Samaneh Kouchaki

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 algorithm is significantly improved by applying tensor decomposition instead of traditional singular value decomposition. 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 electroencephalogram has been analyzed 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.

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 Cohens kappa.

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 (SVMs) and used for the identification of four sleep stages over three sleep types. Furthermore, to emphasise 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.

Multiview classification and dimensionality reduction of scalp and intracranial EEG data through tensor factorisation

Electroencephalography (EEG) signals arise as mixtures of various neural processes which occur in particular spatial, frequency, and temporal brain locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work, we apply tensor factorisation to a set of EEG data from a group of epileptic patients and factorise the data into three modes; space, time, and frequency with each mode containing a number of components or signatures. We train separate classifiers on various feature sets corresponding to complementary combinations of those modes and components and test the classification accuracy for each set. The relative influence on the classification accuracy of the respective spatial, temporal, or frequency signatures can then be analysed and useful interpretations can be made. Additionaly, we show that through tensor factorisation we can perform dimensionality reduction by evaluating the classification performance with regards to the number of components in each mode and also by rejecting components with insignificant contribution to the classification accuracy.

Local binary patterns as a feature descriptor in alignment-free visualisation of metagenomic data

Shotgun sequencing has facilitated the analysis of complex microbial communities. However, clustering and visualising these communities without prior taxonomic information is a major challenge. Feature descriptor methods can be utilised to extract these taxonomic relations from the data. Here, we present a novel approach consisting of local binary patterns (LBP) coupled with randomised singular value decomposition (RSVD) and Barnes-Hut t-stochastic neighbor embedding (BH-tSNE) to highlight the underlying taxonomic structure of the metagenomic data. The effectiveness of our approach is demonstrated using several simulated and a real metagenomic datasets.

Detection of Parkinson's tremor from EMG signals; a singular spectrum analysis approach

A robust constrained complex singular spectrum analysis approach for the assessment of Parkinsons tremor by separation of electromyograms (EMGs) is presented in this paper. This approach exploits the expected EMG characteristics of tremor within a subspace of the single channel surface EMG signal measured during the prescribed hand movement including flexion and extension and decomposed using singular spectrum analysis. The results are validated using the tremor signals simultaneously recorded using motion sensors.

Tensor based singular spectrum analysis for nonstationary source separation

Tensor based singular spectrum analysis (SSA) has been introduced as an extension of traditional singular value decomposition (SVD) based SSA. In the SSA decomposition stage PARAFAC tensor factorization has been employed. Using tensor factorization methods enable SSA to perform much better in nonstationary and underdetermined cases. The results of applying the proposed method to both synthetic and real data show that this system outperforms the original SSA, when used for single channel data decomposition in nonstationary and underdetermined source separation.

Tensor factorisation for narrowband single channel source decomposition

Abstract In many subspace signal decomposition methods such as principal component analysis (PCA) or its extension, singular spectrum analysis (SSA), particularly meant for processing of single channel signals, there is need for a robust determination and validation of the number of sources. Here, we attempt to find a relation between the number of sources within single channel mixtures and the rank of a symmetric tensor constructed from such mixtures by adjusting the embedding dimension. This leads to a new approach for decomposition of single channel mixtures using tensor factorisation. Consequently, the effect of model order is analysed for simulated narrowband data. The inherent frequency diversity of the time series has also been effectively exploited in selection of the desired subspace. The proposed method has been applied to both simulated and real data. The results have been discussed and compared with those of a number of benchmark algorithms.

Metagenomic Binning through Multi-resolution Genomic Binary Patterns

Motivation High-throughput sequencing has facilitated the analysis of complex microbial communities. Consequently, an enormous number of sequences have been generated containing various regions of bacterial and viral genomes. Image processing offers a rich source of descriptors for data analysis. Here, we introduce a feature space called multi-resolution local binary patterns (MLBP) from image processing as a feature descriptor to extract local ‘texture’ changes from nucleotide sequences. We demonstrate its applicability to the alignmentfree binning of metagenomic data. Results The effectiveness of our approach is tested using both simulated and real human gut microbial communities. We compared the performance of our method with several existing techniques that are based on k-mer frequency to show it outperforms existing techniques. In addition, we provide a time-series study of the abundance pattern of each bin to help refine the formed clusters automatically and to find relations that may exist among the clusters. Although the main aim is to introduce the use of genomic signatures using an alternative feature space (MLBP), our results show its application to the analysis of contigs from a metagenomic study. Availability The source code for our Multi-resolution Genomic Binary Patterns method can be found at https://github.com/skouchaki/MrGBP

Complex Tensor based Blind Source Separation of EEG for Tracking P300 Subcomponents

Complex tensor factorisation of correlated brain sources is addressed in this paper. The electrical brain responses due to motory, sensory, or cognitive stimuli, i.e. event related potentials (ERPs), particularly P300, have been used for cognitive information processing. P300 has two subcomponents, P3a and P3b which are correlated and therefore, the traditional blind source separation approaches cannot solve the problem. In this work, a complex-valued tensor factorisation of electroencephalography (EEG) signals is introduced with the aim of separating P300 subcomponents. The proposed method uses complex-valued statistics to exploit the data correlation. In this way, the variations of P3a and p3b can be tracked for the assessment of the brain state. The results of this work will be compared with those of spatial principal component analysis (SPCA) method.