Imperial College London

ProfessorDaniloMandic

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Signal Processing
 
 
 
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Contact

 

+44 (0)20 7594 6271d.mandic Website

 
 
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Assistant

 

Miss Vanessa Rodriguez-Gonzalez +44 (0)20 7594 6267

 
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Location

 

813Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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619 results found

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.

Conference paper

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

Journal article

Hansen ST, Hemakom A, Safeldt MG, Krohne LK, Madsen KH, Siebner HR, Mandic DP, Hansen LKet al., 2019, Unmixing oscillatory brain activity by EEG source localization and empirical mode decomposition, Computational Intelligence and Neuroscience, Vol: 2019, ISSN: 1687-5265

Neuronal activity is composed of synchronous and asynchronous oscillatory activity at different frequencies. The neuronal oscillations occur at time scales well matched to the temporal resolution of electroencephalography (EEG); however, to derive meaning from the electrical brain activity as measured from the scalp, it is useful to decompose the EEG signal in space and time. In this study, we elaborate on the investigations into source-based signal decomposition of EEG. Using source localization, the electrical brain signal is spatially unmixed and the neuronal dynamics from a region of interest are analyzed using empirical mode decomposition (EMD), a technique aimed at detecting periodic signals. We demonstrate, first in simulations, that the EMD is more accurate when applied to the spatially unmixed signal compared to the scalp-level signal. Furthermore, on EEG data recorded simultaneously with transcranial magnetic stimulation (TMS) over the hand area of the primary motor cortex, we observe a link between the peak to peak amplitude of the motor-evoked potential (MEP) and the phase of the decomposed localized electrical activity before TMS onset. The results thus encourage combination of source localization and EMD in the pursuit of further insight into the mechanisms of the brain with respect to the phase and frequency of the electrical oscillations and their cortical origin.

Journal article

Mandic DP, Kanna S, Xia Y, Moniri A, Junyent-Ferre A, Constantinides AGet 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

Journal article

Li Z, Deng W, Pei W, Xia Y, Mandic DPet al., 2019, Refreshing Digital Communications Curriculum with RFID Technology: A Participatory Approach, 23rd IEEE International Conference on Digital Signal Processing (DSP), Publisher: IEEE, ISSN: 1546-1874

Conference paper

Moniri A, Kisil I, Constantinides AG, Mandic DPet al., 2019, Refreshing DSP Courses through Biopresence in the Curriculum: A Successful Paradigm, 23rd IEEE International Conference on Digital Signal Processing (DSP), Publisher: IEEE, ISSN: 1546-1874

Conference paper

Cheng H, Xia Y, Huang Y, Yang L, Mandic DPet 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

Journal article

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

Conference paper

Hammour G, Yarici M, von Rosenberg W, Mandic DPet 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

Conference paper

Yuan L, Li C, Mandic D, Cao J, Zhao Qet 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

Conference paper

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

Conference paper

Variddhisai T, Xiang M, Douglas SC, Mandic DPet 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

Conference paper

Talebi SP, Werner S, Li S, Mandic DPet al., 2019, TRACKING DYNAMIC SYSTEMS IN alpha-STABLE ENVIRONMENTS, 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 4853-4857, ISSN: 1520-6149

Conference paper

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

Conference paper

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

Conference paper

Stankovic L, Dakovic M, Brajovic M, Mandic Det al., 2019, A p-Laplacian Inspired Method for Graph Cut, 27th Telecommunications Forum (TELFOR), Publisher: IEEE, Pages: 273-276

Conference paper

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

Journal article

Zhang X, Dees BS, Li C, Xia Y, Yang L, Mandic DPet 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

Conference paper

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

Conference paper

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

Journal article

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

Conference paper

Alqurashi YD, Nakamura T, Goverdovsky V, Moss J, Polkey MI, Mandic DP, Morrell MJet 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

Journal article

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

Journal article

Nakamura T, Alqurashi YD, Morrell MJ, Mandic DPet 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.

Conference paper

Oliveira V, Martins R, Liow N, Teiserskas J, von Rosenberg W, Adjei T, Shivamurthappa V, Lally PJ, Mandic D, Thayyil Set 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.

Journal article

Kisil I, Calvi GG, Cichocki A, Mandic DPet 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

Conference paper

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

Conference paper

Dees BS, Xia Y, Douglas SC, Mandic DPet 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

Conference paper

Li Z, Pei W, Xia Y, Wang K, Mandic DPet 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

Conference paper

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

Conference paper

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