584 results found
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
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 [Lecture Notes], IEEE Signal Processing Magazine, Vol: 36, Pages: 110-117, ISSN: 1053-5888
© 1991-2012 IEEE. In Part 1 of this «Lecture Notes» article , we introduced a modern perspective on the standard tools for power system analysis-the Clarke and related transforms-through the lens of data analytics. We also indicated their limitations when dealing with unbalanced power system conditions.
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
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., J Vasc Surg Venous Lymphat Disord, Vol: 7, Pages: 382-386
OBJECTIVE: Local anesthetic endovenous procedures were shown to reduce recovery time, to decrease postoperative pain, and to more quickly return the patient to baseline activities. However, a substantial number of patients experience pain during these procedures. The autonomic nervous system modulates pain perception, and its influence on stress response can be noninvasively quantified using heart rate variability (HRV) indices. The aim of our study was to evaluate whether preoperative baseline HRV can predict intraoperative pain during local anesthetic varicose vein surgery. METHODS: Patients scheduled for radiofrequency ablation were included in the study. They had their electrocardiograms recorded from a single channel of a custom-made amplifier. Each patient preoperatively filled in forms Y-1 and Y-2 of Spielberger's State and Trait Anxiety Inventory, completed the Aberdeen Varicose Vein Questionnaire, and rated anxiety level on a numeric scale. Postoperatively, patients filled in the pain they felt during the procedure on the numeric pain intensity scale. MATLAB software (MathWorks, Natick, Mass) was used to extract R waves and to generate HRV signals, and a mathematical model was created to predict the pain score for each patient. RESULTS: In multivariable analysis, we looked into correlation between reported patient's pain score (rPPS) and Aberdeen Varicose Vein Questionnaire score, preoperative forms Y-1 and Y-2, preoperative anxiety level, and predicted patient's pain (pPPS) score. Multivariable analysis found association only between rPPS and pPPS. The pPPS was significantly correlated with rPPS (R = 0.807; P < .001) with accuracy of prediction of 65.2%, which was calculated from R2 on a linear regression model. CONCLUSIONS: This preliminary study shows that preoperative HRV can accurately predict patients' pain, allowing patients with higher predicted score to have the procedure under general anesthesia.
Adjei T, von Rosenberg W, Nakamura T, et al., The ClassA Framework: HRV Based Assessment of SNS and PNS Dynamics Without LF-HF Controversies, Frontiers in Physiology, Vol: 10
Nakamura T, Davies H, Mandic D, Scalable automatic sleep staging in the era of Big Data, IEEE EMBC 2019
Nakamura T, Alqurashi Y, Morrell M, et al., Hearables: Automatic overnight sleep monitoring with standardised in-ear EEG sensor, IEEE Transactions on Biomedical Engineering, ISSN: 0018-9294
Mandic D, Took CC, 2019, Reply to "Comments on 'The Quaternion LMS Algorithm for Adaptive Filtering of Hypercomplex Processes"', IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 67, Pages: 1959-1959, ISSN: 1053-587X
Mandic DP, Kanna S, Xia Y, et al., 2019, A Data Analytics Perspective of Power Grid Analysis-Part 1: The Clarke and Related Transforms, IEEE SIGNAL PROCESSING MAGAZINE, Vol: 36, Pages: 110-116, ISSN: 1053-5888
Cheng H, Xia Y, Huang Y, et al., 2019, Joint Channel Estimation and Tx/Rx I/Q Imbalance Compensation for GFDM Systems, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, Vol: 18, Pages: 1304-1317, ISSN: 1536-1276
Hansen ST, Hemakom A, Safeldt MG, et al., 2019, Unmixing Oscillatory Brain Activity by EEG Source Localization and Empirical Mode Decomposition, COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, ISSN: 1687-5265
Xiang M, Dees BS, Mandic DP, 2019, Multiple-Model Adaptive Estimation for 3-D and 4-D Signals: A Widely Linear Quaternion Approach, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol: 30, Pages: 72-84, ISSN: 2162-237X
Oliveira V, Martins R, Liow N, et al., 2019, Prognostic Accuracy of Heart Rate Variability Analysis in Neonatal Encephalopathy: A Systematic Review, NEONATOLOGY, Vol: 115, Pages: 59-67, ISSN: 1661-7800
Phan AH, Yamagishi M, Mandic D, et al., 2019, Quadratic programming over ellipsoids with applications to constrained linear regression and tensor decomposition, Neural Computing and Applications, ISSN: 0941-0643
© 2019, Springer-Verlag London Ltd., part of Springer Nature. A novel algorithm to solve the quadratic programming (QP) problem over ellipsoids is proposed. This is achieved by splitting the QP problem into two optimisation sub-problems, (1) quadratic programming over a sphere and (2) orthogonal projection. Next, an augmented-Lagrangian algorithm is developed for this multiple constraint optimisation. Benefitting from the fact that the QP over a single sphere can be solved in a closed form by solving a secular equation, we derive a tighter bound of the minimiser of the secular equation. We also propose to generate a new positive semidefinite matrix with a low condition number from the matrices in the quadratic constraint, which is shown to improve convergence of the proposed augmented-Lagrangian algorithm. Finally, applications of the quadratically constrained QP to bounded linear regression and tensor decomposition paradigms are presented.
Adjei T, Xue J, Mandic DP, 2018, The Female Heart: Sex Differences in the Dynamics of ECG in Response to Stress, FRONTIERS IN PHYSIOLOGY, Vol: 9, ISSN: 1664-042X
Stott AE, Kanna S, Mandic DP, 2018, Widely linear complex partial least squares for latent subspace regression, SIGNAL PROCESSING, Vol: 152, Pages: 350-362, ISSN: 0165-1684
Nakamura T, Alqurashi YD, Morrell MJ, et al., 2018, Automatic detection of drowsiness using in-ear EEG
© 2018 IEEE. Sleep monitoring with wearable electroencephalography (EEG) has recently been validated and reported in the research community. One such device is our ultra-wearable, unobtrusive, and inconspicuous in-ear EEG system, which has already been demonstrated to be next-generation solution for out-of-clinic sleep monitoring. We here provide a further proof of concept of the utility of ear-EEG in day time drowsiness monitoring in the real-world. For rigour, hypnograms are obtained from manually scored daytime nap recordings from twentythree subjects, while a complexity science feature-structural complexity extracted from scalp- and ear-EEG recordings - is used in the classification stage, in conjunction with a binary-class support vector machine (SVM). The achieved drowsiness classification accuracies range from 80.0% to 82.9% for ear-EEG, with the corresponding accuracies for scalp-EEG ranging from 86.8 % to 88.8 %. Given the notoriously difficult to classify drowsiness related changes in EEG (similar to the issues with the NREM Stage 1), this conclusively confirms the feasibility of in-ear EEG for automatic light sleep classification. This also promises a key stepping stone towards continuous, discreet, and user-friendly wearable out-of-clinic drowsiness monitoring in the real-world, with numerous applications in the monitoring the state of body and mind of pilots, train drivers, and tele-operators.
Talebi SP, Werner S, Mandic DP, 2018, Distributed Adaptive Filtering of alpha-Stable Signals, IEEE SIGNAL PROCESSING LETTERS, Vol: 25, Pages: 1450-1454, ISSN: 1070-9908
Cheng H, Xia Y, Huang Y, et al., 2018, A Normalized Complex LMS Based Blind I/Q Imbalance Compensator for GFDM Receivers and Its Full Second-Order Performance Analysis, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 66, Pages: 4701-4712, ISSN: 1053-587X
Li Z, Xia Y, Pei W, et al., 2018, An Augmented Nonlinear LMS for Digital Self-Interference Cancellation in Full-Duplex Direct-Conversion Transceivers, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 66, Pages: 4065-4078, ISSN: 1053-587X
Xia Y, Douglas SC, Mandic DP, 2018, A perspective on CLMS as a deficient length augmented CLMS: Dealing with second order noncircularity, SIGNAL PROCESSING, Vol: 149, Pages: 236-245, ISSN: 0165-1684
Brajović M, Stanković L, Daković M, et al., 2018, Additive noise influence on the bivariate two-component signal decomposition, Pages: 1-4
© 2018 IEEE. Decomposition of multicomponent signals overlapping in the time-frequency domain is a challenging research topic. To solve this problem, many approaches have been proposed so far, but only to be efficient for some particular signal classes. Recently, we have proposed a decomposition approach for multivariate multicomponent signals, based on the time-frequency signal analysis and concentration measures. The proposed solution is efficient for multivariate signals partially overlapped in the time-frequency plane regardless of the non-stationarity type of particular signal components. This decomposition approach is shown to be also efficient in noisy environments. In this paper, we investigate the limits of the decomposition efficiency subject to the signal-to-noise ratio and initial phase differences between the signals from different channels. The paper is focused on the decomposition of bivariate two-component signals.
Constantinescu MAM, Lee S-L, Ernst S, et al., 2018, Probabilistic guidance for catheter tip motion in cardiac ablation procedures, MEDICAL IMAGE ANALYSIS, Vol: 47, Pages: 1-14, ISSN: 1361-8415
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
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
Xiang M, Kanna S, Mandic DP, 2018, Performance Analysis of Quaternion-Valued Adaptive Filters in Nonstationary Environments, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 66, Pages: 1566-1579, ISSN: 1053-587X
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
Xia Y, Douglas SC, Mandic DP, 2018, 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
Nakamura T, Goverdovsky V, Mandic DP, 2018, 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
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