Publications
736 results found
Li Z, Xia Y, Pei W, et al., 2017, A DFT Enhanced Complex LMS for Digital Adaptive Spur Cancellation, 2017 22nd International Conference on Digital Signal Processing (DSP), Publisher: IEEE, ISSN: 1546-1874
Kappel SL, Looney D, Mandic DP, et al., 2017, Physiological artifacts in scalp EEG and ear-EEG., BioMedical Engineering OnLine, Vol: 16, ISSN: 1475-925X
BACKGROUND: A problem inherent to recording EEG is the interference arising from noise and artifacts. While in a laboratory environment, artifacts and interference can, to a large extent, be avoided or controlled, in real-life scenarios this is a challenge. Ear-EEG is a concept where EEG is acquired from electrodes in the ear. METHODS: We present a characterization of physiological artifacts generated in a controlled environment for nine subjects. The influence of the artifacts was quantified in terms of the signal-to-noise ratio (SNR) deterioration of the auditory steady-state response. Alpha band modulation was also studied in an open/closed eyes paradigm. RESULTS: Artifacts related to jaw muscle contractions were present all over the scalp and in the ear, with the highest SNR deteriorations in the gamma band. The SNR deterioration for jaw artifacts were in general higher in the ear compared to the scalp. Whereas eye-blinking did not influence the SNR in the ear, it was significant for all groups of scalps electrodes in the delta and theta bands. Eye movements resulted in statistical significant SNR deterioration in both frontal, temporal and ear electrodes. Recordings of alpha band modulation showed increased power and coherence of the EEG for ear and scalp electrodes in the closed-eyes periods. CONCLUSIONS: Ear-EEG is a method developed for unobtrusive and discreet recording over long periods of time and in real-life environments. This study investigated the influence of the most important types of physiological artifacts, and demonstrated that spontaneous activity, in terms of alpha band oscillations, could be recorded from the ear-EEG platform. In its present form ear-EEG was more prone to jaw related artifacts and less prone to eye-blinking artifacts compared to state-of-the-art scalp based systems.
Goverdovsky V, von Rosenberg W, Nakamura T, et al., 2017, Hearables: multimodal physiological in-ear sensing, Scientific Reports, Vol: 7, ISSN: 2045-2322
Future health systems require the means to assess and track the neural and physiological function of a user over long periods of time, and in the community. Human body responses are manifested through multiple, interacting modalities – the mechanical, electrical and chemical; yet, current physiological monitors (e.g. actigraphy, heart rate) largely lack in cross-modal ability, are inconvenient and/or stigmatizing. We address these challenges through an inconspicuous earpiece, which benefits from the relatively stable position of the ear canal with respect to vital organs. Equipped with miniature multimodal sensors, it robustly measures the brain, cardiac and respiratory functions. Comprehensive experiments validate each modality within the proposed earpiece, while its potential in wearable health monitoring is illustrated through case studies spanning these three functions. We further demonstrate how combining data from multiple sensors within such an integrated wearable device improves both the accuracy of measurements and the ability to deal with artifacts in real-world scenarios.
Nakamura T, adjei T, alqurashi Y, et al., 2017, Complexity science for sleep stage classification from EEG, IEEE International Joint Conference on Neural Networks (IJCNN) 2017, Publisher: IEEE, Pages: 4387-4394, ISSN: 2161-4407
Automatic sleep stage classification is an importantparadigm in computational intelligence and promises consider-able advantages to the health care. Most current automatedmethods require the multiple electroencephalogram (EEG) chan-nels and typically cannot distinguish the S1 sleep stage fromEEG. The aim of this study is to revisit automatic sleep stageclassification from EEGs using complexity science methods. Theproposed method applies fuzzy entropy and permutation entropyas kernels of multi-scale entropy analysis. To account for sleeptransition, the preceding and following 30 seconds of epoch datawere used for analysis as well as the current epoch. Combiningthe entropy and spectral edge frequency features extracted fromone EEG channel, a multi-class support vector machine (SVM)was able to classify 93.8% of 5 sleep stages for the SleepEDFdatabase [expanded], with the sensitivity of S1 stage was 49.1%.Also, the Kappa’s coefficient yielded 0.90, which indicates almostperfect agreement.
Nakamura T, Goverdovsky V, Morrell M, et al., 2017, Automatic sleep monitoring using ear-EEG, IEEE Journal of Translational Engineering in Health and Medicine, Vol: 5, ISSN: 2168-2372
The monitoring of sleep patterns without patient’s inconvenience or involvement of a medical specialist is a clinical question of significant importance. To this end, we propose an automatic sleep stage monitoring system based on an affordable, unobtrusive, discreet, and long-term wearable in-ear sensor for recording the Electroencephalogram (ear-EEG). The selected features for sleep pattern classification from a single ear-EEG channel include the spectral edge frequency (SEF) and multiscale fuzzy entropy (MSFE), a structural complexity feature. In this preliminary study, the manually scored hypnograms from simultaneous scalp-EEG and ear-EEG recordings of four subjects are used as labels for two analysis scenarios: 1) classification of ear-EEG hypnogram labels from ear-EEG recordings and 2) prediction of scalp-EEG hypnogram labels from ear-EEG recordings. We consider both 2-class and 4-class sleep scoring, with the achieved accuracies ranging from 78.5% to 95.2% for ear-EEG labels predicted from ear-EEG, and 76.8% to 91.8% for scalp-EEG labels predicted from ear-EEG. The corresponding Kappa coefficients range from 0.64 to 0.83 for Scenario 1, and indicate Substantial to Almost Perfect Agreement, while for Scenario 2 the range of 0.65 to 0.80 indicates Substantial Agreement, thus further supporting the feasibility of in-ear sensing for sleep monitoring in the community.
Kanna S, Mandic DP, 2017, Self-stabilising adaptive three-phase transforms via widely linear modelling, ELECTRONICS LETTERS, Vol: 53, Pages: 875-876, ISSN: 0013-5194
von Rosenberg WC, Chanwimalueang T, Adjei T, et al., 2017, Resolving ambiguities in the LF/HF Ratio: LF-HF scatter plots for the categorization of mental and physical stress from HRV, Frontiers in Physiology, Vol: 8, ISSN: 1664-042X
It is generally accepted that the activities of the autonomic nervous system (ANS), which consists of the sympathetic (SNS) and parasympathetic nervous systems (PNS), are reflected in the low- (LF) and high-frequency (HF) bands in heart rate variability (HRV)—while, not without some controversy, the ratio of the powers in those frequency bands, the so called LF-HF ratio (LF/HF), has been used to quantify the degree of sympathovagal balance. Indeed, recent studies demonstrate that, in general: (i) sympathovagal balance cannot be accurately measured via the ratio of the LF- and HF- power bands; and (ii) the correspondence between the LF/HF ratio and the psychological and physiological state of a person is not unique. Since the standard LF/HF ratio provides only a single degree of freedom for the analysis of this 2D phenomenon, we propose a joint treatment of the LF and HF powers in HRV within a two-dimensional representation framework, thus providing the required degrees of freedom. By virtue of the proposed 2D representation, the restrictive assumption of the linear dependence between the activity of the autonomic nervous system (ANS) and the LF-HF frequency band powers is demonstrated to become unnecessary. The proposed analysis framework also opens up completely new possibilities for a more comprehensive and rigorous examination of HRV in relation to physical and mental states of an individual, and makes possible the categorization of different stress states based on HRV. In addition, based on instantaneous amplitudes of Hilbert-transformed LF- and HF-bands, a novel approach to estimate the markers of stress in HRV is proposed and is shown to improve the robustness to artifacts and irregularities, critical issues in real-world recordings. The proposed approach for resolving the ambiguities in the standard LF/HF-ratio analyses is verified over a number of real-world stress-invoking scenarios.
Cichocki A, Anh-Huy P, Zhao Q, et al., 2017, Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations Part 2 Applications and Future Perspectives, FOUNDATIONS AND TRENDS IN MACHINE LEARNING, Vol: 9, Pages: 431-+, ISSN: 1935-8237
Chanwimalueang T, Aufegger L, Adjei T, et al., 2017, Stage call: Cardiovascular reactivity to audition stress in musicians, PLOS ONE, Vol: 12, ISSN: 1932-6203
Auditioning is at the very center of educational and professional life in music and is associated with significant psychophysical demands. Knowledge of how these demands affect cardiovascular responses to psychosocial pressure is essential for developing strategies to both manage stress and understand optimal performance states. To this end, we recorded the electrocardiograms (ECGs) of 16 musicians (11 violinists and 5 flutists) before and during performances in both low- and high-stress conditions: with no audience and in front of an audition panel, respectively. The analysis consisted of the detection of R-peaks in the ECGs to extract heart rate variability (HRV) from the notoriously noisy real-world ECGs. Our data analysis approach spanned both standard (temporal and spectral) and advanced (structural complexity) techniques. The complexity science approaches—namely, multiscale sample entropy and multiscale fuzzy entropy—indicated a statistically significant decrease in structural complexity in HRV from the low- to the high-stress condition and an increase in structural complexity from the pre-performance to performance period, thus confirming the complexity loss theory and a loss in degrees of freedom due to stress. Results from the spectral analyses also suggest that the stress responses in the female participants were more parasympathetically driven than those of the male participants. In conclusion, our findings suggest that interventions to manage stress are best targeted at the sensitive pre-performance period, before an audition begins.
Xia Y, He Y, Wang K, et al., 2017, A Complex Least Squares Enhanced Smart DFT Technique for Power System Frequency Estimation, IEEE TRANSACTIONS ON POWER DELIVERY, Vol: 32, Pages: 1270-1278, ISSN: 0885-8977
Stott AE, Kanna S, Mandic DP, et al., 2017, AN ONLINE NIPALS ALGORITHM FOR PARTIAL LEAST SQUARES, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 4177-4181, ISSN: 1520-6149
Talebi SP, Kanna S, Xia Y, et al., 2017, COST-EFFECTIVE DIFFUSION KALMAN FILTERING WITH IMPLICIT MEASUREMENT EXCHANGES, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 4411-4415, ISSN: 1520-6149
Douglas SC, Mandic DP, 2017, SINGLE-CHANNEL WIENER FILTERING OF DETERMINISTIC SIGNALS IN STOCHASTIC NOISE USING THE PANORAMA, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 4182-4186, ISSN: 1520-6149
Li Z, Xia Y, Pei W, et al., 2017, Noncircular Measurement and Mitigation of I/Q Imbalance for OFDM-Based WLAN Transmitters, IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, Vol: 66, Pages: 383-393, ISSN: 0018-9456
Alqurashi Y, Moss J, Nakamura T, et al., 2017, The Efficacy Of In-Ear Electroencephalography (eeg) To Monitor Sleep Latency And The Impact Of Sleep Deprivation, International Conference of the American-Thoracic-Society (ATS), Publisher: AMER THORACIC SOC, ISSN: 1073-449X
Ahmed MU, Chanwimalueang T, Thayyil S, et al., 2016, A multivariate multiscale fuzzy entropy algorithm with application to uterine EMG complexity analysis, Entropy, Vol: 19, ISSN: 1099-4300
The recently introduced multivariate multiscale entropy (MMSE) has been successfully used to quantify structural complexity in terms of nonlinear within- and cross-channel correlations as well as to reveal complex dynamical couplings and various degrees of synchronization over multiple scales in real-world multichannel data. However, the applicability of MMSE is limited by the coarse-graining process which defines scales, as it successively reduces the data length for each scale and thus yields inaccurate and undefined entropy estimates at higher scales and for short length data. To that cause, we propose the multivariate multiscale fuzzy entropy (MMFE) algorithm and demonstrate its superiority over the MMSE on both synthetic as well as real-world uterine electromyography (EMG) short duration signals. Based on MMFE features, an improvement in the classification accuracy of term-preterm deliveries was achieved, with a maximum area under the curve (AUC) value of 0.99.
Cichocki A, Lee N, Oseledets I, et al., 2016, Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions, Foundations and Trends in Machine Learning, Vol: 9, Pages: 249-429, ISSN: 1935-8237
Modern applications in engineering and data science are increasingly based on multidimensional data of exceedingly high volume, variety, and structural richness. However, standard machine learning algorithms typically scale exponentially with data volume and complexity of cross-modal couplings - the so called curse of dimensionality - which is prohibitive to the analysis of large-scale, multi-modal and multi-relational datasets. Given that such data are often efficiently represented as multiway arrays or tensors, it is therefore timely and valuable for the multidisciplinary machine learning and data analytic communities to review low-rank tensor decompositions and tensor networks as emerging tools for dimensionality reduction and large scale optimization problems. Our particular emphasis is on elucidating that, by virtue of the underlying low-rank approximations, tensor networks have the ability to alleviate the curse of dimensionality in a number of applied areas. In Part 1 of this monograph we provide innovative solutions to low-rank tensor network decompositions and easy to interpret graphical representations of the mathematical operations on tensor networks. Such a conceptual insight allows for seamless migration of ideas from the flat-view matrices to tensor network operations and vice versa, and provides a platform for further developments, practical applications, and non-Euclidean extensions. It also permits the introduction of various tensor network operations without an explicit notion of mathematical expressions, which may be beneficial for many research communities that do not directly rely on multilinear algebra. Our focus is on the Tucker and tensor train (TT) decompositions and their extensions, and on demonstrating the ability of tensor networks to provide linearly or even super-linearly (e.g., logarithmically) scalable solutions, as illustrated in detail in Part 2 of this monograph.
Talebi SP, Kanna S, Mandic DP, 2016, A Distributed Quaternion Kalman Filter With Applications to Smart Grid and Target Tracking, IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, Vol: 2, Pages: 477-488, ISSN: 2373-776X
Enshaeifar S, Took CC, Park C, et al., 2016, Quaternion Common Spatial Patterns, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 25, Pages: 1278-1286, ISSN: 1534-4320
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%.
Zhou G, Cichocki A, Zhang Y, et al., 2016, Group component analysis for multiblock data: common and individual feature extraction, IEEE Transactions on Neural Networks and Learning Systems, Vol: 27, Pages: 2426-2439, ISSN: 2162-2388
Real-world data are often acquired as a collection of matrices rather than as a single matrix. Such multiblock data are naturally linked and typically share some common features while at the same time exhibiting their own individual features, reflecting the underlying data generation mechanisms. To exploit the linked nature of data, we propose a new framework for common and individual feature extraction (CIFE) which identifies and separates the common and individual features from the multiblock data. Two efficient algorithms termed common orthogonal basis extraction (COBE) are proposed to extract common basis is shared by all data, independent on whether the number of common components is known beforehand. Feature extraction is then performed on the common and individual subspaces separately, by incorporating dimensionality reduction and blind source separation techniques. Comprehensive experimental results on both the synthetic and real-world data demonstrate significant advantages of the proposed CIFE method in comparison with the state-of-the-art.
von Rosenberg W, Chanwimalueang T, Goverdovsky V, et al., 2016, Smart helmet: wearable multichannel ECG & EEG, IEEE Journal of Translational Engineering in Health and Medicine, Vol: 4, ISSN: 2168-2372
Modern wearable technologies have enabled continuous recording of vital signs, however, for activities such as cycling, motor-racing, or military engagement, a helmet with embedded sensors would provide maximum convenience and the opportunity to monitor simultaneously both the vital signs and the electroencephalogram (EEG). To this end, we investigate the feasibility of recording the electrocardiogram (ECG), respiration, and EEG from face-lead locations, by embedding multiple electrodes within a standard helmet. The electrode positions are at the lower jaw, mastoids, and forehead, while for validation purposes a respiration belt around the thorax and a reference ECG from the chest serve as ground truth to assess the performance. The within-helmet EEG is verified by exposing the subjects to periodic visual and auditory stimuli and screening the recordings for the steady-state evoked potentials in response to these stimuli. Cycling and walking are chosen as real-world activities to illustrate how to deal with the so-induced irregular motion artifacts, which contaminate the recordings. We also propose a multivariate R-peak detection algorithm suitable for such noisy environments. Recordings in real-world scenarios support a proof of concept of the feasibility of recording vital signs and EEG from the proposed smart helmet.
Xiang M, Took CC, Mandic DP, 2016, Cost-effective quaternion minimum mean square error estimation: From widely linear to four-channel processing, Signal Processing, Vol: 136, Pages: 81-91, ISSN: 0165-1684
Widely linear estimation plays an important role in quaternion signal processing, as it caters for both proper and improper quaternion signals. However, widely linear algorithms are computationally expensive owing to the use of augmented variables and statistics. To reduce the computation cost while maintaining the performance level, we propose a four-channel estimation framework as an efficient alternative to quaternion widely linear estimation. This is achieved by using four linear models to estimate the four components of quaternion signals. We also show that any of the four channels is able to replace a strictly linear quaternion estimator when estimating strictly linear systems. The proposed method is shown to reduce computational complexity and provide more flexible algorithms, while preserving the physical meaning inherent in the quaternion domain. The proposed framework is next applied to quaternion minimum mean square error estimation to yield the reduced-complexity versions of the quaternion least mean square (QLMS), quaternion recursive least squares (QRLS), and quaternion nonlinear gradient decent (QNGD) algorithms. For the proposed QLMS algorithm, an adaptive step-size strategy is also explored. The effectiveness of the so introduced estimation techniques is validated by simulations on synthetic and real-world signals.
Looney D, Goverdovsky V, Rosenzweig I, et al., 2016, A Wearable In-Ear Encephalography Sensor for Monitoring Sleep: Preliminary Observations from Nap Studies, Annals of the American Thoracic Society, Vol: 13, Pages: 2229-2233, ISSN: 2329-6933
RATIONALE: To date the only quantifiable measure of neural changes that define sleep is electroencephalography (EEG). Although widely used for clinical testing, scalp-electrode EEG is costly and poorly tolerated by sleeping patients. OBJECTIVES: This is a pilot study to assess the agreement between EEG recordings obtained from a new ear-EEG sensor and those obtained simultaneously from standard scalp electrodes. METHODS: Participants were 4 healthy men, ages 25 to 36 years. During naps, EEG tracings were recorded simultaneously from the ear sensor and standard scalp electrodes. A clinical expert, blinded to the data collection, analyzed 30-second epochs of recordings from both devices using standardized criteria. The agreement between scalp- and ear-recordings was assessed. MEASUREMENTS AND MAIN RESULTS: We scored 360 epochs (scalp-EEG and ear-EEG) of which 254 (70.6%) were scored as non-rapid-eye movement (NREM) sleep using scalp-EEG. The ear-EEG sensor had a sensitivity of 0.88 (95% CI 0.82 to 0.92) and specificity of 0.78 (95% CI 0.70 to 0.84) in detecting N2/N3 sleep. The kappa coefficient, between the scalp- and ear-EEG, was 0.65 (95% CI 0.58 to 0.73). As a sleep monitor (all NREM sleep stages versus wake), the in-ear sensor had a sensitivity of 0.91 (95% CI 0.87 to 0.94) and specificity of 0.66 (95% CI 0.56 to 0.75). The kappa coefficient was 0.60 (95% CI 0.50 to 0.69). CONCLUSIONS: Substantial agreement was observed between recordings derived from a new ear-EEG sensor and conventional scalp electrodes on 4 healthy volunteers during daytime naps.
Xu D, Gao H, Mandic DP, 2016, A new proof of the generalized Hamiltonian-Real calculus, Royal Society Open Science, Vol: 3, ISSN: 2054-5703
The recently introduced generalized Hamiltonian–Real (GHR)calculus comprises, for the first time, the product and chainrules that makes it a powerful tool for quaternion-basedoptimization and adaptive signal processing. In this paper,we introduce novel dual relationships between the GHRcalculus and multivariate real calculus, in order to provide anew, simpler proof of the GHR derivative rules. This furtherreinforces the theoretical foundation of the GHR calculus andprovides a convenient methodology for generic extensions ofreal- and complex-valued learning algorithms to the quaterniondomain.
Kim Y, Ryu J, Kim KK, et al., 2016, Motor Imagery Classification Using Mu and Beta Rhythms of EEG with Strong Uncorrelating Transform Based Complex Common Spatial Patterns, Computational Intelligence and Neuroscience, Vol: 2016, ISSN: 1687-5265
Recent studies have demonstrated the disassociation between the mu and beta rhythms of electroencephalogram (EEG) during motor imagery tasks. The proposed algorithm in this paper uses a fully data-driven multivariate empirical mode decomposition (MEMD) in order to obtain the mu and beta rhythms from the nonlinear EEG signals. Then, the strong uncorrelating transform complex common spatial patterns (SUTCCSP) algorithm is applied to the rhythms so that the complex data, constructed with the mu and beta rhythms, becomes uncorrelated and its pseudocovariance provides supplementary power difference information between the two rhythms. The extracted features using SUTCCSP that maximize the interclass variances are classified using various classification algorithms for the separation of the left- and right-hand motor imagery EEG acquired from the Physionet database. This paper shows that the supplementary information of the power difference between mu and beta rhythms obtained using SUTCCSP provides an important feature for the classification of the left- and right-hand motor imagery tasks. In addition, MEMD is proved to be a preferred preprocessing method for the nonlinear and nonstationary EEG signals compared to the conventional IIR filtering. Finally, the random forest classifier yielded a high performance for the classification of the motor imagery tasks.
Akansu AN, Malioutov D, Palomar DP, et al., 2016, Introduction to the Issue on Financial Signal Processing and Machin Learning for Electronic Trading, IEEE Journal of Selected Topics in Signal Processing, Vol: 10, Pages: 979-981, ISSN: 1932-4553
The twelve papers in this special issue presents relevant research contributions from the disciplines of finance, mathematics, data science and engineering to facilitate scientific cross-fertilization. It will also serve the signal processing community to be exposed to the state of the art in mathematical finance, financial engineering, financial signal processing an electronic trading, and to foster future research in this emerging area. The main themes of this special issue include using tools from machine learning and signal processing that help to address some of the main problems arising in quantitative finance: modeling risk and correlations of financial instruments and their baskets, returns and liquidity, and problems involving risk-aware resource allocation -namely portfolio optimization. These problems involve tools from convex and discrete optimization, non-parametric statistics, time-series modeling,graph theory and high-dimensional covariance estimation.
Rehman NU, Abbas SZ, Asif A, et al., 2016, Translation invariant multi-scale signal denoising based on goodness-of-fit tests, SIGNAL PROCESSING, Vol: 131, Pages: 220-234, ISSN: 0165-1684
Carrion Garcia A, Chanwimalueang T, Giovanni Calvi G, et al., 2016, Modelling economic stress through financial systemic balance index, Pages: 565-569
Financial markets undergo cycles oscillating between periods of economic growth followed by periods of recession. This is similar to the principle of sympathovagal balance in humans representing the leverages between two interactive nervous systems: The sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). Based on the Efficient Market Hypothesis (EMH), we conjecture that there could exist two major groups of people who invest in financial markets: 1. Those who drive financial prices to a state of abnormality, and 2. Those who try to sustain the financial prices. To support this conjecture, we introduce the financial systemic balance index (FSBI) which is used to quantify the evolution of financial stress levels in the market. Results show that periods of higher stress detected by the proposed FSBI index correspond to those with higher levels of determinism and nonlinearity, evaluated via the Delay Vector Variance (DVV) method.
Hemakom A, Chanwimalueang T, Carrion A, et al., 2016, Financial Stress Through Complexity Science, IEEE Journal of Selected Topics in Signal Processing, Vol: 10, Pages: 1112-1126, ISSN: 1932-4553
Abstract:Financial markets typically undergo periods of prosperity followed by periods of stagnation, and this undulation makes it challenging to maintain market efficiency. The efficient market hypothesis (EMH) states that there exist differences in structural complexity in security prices between regular and abnormal situations. Yet, despite a clear link between market acceleration (cf. recession in security prices) and stress in physical systems, indices of financial stress still have significant scope for further development. The overarching aim of this work is therefore to determine the characteristics of financial indices related to financial stress, and to establish a robust metric for the extent of such `stress'. This is achieved based on intrinsic multiscale analysis which quantifies the so called complexity-loss hypothesis in the context of financial stress. The multiscale sample entropy and our proposed Assessment of Latent Index of Stress methods have successfully assessed financial stress, and have served as a measure to establish an analogy between transitions from `normal' (relaxed) to `abnormal' (stressed) financial periods with the sympatho-vagal balance in humans. Four major stock indices of the US economy over the past 25 years are considered: (i) Dow Jones Industrial Average, (ii) NASDAQ Composite, (iii) Standard & Poor's 500, and (iv) Russell 2000, together with FTSE 100, CAC 40 and exchange rates. Our findings support the EMH theory and reveal high stress for both the periods of Internet bubble burst and sub-prime mortgage crisis.
Kanna S, Mandic DP, 2016, Steady-State Behavior of General Complex-Valued Diffusion LMS Strategies, IEEE Signal Processing Letters, Vol: 23, Pages: 722-726, ISSN: 1558-2361
A novel methodology to bound the steady-state mean square performance of the diffusion complex least mean square (D-CLMS) and the diffusion widely linear (augmented) CLMS (D-ACLMS) algorithm is proposed. This is achieved by exploiting the almost identical nature of the steady-state filter weights at all nodes. The proposed approach allows for the consideration of the second-order terms in the recursion for the weight error covariance matrix, without compromising the mathematical tractability of the problem. The closed form expressions for the mean square deviation (MSD) and excess mean square error (EMSE) for both the D-CLMS and D-ACLMS allow for the performance of the algorithms to be quantified as a function of the noncircularity of the input data.
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