616 results found
Stanković L, Mandic D, Daković M, et al., 2020, Vertex-frequency graph signal processing: A comprehensive review, Digital Signal Processing: A Review Journal, Vol: 107, ISSN: 1051-2004
© 2020 Elsevier Inc. Graph signal processing deals with signals which are observed on an irregular graph domain. While many approaches have been developed in classical graph theory to cluster vertices and segment large graphs in a signal independent way, signal localization based approaches to the analysis of data on graph represent a new research direction which is also a key to big data analytics on graphs. To this end, after an overview of the basic definitions of graphs and graph signals, we present and discuss a localized form of the graph Fourier transform. To establish analogy with classical signal processing, spectral domain and vertex domain definitions of the localization window are given next. The spectral and vertex localization kernels are then related to the wavelet transform, followed by their polynomial approximations and a study of filtering and inversion operations. For rigor, the analysis of energy representation and frames in the localized graph Fourier transform is extended to the energy forms of vertex-frequency distributions, which operate even without the requirement to apply localization windows. Another link with classical signal processing is established through the concept of local smoothness, which is subsequently related to the paradigm of signal smoothness on graphs, a lynchpin which connects the properties of the signals on graphs and graph topology. This all represents a comprehensive account of the relation of general vertex-frequency analysis with classical time-frequency analysis, an important but missing link for more advanced applications of graph signal processing. The theory is supported by illustrative and practically relevant examples.
Davies HJ, Williams I, Peters NS, et al., 2020, In-ear SpO2: a tool for wearable, unobtrusive monitoring of core blood oxygen saturation, Sensors (Basel, Switzerland), Vol: 20, ISSN: 1424-8220
The non-invasive estimation of blood oxygen saturation (SpO2) by pulse oximetry is of vital importance clinically, from the detection of sleep apnea to the recent ambulatory monitoring of hypoxemia in the delayed post-infective phase of COVID-19. In this proof of concept study, we set out to establish the feasibility of SpO2 measurement from the ear canal as a convenient site for long term monitoring, and perform a comprehensive comparison with the right index finger-the conventional clinical measurement site. During resting blood oxygen saturation estimation, we found a root mean square difference of 1.47% between the two measurement sites, with a mean difference of 0.23% higher SpO2 in the right ear canal. Using breath holds, we observe the known phenomena of time delay between central circulation and peripheral circulation with a mean delay between the ear and finger of 12.4 s across all subjects. Furthermore, we document the lower photoplethysmogram amplitude from the ear canal and suggest ways to mitigate this issue. In conjunction with the well-known robustness to temperature induced vasoconstriction, this makes conclusive evidence for in-ear SpO2 monitoring being both convenient and superior to conventional finger measurement for continuous non-intrusive monitoring in both clinical and everyday-life settings.
Zhang X, Dees BS, Li C, et al., 2020, Analysis of Least Stochastic Entropy Adaptive Filters for Noncircular Gaussian Signals, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, Vol: 67, Pages: 1364-1368, ISSN: 1549-7747
Phan A-H, Cichocki A, Oseledets I, et al., 2020, Tensor Networks for Latent Variable Analysis: Higher Order Canonical Polyadic Decomposition, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol: 31, Pages: 2174-2188, ISSN: 2162-237X
Stankovic L, Dakovic M, Mandic D, et al., 2020, A Low-Dimensionality Method for Data-Driven Graph Learning, Pages: 5340-5344, ISSN: 1520-6149
© 2020 IEEE. In many graph signal processing applications, finding the topology of a graph is part of the overall data processing problem rather than a priori knowledge. Most of the approaches to graph topology learning are based on the assumption of graph Laplacian sparsity, with various additional constraints, followed by variations of the edge weights in the graph domain or the eigenvalues in the graph spectral domain. These domains are high-dimensional, since their dimension is at least equal to the order of the number of vertices. In this paper, we propose a numerically efficient method for estimating of the normalized Laplacian through its eigenvalues estimation and by promoting its sparsity. The minimization problem is solved in quite a low-dimensional space, related to the polynomial order of the underlying system on a graph corresponding to the the observed data. The accuracy of the results is tested on numerical example.
Dees BS, Stankovic L, Constantinides AG, et al., 2020, Portfolio Cuts: A Graph-Theoretic Framework to Diversification, Pages: 8454-8458, ISSN: 1520-6149
© 2020 IEEE. Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure. To this end, we investigate ways for domain knowledge to be conveniently incorporated into the analysis, by means of graphs. Next, to relax the assumption of the completeness of graph topology and to equip the graph model with practically relevant physical intuition, we introduce the portfolio cut paradigm. Such a graph-theoretic portfolio partitioning technique is shown to allow the investor to devise robust and tractable asset allocation schemes, by virtue of a rigorous graph framework for considering smaller, computationally feasible, and economically meaningful clusters of assets, based on graph cuts. In turn, this makes it possible to fully utilize the asset returns covariance matrix for constructing the portfolio, even without the requirement for its inversion. The advantages of the proposed framework over traditional methods are demonstrated through numerical simulations based on real-world price data.
Cheng H, Xia Y, Huang Y, et al., 2020, Improperness Based SINR Analysis of GFDM Systems under Joint Tx and Rx I/Q Imbalance, ISSN: 1525-3511
© 2020 IEEE. Adverse impacts of in-phase and quadrature-phase (I/Q) imbalance in both the transmitter (Tx) and receiver (Rx) are quantified for the generalized frequency division multiplexing (GFDM) based transmission over frequency selective fading channels. To this end, we first equip the standard signal-to-interference-plus-noise (SINR) performance evaluation with the ability to consider second-order noncircular (improper) signals, and thus precisely evaluate performance deterioration caused by I/Q distortions over the in-phase (I) and quadrature-phase (Q) channels of a transmission system. Next, we propose a novel means to evaluate the individual SINR contributions from both the channels of GFDM, and hence, provide more meaningful insights into the underlying wireless transmission in the presence of complex non-circularity. This is accompanied by an account of complete augmented second-order statistics of I/Q imbalanced GFDM waveforms which caters for various sources of complex improperness. Simulations in the GFDM system setting support our analysis.
Stankovic L, Brajovic M, Dakovic M, et al., 2020, On the decomposition of multichannel nonstationary multicomponent signals, SIGNAL PROCESSING, Vol: 167, ISSN: 0165-1684
Stankovic L, Mandic DP, Dakovic M, et al., 2020, Demystifying the Coherence Index in Compressive Sensing [Lecture Notes], IEEE SIGNAL PROCESSING MAGAZINE, Vol: 37, Pages: 152-162, ISSN: 1053-5888
Xiang M, Xia Y, Mandic DP, 2020, Performance Analysis of Deficient Length Quaternion Least Mean Square Adaptive Filters, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 68, Pages: 65-80, ISSN: 1053-587X
Nakamura T, Alqurashi Y, Morrell M, et al., 2020, Hearables: automatic overnight sleep monitoring with standardised in-ear EEG sensor, IEEE Transactions on Biomedical Engineering, Vol: 67, Pages: 203-212, ISSN: 0018-9294
Objective: Advances in sensor miniaturisation and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may impact neural function, and can be a symptom of both physical and mental disorders. This study proposes wearable in-ear electroencephalography (ear- EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community. Methods: Twenty-two healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography (PSG) recordings. The ear- EEG data were analysed in the both structural complexity and spectral domains; the extracted features were used for automatic sleep stage prediction through supervised machine learning, whereby the PSG data were manually scored by a sleep clinician. Results: The agreement between automatic sleep stage prediction based on ear-EEG from a single in-ear sensor and the hypnogram based on the full PSG was 74.1% in the accuracy over five sleep stage classification; this is supported by a Substantial Agreement in the kappa metric (0.61). Conclusion: The in-ear sensor is both feasible for monitoring overnight sleep outside the sleep laboratory and mitigates technical difficulties associated with scalp-EEG. It therefore represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring. Significance: The ‘standardised’ one-size-fits-all viscoelastic in-ear sensor is a next generation solution to monitor sleep - this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth.
Kisil I, Calvi GG, Scalzo Dees B, et al., 2020, Tensor Decompositions and Practical Applications: A Hands-on Tutorial, Studies in Computational Intelligence, Pages: 69-97
© 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. The exponentially increasing availability of big and streaming data comes as a direct consequence of the rapid development and widespread use of multi-sensor technology. The quest to make sense of such large volume and variety of that has both highlighted the limitations of standard flat-view matrix models and the necessity to move toward more versatile data analysis tools. One such model which is naturally suited for data of large volume, variety and veracity are multi-way arrays or tensors. The associated tensor decompositions have been recognised as a viable way to break the “Curse of Dimensionality”, an exponential increase in data volume with the tensor order. Owing to a scalable way in which they deal with multi-way data and their ability to exploit inherent deep data structures when performing feature extraction, tensor decompositions have found application in a wide range of disciplines, from very theoretical ones, such as scientific computing and physics, to the more practical aspects of signal processing and machine learning. It is therefore both timely and important for a wider Data Analytics community to become acquainted with the fundamentals of such techniques. Thus, our aim is not only to provide a necessary theoretical background for multi-linear analysis but also to equip researches and interested readers with an easy to read and understand practical examples in form of a Python code snippets.
© 2019 IEEE. One of the approaches to optimally divide the cellular networks of the next generation, with a large number of base-stations, is based on the graph cuts theory. Graph cut aims at identifying and arranging the vertices into non-overlapping subsets. It is expected that data in each subset share some relative similarities. This paper reviews the graph clustering approach based on the minimum cut with the p-Laplacian, using a simplified method for Fiedler vector thresholding. The proposed method is inspired by the developments within the compressive sensing theory.
Stankovic L, Mandic DP, Dakovic M, et al., 2019, Understanding the Basis of Graph Signal Processing via an Intuitive Example-Driven Approach, IEEE SIGNAL PROCESSING MAGAZINE, Vol: 36, Pages: 133-145, ISSN: 1053-5888
von Rosenberg W, Hoting M-O, Mandic DP, 2019, A physiology based model of heart rate variability, BIOMEDICAL ENGINEERING LETTERS, Vol: 9, Pages: 425-434, ISSN: 2093-9868
Mandic DP, Djuric PM, Cichocki A, et al., 2019, Quo Vadis ICASSP: Echoes of 2019 ICASSP in Brighton, United Kingdom Signal Processing Meets the Needs of Modern Humankind, IEEE SIGNAL PROCESSING MAGAZINE, Vol: 36, Pages: 127-134, ISSN: 1053-5888
Talebi SP, Werner S, Mandic DP, 2019, Complex-Valued Nonlinear Adaptive Filters With Applications in alpha-Stable Environments, IEEE SIGNAL PROCESSING LETTERS, Vol: 26, Pages: 1315-1319, ISSN: 1070-9908
Solo V, Greco MS, Mandic D, et al., 2019, Innovation Starts With Education ICASSP 2019 Education Panel, IEEE SIGNAL PROCESSING MAGAZINE, Vol: 36, Pages: 135-+, ISSN: 1053-5888
Xiang M, Xia Y, Mandic DP, 2019, Complementary Cost Functions for Complex and Quaternion Widely Linear Estimation, IEEE SIGNAL PROCESSING LETTERS, Vol: 26, Pages: 1344-1348, ISSN: 1070-9908
Oliveira V, von Rosenberg W, Montaldo P, et al., 2019, Early postnatal heart rate variability in healthy newborn infants, Frontiers in Physiology, Vol: 10, Pages: 1-12, ISSN: 1664-042X
Background: Despite the increasing interest in fetal and neonatal heart rate variability (HRV) analysis and its potential use as a tool for early disease stratification, no studies have previously described the normal trends of HRV in healthy babies during the first hours of postnatal life.Methods: We prospectively recruited 150 healthy babies from the postnatal ward and continuously recorded their electrocardiogram during the first 24 h after birth. Babies were included if born in good condition and stayed with their mother. Babies requiring any medication or treatment were excluded. Five-minute segments of the electrocardiogram (non-overlapping time-windows) with more than 90% consecutive good quality beats were included in the calculation of hourly medians and interquartile ranges to describe HRV trends over the first 24 h. We used multilevel mixed effects regression with auto-regressive covariance structure for all repeated measures analysis and t-tests to compare group differences. Non-normally distributed variables were log-transformed.Results: Nine out of 16 HRV metrics (including heart rate) changed significantly over the 24 h [Heart rate p < 0.01; Standard deviation of the NN intervals p = 0.01; Standard deviation of the Poincaré plot lengthwise p < 0.01; Cardiac sympathetic index (CSI) p < 0.01; Normalized high frequency power p = 0.03; Normalized low frequency power p < 0.01; Total power p < 0.01; HRV index p = 0.01; Parseval index p = 0.03], adjusted for relevant clinical variables. We observed an increase in several HRV metrics during the first 6 h followed by a gradual normalization by approximately 12 h of age. Between 6 and 12 h of age, only heart rate and the normalized low frequency power changed significantly, while between 12 and 18 h no metric, other than heart rate, changed significantly. Analysis with multilevel mixed effects regression analysis (multivariable) revealed that gestational age, reduced fetal movements, cardi
Zhang X, Xia Y, Li C, et al., 2019, Analysis of the Unconstrained Frequency-Domain Block LMS for Second-Order Noncircular Inputs, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 67, Pages: 3970-3984, ISSN: 1053-587X
Xia Y, Tao S, Li Z, et al., 2019, Full Mean Square Performance Bounds on Quaternion Estimators for Improper Data, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 67, Pages: 4093-4106, ISSN: 1053-587X
Nitta T, Kobayashi M, Mandic DP, 2019, Hypercomplex Widely Linear Estimation Through the Lens of Underpinning Geometry, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 67, Pages: 3985-3994, ISSN: 1053-587X
Stott AE, Dees BS, Kisil I, et al., 2019, A class of multidimensional NIPALS algorithms for quaternion and tensor partial least squares regression, SIGNAL PROCESSING, Vol: 160, Pages: 316-327, ISSN: 0165-1684
Powezka K, Adjei T, von Rosenberg W, et al., 2019, A pilot study of preoperative heart rate variability predicting pain during local anesthetic varicose vein surgery, JOURNAL OF VASCULAR SURGERY-VENOUS AND LYMPHATIC DISORDERS, Vol: 7, Pages: 382-386, ISSN: 2213-333X
Li Z, Xia Y, Pei W, et al., 2019, A cost-effective nonlinear self-interference canceller in full-duplex direct-conversion transceivers, SIGNAL PROCESSING, Vol: 158, Pages: 4-14, ISSN: 0165-1684
Kanna S, Moniri A, Xia Y, et al., 2019, A Data Analytics Perspective of Power Grid Analysis-Part 2: Teaching Old Power Systems New Tricks, IEEE SIGNAL PROCESSING MAGAZINE, Vol: 36, Pages: 110-117, ISSN: 1053-5888
Adjei T, von Rosenberg W, Nakamura T, et al., 2019, The ClassA framework: HRV based assessment of SNS and PNS dynamics without LF-HF controversies, Frontiers in Physiology, Vol: 10, ISSN: 1664-042X
The powers of the low frequency (LF) and high frequency (HF) components of heart rate variability (HRV) have become the de facto standard metrics in the assessment of the stress response, and the related activities of the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). However, the widely adopted physiological interpretations of the LF and HF components in SNS /PNS balance are now questioned, which puts under serious scrutiny stress assessments which employ the LF and HF components. To avoid these controversies, we here introduce the novel Classification Angle (ClassA) framework, which yields a family of metrics which quantify cardiac dynamics in three-dimensions. This is achieved using a finite-difference plot of HRV, which displays successive rates of change of HRV, and is demonstrated to provide sufficient degrees of freedom to determine cardiac deceleration and/or acceleration. The robustness and accuracy of the novel ClassA framework is verified using HRV signals from ten males, recorded during standardized stress tests, consisting of rest, mental arithmetic, meditation, exercise and further meditation. Comparative statistical testing demonstrates that unlike the existing LF-HF metrics, the ClassA metrics are capable of distinguishing both the physical and mental stress epochs from the epochs of no stress, with statistical significance (Bonferroni corrected p-value ≤ 0.025); HF was able to distinguish physical stress from no stress, but was not able to identify mental stress. The ClassA results also indicated that at moderate levels of stress, the extent of parasympathetic withdrawal was greater than the extent of sympathetic activation. Finally, the analyses and the experimental results provide conclusive evidence that the proposed nonlinear approach to quantify cardiac activity from HRV resolves three critical obstacles to current HRV stress assessments: (i) it is not based on controversial assumptions of balance between the
Phan A-H, Yamagishi M, Mandic D, et al., 2020, Quadratic programming over ellipsoids with applications to constrained linear regression and tensor decomposition, Neural Computing and Applications, ISSN: 0941-0643
Nakamura T, Davies H, Mandic D, 2019, Scalable automatic sleep staging in the era of Big Data, IEEE EMBC 2019, Publisher: IEEE
Numerous automatic sleep staging approacheshave been proposed to provide an eHealth alternative to thecurrent gold-standard – hypnogram scoring by human experts.However, a majority of such studies exploit data of limited scale,which compromises both the validation and the reproducibilityand transferability of such automatic sleep staging systemsin the real clinical settings. In addition, the computationalissues and physical meaningfulness of the analysis are typicallyneglected, yet affordable computation is a key criterion inBig Data analytics. To this end, we establish a comprehensiveanalysis framework to rigorously evaluate the feasibility ofautomatic sleep staging from multiple perspectives, includingrobustness with respect to the number of training subjects,model complexity, and different classifiers. This is achievedfor a large collection of publicly accessible polysomnography(PSG) data, recorded over 515 subjects. The trade-off betweenaffordable computation and satisfactory accuracy is shown tobe fulfilled by an extreme learning machine (ELM) classifier,which in conjunction with the physically meaningful hiddenMarkov model (HMM) of the transition between the differentsleep stages (smoothing model) is shown to achieve both fastcomputation and highest average Cohen’s kappa value ofκ=0.73(Substantial Agreement). Finally, it is shown thatfor accurate and robust automatic sleep staging, a combinationof structural complexity (multi-scale entropy) and frequency-domain (spectral edge frequency) features is both computation-ally affordable and physically meaningful.
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