Publications
736 results found
Davies HJ, Nakamura T, Mandic DP, 2019, A Transition Probability Based Classification Model for Enhanced N1 Sleep stage Identification During Automatic Sleep Stage Scoring, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 3641-3644, ISSN: 1557-170X
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- Citations: 4
Hammour G, Yarici M, von Rosenberg W, et al., 2019, Hearables: Feasibility and Validation of In-Ear Electrocardiogram, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 5777-5780, ISSN: 1557-170X
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- Citations: 12
Yuan L, Li C, Mandic D, et al., 2019, Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion, 33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence, Publisher: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE, Pages: 9151-9158, ISSN: 2159-5399
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- Citations: 75
Yu Z, Li S, Mandic D, 2019, Widely Linear Complex-Valued Autoencoder: Dealing with Noncircularity in Generative-Discriminative Models, 28th International Conference on Artificial Neural Networks (ICANN), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 339-350, ISSN: 0302-9743
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- Citations: 1
Variddhisai T, Xiang M, Douglas SC, et al., 2019, Quaternion-Valued Adaptive Filtering via Nesterov's Extrapolation, 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 4868-4872, ISSN: 1520-6149
Talebi SP, Werner S, Li S, et al., 2019, TRACKING DYNAMIC SYSTEMS IN α-STABLE ENVIRONMENTS, 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 4853-4857, ISSN: 1520-6149
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- Citations: 22
Moniri A, Constantinides AG, Mandic DP, 2019, SMART DSP FOR A SMARTER POWER GRID: TEACHING POWER SYSTEM ANALYSIS THROUGH SIGNAL PROCESSING, 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 7883-7887, ISSN: 1520-6149
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- Citations: 1
Calvi GG, Lucic V, Mandic DP, 2019, SUPPORT TENSOR MACHINE FOR FINANCIAL FORECASTING, 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 8152-8156, ISSN: 1520-6149
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- Citations: 7
Stankovic L, Dakovic M, Brajovic M, et al., 2019, A <i>p</i>-Laplacian Inspired Method for Graph Cut, 27th Telecommunications Forum (TELFOR), Publisher: IEEE, Pages: 273-276
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- Citations: 2
Alqurashi Y, Nakamura T, Moss J, et al., 2019, The Efficacy of a Novel In-ear Electroencephalography (EEG) Sensor to Measure Overnight Sleep in Healthy Participants, International Conference of the American-Thoracic-Society, Publisher: AMER THORACIC SOC, ISSN: 1073-449X
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- Citations: 1
Zhang X, Dees BS, Li C, et al., 2019, SIMULTANEOUS DFT AND IDFT THROUGH WIDELY LINEAR CLMS, 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 7750-7754, ISSN: 1520-6149
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- Citations: 1
Calvi GG, Kisil I, Mandic DP, 2018, Feature Fusion via Tensor Network Summation, European Signal Processing Conference (EUSIPCO), Publisher: IEEE COMPUTER SOC, Pages: 2623-2627, ISSN: 2076-1465
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
Sex differences in the study of the human physiological response to mental stress are often erroneously ignored. To this end, we set out to show that our understanding of the stress response is fundamentally altered once sex differences are taken into account. This is achieved by comparing the heart rate variability (HRV) signals acquired during mental maths tests from ten females and ten males of similar maths ability; all females were in the follicular phase of their menstrual cycle. For rigor, the HRV signals from this pilot study were analyzed using temporal, spectral and nonlinear signal processing techniques, which all revealed significant statistical differences between the sexes, with the stress-induced increases in the heart rates from the males being significantly larger than those from the females (p-value = 4.4 × 10−4). In addition, mental stress produced an overall increase in the power of the low frequency component of HRV in the males, but caused an overall decrease in the females. The stress-induced changes in the power of the high frequency component were even more profound; it greatly decreased in the males, but increased in the females. We also show that mental stress was followed by the expected decrease in sample entropy, a nonlinear measure of signal regularity, computed from the males' HRV signals, while overall, stress manifested in an increase in the sample entropy computed from the females' HRV signals. This finding is significant, since mental stress is commonly understood to be manifested in the decreased entropy of HRV signals. The significant difference (p-value = 2.1 × 10−9) between the changes in the entropies from the males and females highlights the pitfalls in ignoring sex in the formation of a physiological hypothesis. Furthermore, it has been argued that estrogen attenuates the effect of catecholamine stress hormones; the findings from this investigation suggest for the first time that the conventionally c
Kisil I, Moniri A, Mandic DP, 2018, TENSOR ENSEMBLE LEARNING FOR MULTIDIMENSIONAL DATA, IEEE Global Conference on Signal and Information Processing (GlobalSIP), Publisher: IEEE, Pages: 1358-1362, ISSN: 2376-4066
Alqurashi YD, Nakamura T, Goverdovsky V, et al., 2018, A novel in-ear sensor to determine sleep latency during the Multiple Sleep Latency Test in healthy adults with and without sleep restriction, Nature and Science of Sleep, Vol: 10, Pages: 385-396, ISSN: 1179-1608
Objectives: Detecting sleep latency during the Multiple Sleep Latency Test (MSLT) using electroencephalogram (scalp-EEG) is time-consuming. The aim of this study was to evaluate the efficacy of a novel in-ear sensor (in-ear EEG) to detect the sleep latency, compared to scalp-EEG, during MSLT in healthy adults, with and without sleep restriction.Methods: We recruited 25 healthy adults (28.5±5.3 years) who participated in two MSLTs with simultaneous recording of scalp and in-ear EEG. Each test followed a randomly assigned sleep restriction (≤5 hours sleep) or usual night sleep (≥7 hours sleep). Reaction time and Stroop test were used to assess the functional impact of the sleep restriction. The EEGs were scored blind to the mode of measurement and study conditions, using American Academy of Sleep Medicine 2012 criteria. The Agreement between the scalp and in-ear EEG was assessed using Bland-Altman analysis.Results: Technically acceptable data were obtained from 23 adults during 69 out of 92 naps in the sleep restriction condition and 25 adults during 85 out of 100 naps in the usual night sleep. Meaningful sleep restrictions were confirmed by an increase in the reaction time (mean ± SD: 238±30 ms vs 228±27 ms; P=0.045). In the sleep restriction condition, the in-ear EEG exhibited a sensitivity of 0.93 and specificity of 0.80 for detecting sleep latency, with a substantial agreement (κ=0.71), whereas after the usual night’s sleep, the in-ear EEG exhibited a sensitivity of 0.91 and specificity of 0.89, again with a substantial agreement (κ=0.79).Conclusion: The in-ear sensor was able to detect reduced sleep latency following sleep restriction, which was sufficient to impair both the reaction time and cognitive function. Substantial agreement was observed between the scalp and in-ear EEG when measuring sleep latency. This new in-ear EEG technology is shown to have a significant value as a convenient measure for sleep lat
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
Oliveira V, Martins R, Liow N, et al., 2018, Prognostic accuracy of heart rate variability analysis in neonatal encephalopathy: a systematic review, Neonatology, Vol: 115, Pages: 59-67, ISSN: 1661-7800
BACKGROUND: Heart rate variability analysis offers real-time quantification of autonomic disturbance after perinatal asphyxia, and may therefore aid in disease stratification and prognostication after neonatal encephalopathy (NE). OBJECTIVE: To systematically review the existing literature on the accuracy of early heart rate variability (HRV) to predict brain injury and adverse neurodevelopmental outcomes after NE. DESIGN/METHODS: We systematically searched the literature published between May 1947 and May 2018. We included all prospective and retrospective studies reporting HRV metrics, within the first 7 days of life in babies with NE, and its association with adverse outcomes (defined as evidence of brain injury on magnetic resonance imaging and/or abnormal neurodevelopment at ≥1 year of age). We extracted raw data wherever possible to calculate the prognostic indices with confidence intervals. RESULTS: We retrieved 379 citations, 5 of which met the criteria. One further study was excluded as it analysed an already-included cohort. The 4 studies provided data on 205 babies, 80 (39%) of whom had adverse outcomes. Prognostic accuracy was reported for 12 different HRV metrics and the area under the curve (AUC) varied between 0.79 and 0.94. The best performing metric reported in the included studies was the relative power of high-frequency band, with an AUC of 0.94. CONCLUSIONS: HRV metrics are a promising bedside tool for early prediction of brain injury and neurodevelopmental outcome in babies with NE. Due to the small number of studies available, their heterogeneity and methodological limitations, further research is needed to refine this tool so that it can be used in clinical practice.
Douglas SC, Mandic DP, 2018, AFFINE-PROJECTION LEAST-MEAN-MAGNITUDE-PHASE ALGORITHMS USING A POSTERIORI UPDATES, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 4154-4158
Hemakom A, Goverdovsky V, Mandic DP, 2018, EAR-EEG FOR DETECTING INTER-BRAIN SYNCHRONISATION IN CONTINUOUS COOPERATIVE MULTI-PERSON SCENARIOS, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 911-915
Kisil I, Calvi GG, Cichocki A, et al., 2018, COMMON AND INDIVIDUAL FEATURE EXTRACTION USING TENSOR DECOMPOSITIONS: A REMEDY FOR THE CURSE OF DIMENSIONALITY?, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 6299-6303
Dees BS, Douglas SC, Mandic DP, 2018, COMPLEMENTARY COMPLEX-VALUED SPECTRUM FOR REAL-VALUED DATA: REAL TIME ESTIMATION OF THE PANORAMA THROUGH CIRCULARITY-PRESERVING DFT, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 3999-4003
Li Z, Pei W, Xia Y, et al., 2018, WIDELY LINEAR CLMS BASED CANCELATION OF NONLINEAR SELF-INTERFERENCE IN FULL-DUPLEX DIRECT-CONVERSION TRANSCEIVERS, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 4329-4333
Dees BS, Xia Y, Douglas SC, et al., 2018, CORRENTROPY-BASED ADAPTIVE FILTERING OF NONCIRCULAR COMPLEX DATA, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 4339-4343
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
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
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
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 MA, 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
Radiofrequency catheter ablation is one of the commonly available therapeutic methods for patients suffering from cardiac arrhythmias. The prerequisite of successful ablation is sufficient energy delivery at the target site. However, cardiac and respiratory motion, coupled with endocardial irregularities, can cause catheter drift and dispersion of the radiofrequency energy, thus prolonging procedure time, damaging adjacent tissue, and leading to electrical reconnection of temporarily ablated regions. Therefore, positional accuracy and stability of the catheter tip during energy delivery is of great importance for the outcome of the procedure. This paper presents an analytical scheme for assessing catheter tip stability, whereby a sequence of catheter tip motion recorded at sparse locations on the endocardium is decomposed. The spatial sliding component along the endocardial wall is extracted from the recording and maximal slippage and its associated probability are computed at each mapping point. Finally, a global map is generated, allowing the assessment of potential areas that are compromised by tip slippage. The proposed framework was applied to 40 retrospective studies of congenital heart disease patients and further validated on phantom data and simulations. The results show a good correlation with other intraoperative factors, such as catheter tip contact force amplitude and orientation, and with clinically documented anatomical areas of high catheter tip instability.
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
In future full-duplex communications, the cancellation of self-interference (SI) arising from hardware nonidealities will play an important role in the design of mobile-scale devices. To this end, we introduce an optimal digital SI cancellation solution for shared-antenna-based direct-conversion transceivers. To establish that the underlying widely linear signal model is not adequate for strong transmit signals, the impact of various circuit imperfections, including power amplifier distortion, frequency-dependent I/Q imbalance, quantization noise, and thermal noise, on the performance of the conventional augmented least mean square (LMS) based SI canceller, is analyzed. In order to achieve a sufficient signal-to-interference-plus-noise ratio when the nonlinear SI components are not negligible, we propose an augmented nonlinear LMS based SI canceller for a joint cancellation of both the linear and nonlinear SI components by virtue of a widely nonlinear model fit. A rigorous mean and mean square performance evaluation is conducted to justify the performance advantages of the proposed scheme over the conventional augmented LMS solution. Simulations on orthogonal frequency division multiplexing-based wireless local area network standard compliant waveforms support the analysis.
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
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