Shirin Enshaeifar

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.

AN EIGEN-BASED APPROACH FOR COMPLEX-VALUED FORECASTING

Forecasting one step ahead is generally straightforward. Forecasting two steps ahead a little more challenging. Forecasting further into the horizon may require prior forecasted samples, as the availability of historical data may not be adequate to do so. It is in this motivational context that we proposed an eigen-based approach for complex-valued multiple-step ahead forecasting. Here we establish an augmented complex-domain singular spectrum analysis framework to perform prediction beyond 50 step ahead. It is shown that other prediction algorithms such as the least mean square, though useful and adaptive, cannot use the predicted samples to predict further. In some cases, they may diverge from the trend. Simulations on real-world data support our approach.

A regularised EEG informed Kalman filtering algorithm

Abstract The conventional Kalman filter assumes a constant process noise covariance according to the systems dynamics. However, in practice, the dynamics might alter and the initial model for the process noise may not be adequate to adapt to abrupt dynamics of the system. In this paper, we provide a novel informed Kalman filter (IKF) which is informed by an extrinsic data channel carrying information about the systems future state. Thus, each state can be represented with a corresponding process noise covariance, i.e. the Kalman gain is automatically adjusted according to the detected state. As a real-world application, we demonstrate for the first time how the analysis of electroencephalogram (EEG) can be used to predict the voluntary body movement and inform the tracking Kalman algorithm about a possible state transition. Furthermore, we provide a rigorous analysis to establish a relationship between the Kalman performance and the detection accuracy. Simulations on both synthetic and real-world data support our analysis.

A distributed in-network indexing mechanism for the Internet of Things

The current Web and data indexing and search mechanisms are mainly tailored to process text-based data and are limited in addressing the intrinsic characteristics of distributed, large-scale and dynamic Internet of Things (IoT) data networks. The IoT demands novel indexing solutions for large-scale data to create an ecosystem of system; however, IoT data are often numerical, multi-modal and heterogeneous. We propose a distributed and adaptable mechanism that allows indexing and discovery of real-world data in IoT networks. Comparing to the state-of-the-art approaches, our model does not require any prior knowledge about the data or their distributions. We address the problem of distributed, efficient indexing and discovery for voluminous IoT data by applying an unsupervised machine learning algorithm. The proposed solution aggregates and distributes the indexes in hierarchical networks. We have evaluated our distributed solution on a large-scale dataset, and the results show that our proposed indexing scheme is able to efficiently index and enable discovery of the IoT data with 71% to 92% better response time than a centralised approach.

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 diago-nalisation technique and indicate that our approach is particularly useful when the sources are correlated.

Classification of awake, REM, and NREM from EEG via singular spectrum analysis

In this study, a single-channel electroencephalography (EEG) analysis method has been proposed for automated 3-state-sleep classification to discriminate Awake, NREM (non-rapid eye movement) and REM (rapid eye movement). For this purpose, singular spectrum analysis (SSA) is applied to automatically extract four brain rhythms: delta, theta, alpha, and beta. These subbands are then used to generate the appropriate features for sleep classification using a multi class support vector machine (M-SVM). The proposed method provided 0.79 agreement between the manual and automatic scores.

Singular spectrum analysis for tracking of P300

In this work, we introduce a complex-valued singular spectrum analysis for the analysis of electroencephalogram (EEG), which typically exhibits noncircular probability distribution. To exploit such prior knowledge, our technique makes use of recent advances in complex-valued statistics to exploit the power difference or the correlation between the data channels, in contrast to current methods which cater only for the restrictive class of circular data. In particular, the principal component analysis-like technique was employed to detect the onset of P300, and tracked this event-related potential. In this way, the classification of EEG can be made possible to differentiate between a healthy subject and a schizophrenic patient. In particular, we illuminate how features such as P3a and P3b can be used to perform such classification.

Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques

The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients’ routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.

Quaternion Common Spatial Patterns

A novel quaternion-valued common spatial patterns (QCSP) algorithm is introduced to model co-channel coupling of multi-dimensional processes. To cater for the generality of quaternion-valued non-circular data, we propose a generalized QCSP (G-QCSP) which incorporates the information on power difference between the real and imaginary parts of data channels. As an application, we demonstrate how G-QCSP can be used to provide high classification rates, even at a signal-to-noise ratio (SNR) as low as −10 dB. To illustrate the usefulness of our method in EEG analysis, we employ G-QCSP to extract features for discriminating between imagery left and right hand movements. The classification accuracy using these features is 70%. Furthermore, the proposed method is used to distinguish between Parkinson’s disease (PD) patients and healthy control subjects, providing an accuracy of 87%.

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.