Signal processing techniques for extracting signals with periodic structure : applications to biomedical signals

In this dissertation some advanced methods for extracting sources from single and multichannel data are developed and utilized in biomedical applications. It is assumed that the sources of interest have periodic structure and therefore, the periodicity is exploited in various forms. The proposed methods can even be used for the cases where the signals have hidden periodicities, i.e., the periodic behaviour is not detectable from their time representation or even Fourier transform of the signal. For the case of single channel recordings a method based on singular spectrum anal ysis (SSA) of the signal is proposed. The proposed method is utilized in localizing heart sounds in respiratory signals, which is an essential pre-processing step in most of the heart sound cancellation methods. Artificially mixed and real respiratory signals are used for evaluating the method. It is shown that the performance of the proposed method is superior to those of the other methods in terms of false detection. More over, the execution time is significantly lower than that of the method ranked second in performance. For multichannel data, the problem is tackled using two approaches. First, it is assumed that the sources are periodic and the statistical characteristics of periodic sources are exploited in developing a method to effectively choose the appropriate delays in which the diagonalization takes place. In the second approach it is assumed that the sources of interest are cyclostationary. Necessary and sufficient conditions for extractability of the sources are mathematically proved and the extraction algorithms are proposed. Ballistocardiogram (BCG) artifact is considered as the sum of a number of independent cyclostationary components having the same cycle frequency. The proposed method, called cyclostationary source extraction (CSE), is able to extract these components without much destructive effect on the background electroencephalogram (EEG

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

© 2015 IEEE. 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

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

Detecting phase synchronization in coupled oscillators by combining multivariate singular spectrum analysis and fast factorization of structured matrices

It is shown that a fast reliable block Fourier algorithm for the factorization of structured matrices improves computational efficiency of known method for detecting phase synchronization in a large system of coupled oscillators, based on multivariate singular spectrum analysis. In this paper, a novel algorithm for the detection of cluster synchronization in a system of coupled oscillators is proposed. The block Toeplitz covariance matrix of the total trajectory matrix is efficiently block-diagonalized by means of the Fast Fourier Transform by embedding it first into a block circulant matrix. The synchronization structure of the underlying multivariate data set is defined based on the 2D spatiotemporal eigenvalue spectrum. The benefits of the proposed method are illustrated by simulations of the phase synchronization effects in a chain of coupled chaotic Rössler oscillators and using multichannel electroencephalogram (EEG) recordings from epilepsy patients

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

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

IMPROVING THE QUALITY, ANALYSIS AND INTERPRETATION OF BODY SOUNDS ACQUIRED IN CHALLENGING CLINICAL SETTINGS

Despite advances in medicine and technology, Acute Lower Respiratory Diseases are a leading cause of sickness and mortality worldwide, highly affecting countries where access to appropriate medical technology and expertise is scarce. Chest auscultation provides a low-cost, non-invasive, widely available tool for the examination of pulmonary health. Despite universal adoption, its use is riddled by a number of issues including subjectivity in interpretation and vulnerability to ambient noise, limiting its diagnostic capability. Digital auscultation and computerized methods come as a natural aid towards overcoming such imposed limitations. Focused on the challenges, we address the demanding real-life scenario of pediatric lung auscultation in busy clinical settings. Two major objectives lead to our contributions: 1) Can we improve the quality of the delicate auscultated sounds and reduce unwanted noise contamination; 2) Can we augment the screening capabilities of current stethoscopes using computerized lung sound analysis to capture the presence of abnormal breaths, and can we standardize findings. To address the first objective, we developed an adaptive noise suppression scheme that tackles contamination coming from a variety of sources, including subject-centric and electronic artifacts, and environmental noise. The proposed method was validated using objective and subjective measures including an expert reviewer panel and objective signal quality metrics. Results revealed the ability and superiority of the proposed method to i) suppress unwanted noise when compared to state-of-the-art technology, and ii) faithfully maintain the signature of the delicate body sounds. The second objective was addressed by exploring appropriate feature representations that capture distinct characteristics of body sounds. A biomimetic approach was employed, and the acoustic signal was projected onto high-dimensional spaces spanning time, frequency, temporal dynamics and spectral modulations. Trained classifiers produced localized decisions on these breath content features, indicating lung diseases. Unlike existing literature, our proposed scheme is further able to combine and integrate the localized decisions into individual, patient-level evaluation. A large corpus of annotated patient data was used to validate our approach, demonstrating the superiority of the proposed features and patient evaluation scheme. Overall findings indicate that improved accessible auscultation care is possible, towards creating affordable health care solutions with worldwide impact

A Time-Frequency approach for EEG signal segmentation

The record of human brain neural activities, namely electroencephalogram (EEG), is generally known as a non-stationary and nonlinear signal. In many applications, it is useful to divide the EEGs into segments within which the signals can be considered stationary. Combination of empirical mode decomposition (EMD) and Hilbert transform, called Hilbert-Huang transform (HHT), is a new and powerful tool in signal processing. Unlike traditional time-frequency approaches, HHT exploits the nonlinearity of the medium and non-stationarity of the EEG signals. In addition, we use singular spectrum analysis (SSA) in the pre-processing step as an effective noise removal approach. By using synthetic and real EEG signals, the proposed method is compared with wavelet generalized likelihood ratio (WGLR) as a well-known signal segmentation method. The simulation results indicate the performance superiority of the proposed method