Publications
737 results found
Kanna S, von Rosenberg W, Goverdovsky V, et al., 2018, Bringing Wearable Sensors into the Classroom: A Participatory Approach, IEEE SIGNAL PROCESSING MAGAZINE, Vol: 35, Pages: 110-+, ISSN: 1053-5888
Normahani P, Makwana N, von Rosenberg W, et al., 2018, Self-assessment of surgical ward crisis management using video replay augmented with stress biofeedback, Patient Safety in Surgery, Vol: 12, ISSN: 1754-9493
BackgroundWe aimed to explore the feasibility and attitudes towards using video replay augmented with real time stress quantification for the self-assessment of clinical skills during simulated surgical ward crisis management.Methods22 clinicians participated in 3 different simulated ward based scenarios of deteriorating post-operative patients. Continuous ECG recordings were made for all participants to monitor stress levels using heart rate variability (HRV) indices. Video recordings of simulated scenarios augmented with real time stress biofeedback were replayed to participants. They were then asked to self-assess their performance using an objective assessment tool. Participants’attitudes were explored using a post study questionnaire. ResultsUsing HRV stress indices, we demonstrated higher stress levels in novice participants. Self-assessment scores were significantly higherin more experienced participants. Overall, participants felt that video replay andaugmented stress biofeedback were useful in self-assessment. ConclusionSelf-assessment using an objective self-assessment tool alongside video replay augmented with stress biofeedback is feasible in a simulated setting and well liked by participants.
Xiang M, Enshaeifar S, Stott AE, et al., 2018, Simultaneous diagonalisation of the covariance and complementary covariance matrices in quaternion widely linear signal processing, Signal Processing, Vol: 148, Pages: 193-204, ISSN: 0165-1684
Recent developments in quaternion-valued widely linear processing have established that the exploitation of complete second-order statistics requires consideration of both the standard covariance and the three complementary covariance matrices. Although such matrices have a tremendous amount of structure and their decomposition is a powerful tool in a variety of applications, the non-commutative nature of the quaternion product has been prohibitive to the development of quaternion uncorrelating transforms. To this end, we introduce novel techniques for a simultaneous decomposition of the covariance and complementary covariance matrices in the quaternion domain, whereby the quaternion version of the Takagi factorisation is explored to diagonalise symmetric quaternion-valued matrices. This gives new insights into the quaternion uncorrelating transform (QUT) and forms a basis for the proposed quaternion approximate uncorrelating transform (QAUT) which simultaneously diagonalises all four covariance matrices associated with improper quaternion signals. The effectiveness of the proposed uncorrelating transforms is validated by simulations on both synthetic and real-world quaternion-valued signals.
Looney D, Adjei T, Mandic DP, 2018, A Novel Multivariate Sample Entropy Algorithm for Modeling Time Series Synchronization, ENTROPY, Vol: 20, ISSN: 1099-4300
Xia Y, Kanna S, Mandic DP, 2018, Maximum likelihood parameter estimation of unbalanced three-phase power signals, IEEE Transactions on Instrumentation and Measurement, Vol: 67, Pages: 569-581, ISSN: 0018-9456
Accurate detection of the system parameters in unbalanced three-phase power systems is a prerequisite for the optimal operation and control of future smart grids. However, theoretical and practical performance bounds of various estimators for unbalanced systems are only just being established. To this end, we introduce the appropriate Cramer- Rao lower bounds (CRLBs) for frequency estimation, based on the αβ-transformed unbalanced voltage contaminated with noise. Next, for rigor, the maximum likelihood estimation (MLE) method for frequency estimation is introduced as a maximizer of an “augmented periodogram.” The underlying augmented complex statistics is shown to cater for all the available secondorder information, including the noncircularity associated with unbalanced systems. To find the ML solution, Newton's iterative method is employed and its initialization is implemented by a discrete Fourier transform-based dichotomous search technique. We show that the MLE of phases and amplitudes of both the positive and negative phase-sequence components within the αβ-transformed voltage can be generically derived based on the ML frequency estimates. In this way, a unified framework is provided to accurately detect voltage characteristics of the positive and negative phase-sequence components within an unbalanced three-phase power system when its frequency experiences off-nominal conditions. Simulations verify that the proposed MLE approaches theoretical CRLBs for all parameters under consideration.
Xia Y, Mandic DP, 2018, Augmented Performance Bounds on Strictly Linear and Widely Linear Estimators With Complex Data, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 66, Pages: 507-514, ISSN: 1053-587X
Stankovic L, Mandic D, Dakovic M, et al., 2018, Time-frequency decomposition of multivariate multicomponent signals, SIGNAL PROCESSING, Vol: 142, Pages: 468-479, ISSN: 0165-1684
Xia Y, Xiang M, Li Z, et al., 2018, Echo state networks for multidimensional data: Exploiting noncircularity and widely linear models, Adaptive Learning Methods for Nonlinear System Modeling, Pages: 267-288, ISBN: 9780128129777
The quaternion domain offers a convenient and unified means to process multidimensional data which are typically 3D and 4D, such as those measurements from 3D inertial sensors in body sensor networks and 3D wind modeling from 3D ultrasonic anemometers. To deal with the nonlinear and nonstationary characteristics of real-world multidimensional data, quaternion-valued nonlinear learning systems, like recurrent neural networks (RNNs), are highly desirable. To avoid current problems associated with the design of quaternion-valued RNNs, such as the computationally demanding training tasks and the stringent standard analyticity conditions in developing full quaternion-valued nonlinearities, quaternion-valued echo state networks (QESNs), built upon quaternion nonlinear activation functions with local analytic properties, are introduced. To further make QESNs second-order optimal for the generality of quaternion signals (both circular and noncircular), the standard widely linear model is modified so as to suit the properties of dynamical reservoir, typically realized by RNNs. This allows for a full exploitation of second-order information in the multidimensional data, contained both in the covariance and pseudocovariances. Simulations in the prediction setting on both benchmark 3D and 4D circular and noncircular signals and on noncircular, nonlinear and nonstationary real-world 3D body motion tracking and wind forecasting support the analysis.
Konstantinidis A, Scalzo Dees B, Calvi GG, et al., 2018, Text Mining - A Key Lynchpin in the Investment Process: A Survey, 1st International Conference on Applications of Intelligent Systems (APPIS), Publisher: IOS PRESS, Pages: 181-193, ISSN: 0922-6389
Nakamura T, Alqurashi YD, Morrell MJ, et al., 2018, Automatic detection of drowsiness using in-ear EEG, International Joint Conference on Neural Networks (IJCNN), Publisher: IEEE, ISSN: 2161-4393
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- Citations: 11
Brajovic M, Stankovic L, Dakovic M, et al., 2018, Additive Noise Influence on the Bivariate Two-Component Signal Decomposition, 7th Mediterranean Conference on Embedded Computing (MECO), Publisher: IEEE, Pages: 263-266, ISSN: 2377-5475
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- Citations: 2
Mostafanezhad I, Boric-Lubecke O, Mandic D, et al., 2018, Noise- and Interference-Reduction Methods for Microwave Doppler Radar Vital Signs Monitors, MEDICAL AND BIOLOGICAL MICROWAVE SENSORS AND SYSTEMS, Publisher: CAMBRIDGE UNIV PRESS, Pages: 202-227
Xia Y, Kanna S, Mandic DP, 2018, DIFFUSION AUGMENTED COMPLEX EXTENDED KALMAN FILTERING FOR ADAPTIVE FREQUENCY ESTIMATION IN DISTRIBUTED POWER NETWORKS, COOPERATIVE AND GRAPH SIGNAL PROCESSING: PRINCIPLES AND APPLICATIONS, Editors: Djuric, Richard, Publisher: ACADEMIC PRESS LTD-ELSEVIER SCIENCE LTD, Pages: 735-755, ISBN: 978-0-12-813677-5
Saito S, Mandic DP, Suzuki H, 2018, Hypergraph <i>p</i>-Laplacian: A Differential Geometry View, 32nd AAAI Conference on Artificial Intelligence / 30th Innovative Applications of Artificial Intelligence Conference / 8th AAAI Symposium on Educational Advances in Artificial Intelligence, Publisher: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE, Pages: 3984-3991, ISSN: 2159-5399
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- Citations: 12
Xiang M, Kanna S, Mandic DP, 2017, Performance analysis of quaternion-valued adaptive filters in nonstationary environments, IEEE Transactions on Signal Processing, Vol: 66, Pages: 1566-1579, ISSN: 1053-587X
Quaternion adaptive filters have been widely used for the processing of three-dimensional (3-D) and 4-D phenomena, but complete analysis of their performance is still lacking, partly due to the cumbersomeness of multivariate quaternion analysis. This causes difficulties in both understanding their behavior and designing optimal filters. Based on a thorough exploration of the augmented statistics of quaternion random vectors, this paper extends an analysis framework for real-valued adaptive filters to the mean and mean square convergence analyses of general quaternion adaptive filters in nonstationary environments. The extension is nontrivial, considering the noncommutative quaternion algebra, only recently resolved issues with quaternion gradient, and the multidimensional augmented quaternion statistics. Also, for rigor, in order to model a nonstationary environment, the system weights are assumed to vary according to a first-order random-walk model. Transient and steady-state performance of a general class of quaternion adaptive filters is provided by exploiting the augmented quaternion statistics. An innovative quaternion decorrelation technique allows us to develop intuitive closed-form expressions for the performance of quaternion least mean square (QLMS) filters with Gaussian inputs, which provide new insights into the relationship between the filter behavior and the complete second-order statistics of the input signal, that is, quaternion noncircularity. The closed-form expressions for the performance of strictly linear, semiwidely linear, and widely linear QLMS filters are investigated in detail, while numerical simulations for the three classes of QLMS filters with correlated Gaussian inputs support the theoretical analysis.
Hemakom A, Powezka K, Goverdovsky V, et al., 2017, Quantifying team cooperation through intrinsic multi-scale measures: respiratory and cardiac synchronization in choir singers and surgical teams, Royal Society Open Science, Vol: 4, ISSN: 2054-5703
A highly localized data-association measure, termed intrinsic synchrosqueezing transform (ISC), is proposed for the analysis of coupled nonlinear and non-stationary multivariate signals. This is achieved based on a combination of noise-assisted multivariate empirical mode decomposition and short-time Fourier transform-based univariate and multivariate synchrosqueezing transforms. It is shown that the ISC outperforms six other combinations of algorithms in estimating degrees of synchrony in synthetic linear and nonlinear bivariate signals. Its advantage is further illustrated in the precise identification of the synchronized respiratory and heart rate variability frequencies among a subset of bass singers of a professional choir, where it distinctly exhibits better performance than the continuous wavelet transform-based ISC. We also introduce an extension to the intrinsic phase synchrony (IPS) measure, referred to as nested intrinsic phase synchrony (N-IPS), for the empirical quantification of physically meaningful and straightforward-to-interpret trends in phase synchrony. The N-IPS is employed to reveal physically meaningful variations in the levels of cooperation in choir singing and performing a surgical procedure. Both the proposed techniques successfully reveal degrees of synchronization of the physiological signals in two different aspects: (i) precise localization of synchrony in time and frequency (ISC), and (ii) large-scale analysis for the empirical quantification of physically meaningful trends in synchrony (N-IPS).
Talebi SP, Mandic DP, 2017, Distributed Particle Filtering of alpha-Stable Signals, IEEE SIGNAL PROCESSING LETTERS, Vol: 24, Pages: 1862-1866, ISSN: 1070-9908
Chanwimalueang T, Mandic DP, 2017, Cosine Similarity Entropy: Self-Correlation-Based Complexity Analysis of Dynamical Systems, Entropy, Vol: 19, ISSN: 1099-4300
The nonparametric Sample Entropy (SE) estimator has become a standard for the quantification of structural complexity of nonstationary time series, even in critical cases of unfavorable noise levels. The SE has proven very successful for signals that exhibit a certain degree of the underlying structure, but do not obey standard probability distributions, a typical case in real-world scenarios such as with physiological signals. However, the SE estimates structural complexity based on uncertainty rather than on (self) correlation, so that, for reliable estimation, the SE requires long data segments, is sensitive to spikes and erratic peaks in data, and owing to its amplitude dependence it exhibits lack of precision for signals with long-term correlations. To this end, we propose a class of new entropy estimators based on the similarity of embedding vectors, evaluated through the angular distance, the Shannon entropy and the coarse-grained scale. Analysis of the effects of embedding dimension, sample size and tolerance shows that the so introduced Cosine Similarity Entropy (CSE) and the enhanced Multiscale Cosine Similarity Entropy (MCSE) are amplitude-independent and therefore superior to the SE when applied to short time series. Unlike the SE, the CSE is shown to yield valid entropy values over a broad range of embedding dimensions. By evaluating the CSE and the MCSE over a variety of benchmark synthetic signals as well as for real-world data (heart rate variability of three different cardiovascular pathologies), the proposed algorithms are demonstrated to be able to quantify degrees of structural complexity in the context of self-correlation over small to large temporal scales, thus offering physically meaningful interpretations and rigor in the understanding the intrinsic properties of the structural complexity of a system, such as the number of its degrees of freedom.
von Rosenberg W, Chanwimalueang T, Goverdovsky V, et al., 2017, Hearables: feasibility of recording cardiac rhythms from head and in-ear locations, Royal Society Open Science, Vol: 4, ISSN: 2054-5703
Mobile technologies for the recording of vital signs and neural signals are envisaged to underpin the operation of future health services. For practical purposes, unobtrusive devices are favoured, such as those embedded in a helmet or incorporated onto an earplug. However, these locations have so far been underexplored, as the comparably narrow neck impedes the propagation of vital signals from the torso to the head surface. To establish the principles behind electrocardiogram (ECG) recordings from head and ear locations, we first introduce a realistic three-dimensional biophysics model for the propagation of cardiac electric potentials to the head surface, which demonstrates the feasibility of head-ECG recordings. Next, the proposed biophysics propagation model is verified over comprehensive real-world experiments based on head- and in-ear-ECG measurements. It is shown both that the proposed model is an excellent match for the recordings, and that the quality of head- and ear-ECG is sufficient for a reliable identification of the timing and shape of the characteristic P-, Q-, R-, S- and T-waves within the cardiac cycle. This opens up a range of new possibilities in the identification and management of heart conditions, such as myocardial infarction and atrial fibrillation, based on 24/7 continuous in-ear measurements. The study therefore paves the way for the incorporation of the cardiac modality into future ‘hearables’, unobtrusive devices for health monitoring.
Kanna S, Mandic DP, 2017, Stability of Distributed Extended Kalman Filters, 2017 22nd International Conference on Digital Signal Processing (DSP), Publisher: IEEE, ISSN: 1546-1874
Xiang M, Douglas SC, Mandic DP, 2017, The Quaternion Least Mean Magnitude Phase Adaptive Filtering Algorithm, 2017 22nd International Conference on Digital Signal Processing (DSP), Publisher: IEEE, ISSN: 1546-1874
Stankovic L, Brajovic M, Dakovic M, et al., 2017, Two-Component Bivariate Signal Decomposition Based on Time-Frequency Analysis, 2017 22nd International Conference on Digital Signal Processing (DSP), Publisher: IEEE, ISSN: 1546-1874
Variddhisaï T, Mandic DP, 2017, On an RLS-like LMS adaptive filter
A unified and generalized framework for a recursive least squares (RLS)-like least mean square (LMS) algorithm is proposed, which adopts the cost function of the RLS to minimize the mean square error. This paper aims to explore, in a systematic way, the corresponding ideas scattered and multiple-time re-invented in the literature, and introduces a unified approach in the same spirit as in [1], which relates LMS with the Kalman filter. The proposed alternative to the RLS is favored when the matrix inversion lemma is not useful, such as the case of multivariate or multichannel data where the input is not a vector. Furthermore, all the derivations are conducted in the quaternion domain and are hence generalizable to complex- and real-valued models. The resulting algorithm has a neat form and a similar complexity to the RLS. Through experiments, the method is shown to exhibit performance close to or even better than the RLS algorithm. Other aspects, such as the choice of descent directions and variable stepsizes, are also discussed to support the analysis.
Tonoyan Y, Chanwimalueang T, Mandic DP, et al., 2017, Discrimination of emotional states from scalp- and intracranial EEG using multiscale Renyi entropy, PLOS One, Vol: 12, ISSN: 1932-6203
A data-adaptive, multiscale version of Rényi’s quadratic entropy (RQE) is introduced for emotional state discrimination from EEG recordings. The algorithm is applied to scalp EEG recordings of 30 participants watching 4 emotionally-charged video clips taken from a validated public database. Krippendorff’s inter-rater statistic reveals that multiscale RQE of the mid-frontal scalp electrodes best discriminates between five emotional states. Multiscale RQE is also applied to joint scalp EEG, amygdala- and occipital pole intracranial recordings of an implanted patient watching a neutral and an emotionally charged video clip. Unlike for the neutral video clip, the RQEs of the mid-frontal scalp electrodes and the amygdala-implanted electrodes are observed to coincide in the time range where the crux of the emotionally-charged video clip is revealed. In addition, also during this time range, phase synchrony between the amygdala and mid-frontal recordings is maximal, as well as our 30 participants’ inter-rater agreement on the same video clip. A source reconstruction exercise using intracranial recordings supports our assertion that amygdala could contribute to mid-frontal scalp EEG. On the contrary, no such contribution was observed for the occipital pole’s intracranial recordings. Our results suggest that emotional states discriminated from mid-frontal scalp EEG are likely to be mirrored by differences in amygdala activations in particular when recorded in response to emotionally-charged scenes.
Xia Y, Mandic DP, 2017, A Full Mean Square Analysis of CLMS for Second-Order Noncircular Inputs, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 65, Pages: 5578-5590, ISSN: 1053-587X
Xia Y, Douglas SC, Mandic DP, 2017, Performance analysis of the deficient length augmented CLMS algorithm for second order noncircular complex signals, Signal Processing, Vol: 144, Pages: 214-225, ISSN: 0165-1684
The augmented complex LMS (ACLMS) algorithm deals with second order noncircular (improper) input signals, based on widely linear modelling and the use of full second order statistical information. In current analyses of ACLMS, it is implicitly or explicitly assumed that the length of the adaptive filter is equal to that of the unknown system's impulse response (optimal model order). In many applications, however, the length of the adaptive filter is smaller than required, the so called deficient length case, which renders the analysis for a ‘sufficient length’ ACLMS inadequate. To this end, we examine the statistical behaviour of the ACLMS algorithm in undermodelling situations. Exact expressions are developed to completely characterise both the transient and steady-state mean and mean square performances of the deficient length ACLMS for general second order noncircular Gaussian input signals. This is achieved using the recently introduced approximate uncorrelating transform (AUT), in order to jointly diagonalise the covariance and pseudo-covariance matrices with a single singular value decomposition (SVD), which both simplifies the analysis and enables a link between the degree of input noncircularity and the steady state mean square error (MSE) performance of the deficient length ACLMS. Simulations in system identification settings support the analysis.
Nakamura T, Goverdovsky V, mandic D, 2017, In-ear EEG biometrics for feasible and readily collectable real-world person authentication, IEEE Transactions on Information Forensics and Security, Vol: 13, Pages: 648-661, ISSN: 1556-6013
The use of EEG as a biometrics modality has been investigated for about a decade, however its feasibility in real-world applications is not yet conclusively established, mainly due to the issues with collectability and reproducibility. To this end, we propose a readily deployable EEG biometrics system based on a ‘one-fits-all’ viscoelastic generic in-ear EEG sensor (collectability), which does not require skilled assistance or cumbersome preparation. Unlike most existing studies, we consider data recorded over multiple recording days and for multiple subjects (reproducibility) while, for rigour, the training and test segments are not taken from the same recording days. A robust approach is considered based on the resting state with eyes closed paradigm, the use of both parametric (autoregressive model) and non-parametric (spectral) features, and supported by simple and fast cosine distance, linear discriminant analysis and support vector machine classifiers. Both the verification and identification forensics scenarios are considered and the achieved results are on par with the studies based on impractical on-scalp recordings. Comprehensive analysis over a number of subjects, setups, and analysis features demonstrates the feasibility of the proposed ear-EEG biometrics, and its potential in resolving the critical collectability, robustness, and reproducibility issues associated with current EEG biometrics.
Adjei T, von Rosenberg W, Goverdovsky V, et al., 2017, Pain prediction from ECG in vascular surgery, IEEE Journal of Translational Engineering in Health and Medicine, Vol: 5, ISSN: 2168-2372
Varicose vein surgeries are routine outpatient procedures, which are often performed under local anaesthesia. The use of local anaesthesia both minimises the risk to patients and is cost effective, however, a number of patients still experience pain during surgery. Surgical teams must therefore decide to administer either a general or local anaesthetic based on their subjective qualitative assessment of patient anxiety and sensitivity to pain, without any means to objectively validate their decision. To this end, we develop a 3-D polynomial surface fit, of physiological metrics and numerical pain ratings from patients, in order to model the link between the modulation of cardiovascular responses and pain in varicose vein surgeries. Spectral and structural complexity features found in heart rate variability signals, recorded immediately prior to 17 varicose vein surgeries, are used as pain metrics. The so obtained pain prediction model is validated through a leave-one-out validation, and achieved a Kappa coefficient of 0.72 (substantial agreement) and an area below a receiver operating characteristic curve of 0.97 (almost perfect accuracy). This proof-of-concept study conclusively demonstrates the feasibility of the accurate classification of pain sensitivity, and introduces a mathematical model to aid clinicians in the objective administration of the safest and most cost-effective anaesthetic to individual patients.
Xia Y, Mandic DP, 2017, Complementary Mean Square Analysis of Augmented CLMS for Second-Order Noncircular Gaussian Signals, IEEE SIGNAL PROCESSING LETTERS, Vol: 24, Pages: 1413-1417, ISSN: 1070-9908
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