restriction, and with sleep extension have been accurately estimated and compared with the results of sleep scoring by clinical experts

Novel quaternion matrix factorisations

The recent introduction of η-Hermitian matrices A = AηH has opened a new avenue of research in quaternion signal processing. However, the exploitation of this matrix structure has been limited, perhaps due to the lack of joint diagonalisation methodologies of these matrices. As such, we propose novel decompositions of η- Hermitian matrices to address this shortcoming in the literature. As an application, we consider a blind source separation problem in the form of an Alamouti-based communication system. Simulation studies demonstrate the effectiveness of our proposed joint diagonalisation technique and indicate that our approach is particularly useful when the sources are correlated