Publications
736 results found
Davies HJ, Bachtiger P, Williams I, et al., 2022, Wearable in-ear PPG: detailed respiratory variations enable classification of COPD, IEEE Transactions on Biomedical Engineering, Vol: 69, ISSN: 0018-9294
An ability to extract detailed spirometry-like breath-ing waveforms from wearable sensors promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has been researched in depth for estimation of respiration rate, given that it varies with respiration through overall intensity, pulse amplitude and pulse interval. We compare and contrast the extraction of these three respiratory modes from both the ear canal and finger and show a marked improvement in the respiratory power for respiration induced intensity variations and pulse amplitude variations when recording from the ear canal. We next employ a data driven multi-scale method, noise assisted multivariate empirical mode decomposition (NA-MEMD), which allows for simultaneous analysis of all three respiratory modes to extract detailed respiratory waveforms from in-ear PPG. For rigour, we considered in-ear PPG recordings from healthy subjects, both older and young, patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) and healthy subjects with artificially obstructed breathing. Specific in-ear PPG waveform changes are observed for COPD, such as a decreased inspiratory duty cycle and an increased inspiratory magnitude, when compared with expiratory magnitude. These differences are used to classify COPD from healthy and IPF waveforms with a sensitivity of 87% and an overall accuracy of 92%. Our findings indicate the promise of in-ear PPG for COPD screening and unobtrusive respiratory monitoring in ambulatory scenarios and in consumer wearables.
Lo Giudice M, Mammone N, Ieracitano C, et al., 2022, Explainable Deep Learning Classification of Respiratory Sound for Telemedicine Applications, Pages: 391-403, ISSN: 1865-0929
The recent pandemic crisis combined with the explosive growth of Artificial Intellignence (AI) algorithms has highlighted the potential benefits of telemedicine for decentralised, accurate and automated clinical diagnoses. One of the most popular and essential diagnoses is the auscultation; it is non-invasive, real-time and very informative diagnoses for knowing the state of the respiratory system. To implement a possible automated auscultation analysis, the decision-making explanation of complex models (such as Deep Learning models) is crucial for trusted application in the clinical domain. In this context, we will analyse the behaviour of a Convolutional Neural Network (CNN) in classifying the largest publicly available database of respiratory sounds, originally compiled to support the scientific challenge organized at Int. Conf. on Biomedical Health Informatics (ICBHI17). It contains respiratory sounds (recorded with auscultation) of normal respiratory cycles, crackles, wheezes and both. To capture the phonetically important features of breath sounds, the Mel-Frequency Cepstrum (MFC) for short-term power spectrum representation was applied. The MFC allowed us to identify latent features without losing the temporal information so that we could easily identify the correspondence of the features to the starting sound. The MFCs were used as input to the proposed CNN who was able to classify the four above-mentioned respiratory classes with an accuracy of 72.8%. Despite interesting results, the main focus of the present study was to investigate how the CNN achieved this classification. The explainable Artificial Intelligence (xAI) technique of Gradient-weighted Class Activation Mapping (Grad-CAM) was applied. xAI made it possible to visually identify the most relevant areas, especially for the recognition of abnormal sounds, which is crucial for inspecting the correct learning of the CNN.
Leng Y, Chen Z, Guo J, et al., 2022, BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis, ISSN: 1049-5258
Binaural audio plays a significant role in constructing immersive augmented and virtual realities. As it is expensive to record binaural audio from the real world, synthesizing them from mono audio has attracted increasing attention. This synthesis process involves not only the basic physical warping of the mono audio, but also room reverberations and head/ear related filtrations, which, however, are difficult to accurately simulate in traditional digital signal processing. In this paper, we formulate the synthesis process from a different perspective by decomposing the binaural audio into a common part that shared by the left and right channels as well as a specific part that differs in each channel. Accordingly, we propose BinauralGrad, a novel two-stage framework equipped with diffusion models to synthesize them respectively. Specifically, in the first stage, the common information of the binaural audio is generated with a single-channel diffusion model conditioned on the mono audio, based on which the binaural audio is generated by a two-channel diffusion model in the second stage. Combining this novel perspective of two-stage synthesis with advanced generative models (i.e., the diffusion models), the proposed BinauralGrad is able to generate accurate and high-fidelity binaural audio samples. Experiment results show that on a benchmark dataset, BinauralGrad outperforms the existing baselines by a large margin in terms of both object and subject evaluation metrics (Wave L2: 0.128 vs. 0.157, MOS: 3.80 vs. 3.61). The generated audio samples3 and code4 are available online.
Xiao H, Mandic DP, 2022, Variational Embedding Multiscale Sample Entropy: A Tool for Complexity Analysis of Multichannel Systems, ENTROPY, Vol: 24
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- Citations: 3
Xiao H, Chanwimalueang T, Mandic DP, 2022, MULTIVARIATE MULTISCALE COSINE SIMILARITY ENTROPY, 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 5997-6001, ISSN: 1520-6149
Talebi SP, Werner S, Mandic DP, 2022, Fractional-Order Learning Systems, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC), Publisher: IEEE, ISSN: 2161-4393
Occhipinti E, Davies HJ, Hammour G, et al., 2022, Hearables: Artefact removal in Ear-EEG for continuous 24/7 monitoring, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC), Publisher: IEEE, ISSN: 2161-4393
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- Citations: 3
Talebi SP, Werner S, Xia Y, et al., 2022, A Joint Particle Filter for Quaternion-Valued α-Stable Signals via the Characteristic Function, IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM), Publisher: IEEE, Pages: 390-394
Xu YL, Konstantinidis K, Li S, et al., 2022, LOW-COMPLEXITY ATTENTION MODELLING VIA GRAPH TENSOR NETWORKS, 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 3928-3932, ISSN: 1520-6149
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- Citations: 1
Chen Z, Tan X, Wang K, et al., 2022, INFERGRAD: IMPROVING DIFFUSION MODELS FOR VOCODER BY CONSIDERING INFERENCE IN TRAINING, 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 8432-8436, ISSN: 1520-6149
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- Citations: 2
Arroyo A, Scalzo B, Stankovic L, et al., 2022, DYNAMIC PORTFOLIO CUTS: A SPECTRAL APPROACH TO GRAPH-THEORETIC DIVERSIFICATION, 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 5468-5472, ISSN: 1520-6149
Michalas L, Konstantinidis K, Farinelli P, et al., 2022, RF MEMS Switch Design Methodology by Electromagnetic Simulations and Machine Learning, 52nd European Microwave Conference (EuMC), Publisher: IEEE, ISSN: 2325-0305
Michalas L, Konstantinidis K, Farinelli P, et al., 2022, RF MEMS Switch Design Methodology by Electromagnetic Simulations and Machine Learning, 52nd European Microwave Conference (EuMC), Publisher: IEEE, ISSN: 2325-0305
Konstantinidis K, Xu YL, Zhao Q, et al., 2022, VARIATIONAL BAYESIAN TENSOR NETWORKS WITH STRUCTURED POSTERIORS, 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 3638-3642, ISSN: 1520-6149
Michalas L, Konstantinidis K, Farinelli P, et al., 2022, RF MEMS Switch Design Methodology by Electromagnetic Simulations and Machine Learning, 52nd European Microwave Conference (EuMC), Publisher: IEEE, Pages: 369-372, ISSN: 2325-0305
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- Citations: 1
Li Z, Deng W, Zhu Z, et al., 2021, A layer-wise distribution analysis of the WLMMSE-SIC MIMO receiver for rectilinear or quasi-rectilinear signals, SIGNAL PROCESSING, Vol: 189, ISSN: 0165-1684
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- Citations: 1
Xu D, Zhang S, Zhang H, et al., 2021, Convergence of the RMSProp deep learning method with penalty for nonconvex optimization, NEURAL NETWORKS, Vol: 139, Pages: 17-23, ISSN: 0893-6080
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- Citations: 60
Li S, Yu Z, Mandic D, 2021, A Universal Framework for Learning the Elliptical Mixture Model, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol: 32, Pages: 3181-3195, ISSN: 2162-237X
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- Citations: 2
Ruiz-Garcia A, Schmidhuber J, Palade V, et al., 2021, Deep neural network representation and Generative Adversarial Learning, NEURAL NETWORKS, Vol: 139, Pages: 199-200, ISSN: 0893-6080
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- Citations: 4
Stankovic L, Lerga J, Mandic D, et al., 2021, From Time-Frequency to Vertex-Frequency and Back, MATHEMATICS, Vol: 9
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- Citations: 1
Khalifa Y, Mandic D, Sejdic E, 2021, A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals, INFORMATION FUSION, Vol: 69, Pages: 52-72, ISSN: 1566-2535
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- Citations: 21
Bugallo MF, Constantinides AG, Mandic DP, et al., 2021, Innovation Starts With Education, IEEE SIGNAL PROCESSING MAGAZINE, Vol: 38, Pages: 11-13, ISSN: 1053-5888
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- Citations: 1
Scalzo B, Konstantinidis A, Mandic DP, 2021, Analysis of global fixed-income returns using multilinear tensor algebra, Journal of Fixed Income, Vol: 30, Pages: 32-52, ISSN: 1059-8596
Global fixed-income returns exhibit highly structured correlations across maturities and economies (data modes), and their modeling therefore requires analysis tools that are capable of directly capturing the inherent multiway couplings present in such multimodal data. Yet, current analyses typically employ “flat-view” multivariate matrix models and their associated linear algebras; these are agnostic to the global data structure and can only describe local linear pairwise relationships between data entries. To address this issue, the authors first show that global fixed-income returns naturally reside on multi-modal lattice data structures, referred to as tensors. This serves as a basis to introduce a multilinear algebraic approach, inherent in tensors, to the modeling of the global term structure underlying multiple interest rate curves. Owing to the enhanced flexibility of multilinear algebra, statistical descriptors, such as correlations, exist between tensor columns and rows (fibers), as opposed to between individual entries in standard matrix analysis. This allows for the expression of the covariance of global returns as a joint multilinear decomposition of the maturity-domain and country-domain covariances. This not only drastically reduces the number of parameters required to fully capture the global return covariance structure, but also makes it possible to devise rigorous and tractable global portfolio management strategies; the authors tailor these specifically to each of the data domains and thereby fully exploit the lattice structure of global fixed-income returns. The ability of the proposed multilinear tensor approach to compactly describe the macroeconomic environment through economically meaningful factors is validated via empirical analysis that demonstrates the existence of maturity-domain and country-domain covariances underlying the interest rate curves of eight developed economies.
Calvi GG, Dees BS, Mandic DP, 2021, A lower bound on the tensor rank based on its maximally square matrix unfolding, SIGNAL PROCESSING, Vol: 180, ISSN: 0165-1684
Stankovic L, Mandic D, Dakovic M, 2021, Data Analytics on Graphs, Publisher: Now Publishers, ISBN: 9781680839821
Aimed at readers with a good grasp of the fundamentals of data analytics, this book sets out the fundamentals of graph theory and the emerging mathematical techniques for the analysis of a wide range of data acquired on graph environments.
Brajović M, Stanković I, Daković M, et al., 2021, On the Number of Channels in Multicomponent Nonstationary Noisy Signal Decomposition
If acquired using multiple sensors, non-stationary multicomponent signals can be decomposed into individual components by exploiting interdependences of signals from different channels. Earlier, we have proposed a decomposition approach being able to extract individual non-stationary signal components even in the challenging cases when their domains of support overlap in the time, frequency or joint time-frequency (TF) domains. The approach is based upon the eigenvalue analysis of the multichannel autocorrelation matrix and minimizations of concentration measures calculated using TF representations. In this paper, we investigate the influence of the number of sensors (channels) and external noise variance to the outcome of the decomposition process.
Cuevas A, Lopez S, Mandic D, et al., 2021, Bayesian autoregressive spectral estimation, IEEE Latin American Conference on Computational Intelligence (LA-CCI), Publisher: IEEE
Xu YL, Mandic DP, 2021, Recurrent Graph Tensor Networks: A Low-Complexity Framework for Modelling High-Dimensional Multi-Way Sequences, 29th European Signal Processing Conference (EUSIPCO), Publisher: EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP, Pages: 1795-1799, ISSN: 2076-1465
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- Citations: 1
Hammour GM, Mandic DP, 2021, Hearables: Making Sense from Motion Artefacts in Ear-EEG for Real-Life Human Activity Classification, 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), Pages: 6889-6893, ISSN: 1557-170X
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- Citations: 5
Xu YL, Calvi GG, Mandic DP, 2021, Tensor-Train Recurrent Neural Networks for Interpretable Multi-Way Financial Forecasting, International Joint Conference on Neural Networks (IJCNN), Publisher: IEEE, ISSN: 2161-4393
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- Citations: 1
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