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
to

606 results found

Stankovic L, Brajovic M, Dakovic M, Mandic Det al., 2020, On the decomposition of multichannel nonstationary multicomponent signals, SIGNAL PROCESSING, Vol: 167, ISSN: 0165-1684

Journal article

Stankovic L, Mandic DP, Dakovic M, Kisil Iet al., 2020, Demystifying the Coherence Index in Compressive Sensing [Lecture Notes], IEEE Signal Processing Magazine, Vol: 37, Pages: 152-162, ISSN: 1053-5888

© 1991-2012 IEEE. The existence and uniqueness conditions are a prerequisite to ensure the reliable reconstruction of sparse signals from reduced sets of measurements within the compressive sensing (CS) paradigm. However, despite their underpinning role in practical applications, the existing uniqueness relations are either computationally prohibitive to implement [the restricted isometry property (RIP)] or involve mathematical tools that are beyond the standard background of engineering graduates (the coherence index). This may introduce conceptual and computational obstacles in the development of engineering intuition, design of suboptimal practical solutions, and understanding of theoretical and practical limitations of the CS framework.

Journal article

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

Journal article

Nakamura T, Alqurashi Y, Morrell M, Mandic Det 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.

Journal article

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

Journal article

Stankovic L, Mandic DP, Dakovic M, Kisil I, Sejdic E, Constantinides AGet 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

Journal article

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

Journal article

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

Journal article

Solo V, Greco MS, Mandic D, Djuric P, Spanias A, Bugallo MFet al., 2019, Innovation Starts With Education ICASSP 2019 Education Panel, IEEE SIGNAL PROCESSING MAGAZINE, Vol: 36, Pages: 135-+, ISSN: 1053-5888

Journal article

Mandic DP, Djuric PM, Cichocki A, Cheong-Took C, Sanei S, Hanzo Let 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

Journal article

Phan A-H, Cichocki A, Oseledets I, Calvi GG, Ahmadi-Asl S, Mandic DPet al., 2019, Tensor Networks for Latent Variable Analysis: Higher Order Canonical Polyadic Decomposition., IEEE Trans Neural Netw Learn Syst

The canonical polyadic decomposition (CPD) is a convenient and intuitive tool for tensor factorization; however, for higher order tensors, it often exhibits high computational cost and permutation of tensor entries, and these undesirable effects grow exponentially with the tensor order. Prior compression of tensor in-hand can reduce the computational cost of CPD, but this is only applicable when the rank $R$ of the decomposition does not exceed the tensor dimensions. To resolve these issues, we present a novel method for CPD of higher order tensors, which rests upon a simple tensor network of representative inter-connected core tensors of orders not higher than 3. For rigor, we develop an exact conversion scheme from the core tensors to the factor matrices in CPD and an iterative algorithm of low complexity to estimate these factor matrices for the inexact case. Comprehensive simulations over a variety of scenarios support the proposed approach.

Journal article

Oliveira V, von Rosenberg W, Montaldo P, Adjei T, Mendoza J, Shivamurthappa V, Mandic D, Thayyil Set 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

Journal article

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

Journal article

Xia Y, Tao S, Li Z, Xiang M, Pei W, Mandic DPet 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

Journal article

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

Journal article

Hammour G, Yarici M, Rosenberg WV, Mandic DPet al., 2019, Hearables: Feasibility and Validation of In-Ear Electrocardiogram., Pages: 5777-5780, ISSN: 1557-170X

Out-of-clinic, continuous monitoring of vital signs is envisaged to become the backbone of future e-health. The emerging wrist worn devices have already proven to be a success in the measurement of pulse, however, a susceptibility to artefacts and missing data caused by regular motion in everyday activities, and the inability to continuously acquire the electrocardiogram call into question the utility of this technology in future e-Health. With this in mind, the head, and in particular the ear canals, have been investigated as possible locations for wearable devices. The ears offer a stable position relative to the vital signs during everyday activities, such as sitting, walking, running and sleeping, as well as being a practical and widely accepted base for wearable accessories. This all suggests that the ear canals are a most natural location for physiological sensing in the community. This work addresses the feasibility of recording the ECG from the ear canals, from a one-fits-all, user-friendly device. For rigour and clarity, we quantitatively compare the timings of the identified P-, QRS-, and T-waves within Ear-ECG and standard arm-ECG. Finally, to depict a future e-Health scenario for the Ear-ECG technology, a case study with an abnormal heart condition, the ventricular bigeminy, is presented. A comprehensive study over ten subjects demonstrates conclusively the possibility of in-ear cardiac monitoring in normal daily life.

Conference paper

Stott AE, Dees BS, Kisil I, Mandic DPet 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

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., Pages: 3641-3644, ISSN: 1557-170X

Automatic sleep staging provides a cheaper, faster and more accessible alternative for evaluating sleep patterns and quality compared with manual hypnogram scoring performed by a clinician. Traditionally, classification methods treat sleep stages independently of their temporal order, despite sleep patterns themselves being highly sequential. Such independent sleep stage classification can result in poor sensitivity and precision, in particular when attempting to classify the sleep stage N1, otherwise known as the transition stage of sleep which links periods of wakefulness to periods of deep sleep. To this end, we propose a novel transition sleep classification method which aims to improve classification accuracy. This is achieved by utilising both the temporal information of previous stages and treating the transitions between stages as classes in their own right. Simulations on publicly available polysomnography (PSG) data and a comprehensive performance comparison with standard classifiers demonstrate a marked improvement achieved by the proposed method in both N1 sensitivity and precision across all considered classifiers. This includes an increase in N1 precision from 0.01% to 36.75% in an MLP classifier, and an increase in both accuracy and Cohen's kappa value in two of the three classifiers. Overall best mean performance is obtained by transition classification with a random forest classifier (RF) which achieved a kappa value of κ = 0.75 (substantial agreement), and an N1 stage precision of 58%.

Conference paper

Powezka K, Adjei T, von Rosenberg W, Normahani P, Goverdovsky V, Standfield NJ, Mandic DP, Jaffer Uet 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

Journal article

Kanna S, Moniri A, Xia Y, Constantinides AG, Mandic DPet 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

Journal article

Li Z, Xia Y, Pei W, Mandic DPet 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

Journal article

Adjei T, von Rosenberg W, Nakamura T, Chanwimalueang T, Mandic Det 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

Journal article

Phan A-H, Yamagishi M, Mandic D, Cichocki Aet al., Quadratic programming over ellipsoids with applications to constrained linear regression and tensor decomposition, Neural Computing and Applications, ISSN: 0941-0643

Journal article

Nakamura T, Davies H, Mandic D, 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

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