Publications
736 results found
Davies HJ, Williams I, Peters NS, et al., 2023, In-Ear Blood Oxygen Saturation: A Tool for Wearable, Unobtrusive Monitoring of Core Blood Oxygen Saturation, Advances in Medical Imaging, Detection, and Diagnosis, Pages: 667-682, ISBN: 9789814877466
One of the major roles of blood is to supply oxygen to tissues throughout the body. This is achieved through the protein haemoglobin within red blood cells, which has a high affinity to oxygen. The term blood oxygen saturation specifically refers to the proportion of haemoglobin in the blood that is carrying oxygen and is given by Oxygen Saturation==HbO2HbO2+Hb, where Hb refers to haemoglobin not bound with oxygen and HbO2 refers to haemoglobin bound to oxygen. The blood oxygen estimation delay was calculated using the button release point as the marker for minimal blood oxygen, corresponding to the point at which the breath hold ends. The time between this point of minimal blood oxygen and the first trough of the SpO2 waveform for the ear and the finger was then used to calculate the SpO2 delay for the ear, the finger and then the relative delay between the ear and the finger.
Thornton M, Mandic D, Reichenbach T, 2023, Relating EEG Recordings to Speech Using Envelope Tracking and The Speech-FFR, ISSN: 1520-6149
During speech perception, a listener's electroencephalogram (EEG) reflects acoustic-level processing as well as higher-level cognitive factors such as speech comprehension and attention. However, decoding speech from EEG recordings is challenging due to the low signal-to-noise ratios of EEG signals. We report on an approach developed for the ICASSP 2023 'Auditory EEG Decoding' Signal Processing Grand Challenge. A simple ensembling method is shown to considerably improve upon the baseline decoder performance. Even higher classification rates are achieved by jointly decoding the speech-evoked frequency-following response and responses to the temporal envelope of speech, as well as by fine-tuning the decoders to individual subjects. Our results could have applications in the diagnosis of hearing disorders or in cognitively steered hearing aids.
Liu H, Chen Z, Yuan Y, et al., 2023, AudioLDM: Text-to-Audio Generation with Latent Diffusion Models, Pages: 22113-22136
Text-to-audio (TTA) systems have recently gained attention for their ability to synthesize general audio based on text descriptions. However, previous studies in TTA have limited generation quality with high computational costs. In this study, we propose AudioLDM, a TTA system that is built on a latent space to learn continuous audio representations from contrastive language-audio pretraining (CLAP) embeddings. The pretrained CLAP models enable us to train LDMs with audio embeddings while providing text embeddings as the condition during sampling. By learning the latent representations of audio signals without modelling the cross-modal relationship, AudioLDM improves both generation quality and computational efficiency. Trained on AudioCaps with a single GPU, AudioLDM achieves state-of-the-art TTA performance compared to other open-sourced systems, measured by both objective and subjective metrics. AudioLDM is also the first TTA system that enables various text-guided audio manipulations (e.g., style transfer) in a zero-shot fashion. Our implementation and demos are available at https://audioldm.github.io.
Mandic D, 2023, Welcome Message from the President of INNS, Proceedings of the International Joint Conference on Neural Networks, Vol: 2023-June
Lo Giudice M, Mammone N, Ieracitano C, et al., 2023, A Convolutional Neural Network Approach for the Classification of Subjects with Epileptic Seizures Versus Psychogenic Non-epileptic Seizures and Control, Based on Automatic Feature Extraction from Empirical Mode Decomposition of Interictal EEG Recordings, Smart Innovation, Systems and Technologies, Pages: 207-214
A reliable data-driven pipeline based on deep learning is introduced to differentiate between individuals with epileptic seizures (ES), psychogenic non-epileptic seizures (PNES), and control subjects (CS) using non-invasive, low-density interictal scalp EEG recordings. The study recruited 42 subjects with ES (new onset), 42 subjects with PNES diagnosed via video-EEG, and 19 CS with normal EEG. Subjects taking psychotropic drugs were excluded to avoid alterations in the EEG signal. The proposed methodology involves automatically extracting features from the 19-channel EEG channels using Empirical Mode Decomposition (EMD) and a customized Convolutional Neural Network (CNN) with a convolutional processing module, rectified linear units (ReLu), and pooling layer to extract and learn relevant features and perform the necessary classification. The CNN displayed excellent classification performance, achieving an accuracy of 85.7%, thereby fostering the use of deep processing systems to aid physicians in challenging clinical situations.
Konstantinidis T, Xu YL, Constantinides TG, et al., 2023, A comparative study on ML-based approaches for Main Entity Detection in Financial Reports
Modern AI technologies which exploit the classification and/or prediction capacities of Deep Neural Architectures demonstrate superior performance to traditional approaches in most cases. However, they come with the unavoidable shortcoming of lack of transparency in their outcomes. This attribute renders them unsuitable for big industrial sectors, such as finance, investment management, etc. Specifically, their "black-box"nature makes them unattractive in cases where human understanding in the decision making process is required and may be legally mandatory. In such cases, traditional (i.e., non-deep learning) ML approaches are still preferred, to minimize for example the presence of false positives. In this context, this paper introduces an unsupervised, trustful, bottom-up probabilistic approach for Named Entity Recognition (NER) in financial reports, while in parallel it provides a comparative study on well-known ML-approaches in terms of their performance. The proposed approach builds on the probability of appearance of representative tokens within the given reports and utilizes Kronecker's Delta and the Total Probability Theorem to construct a probabilistic model that estimates the overall classification probability of a document.
Konstantinidis T, Golan M, Xu YL, et al., 2023, Text Mining for Sentiment Analysis in Bond Portfolio Construction
The Efficient Market Hypothesis stipulates that financial instruments within a market perfectly reflect all public information. As the predominant method by which information is disseminated, it is considered that information contained within news influences the value of financial instruments. Thus, if the Efficient Market Hypothesis holds, then understanding the implications of such information is vital in informing portfolio construction. Sentiment analysis is the attempt of a machine to quantify the opinion of natural language. Therefore, this paper visits the Efficient Market Hypothesis by using a novel approach of analysing the relationship between financial news sentiment and corporate bonds in the time frame between 2015 and 2021. Several methods of sentiment analysis are employed to compare the efficacy of traditional lexicon-based knowledge extraction methods and the novel Transformer model approach. Furthermore, the short- and long-term impact of news is evaluated by comparing lagging and decaying signals. The outputs yielded from the sentiment analysis are used as a parameter in portfolio construction ant their returns are examined in light of Sharpe's Ratio to find success in demonstrating the continued viability of the Efficient Market Hypothesis, and the strong relationship between this theory and modern methods of financial sentiment analysis. Ultimately, this paper yields literature leading results with a Sharpe Ratio of 2.1.
Konstantinidis T, Farres AB, Xu YL, et al., 2023, Financial News Classification Model for NLP-based Bond Portfolio Construction
The objective of this paper is to build an accurate classification model for news articles, and to study the effect of different news categories in corporate bond returns. The first step is to show how to classify news articles for traded companies into different defined categories. A classification model using Bag of Words (BoW) and Term Frequency Inverse Document Frequency (TF-IDF) methodology is developed to classify the news into 10 defined categories, and gives an average accuracy of 71%. After web scrapping news articles from 3 different sources to build a dataset of over 550 thousand articles, these are classified into 10 different categories. Natural Language Processing is one of the main techniques used in systematic portfolio construction. Also in the paper, the constructed portfolio based on the sentiment of news articles, by buying the corporate bonds of companies that have positive sentiment in the news, and shorting the bonds of companies with negative sentiment in the news is presented. The effect of each news category in corporate bond returns is studied through these portfolios.
Xiao H, Li L, Mandic DP, 2023, ClassA Entropy for the Analysis of Structural Complexity of Physiological Signals, ISSN: 1520-6149
Despite the recent theoretical boom in Sample Entropy based algorithms for the analysis of physiological and pathological systems, the major issue which prevents their more widespread use remains that of large computational load, particularly in the studies of quantification of structural richness in data. This issue becomes even more prohibitive when it comes to large data sizes and real-time processing. To this end, a new Classification Angle (ClassA) Entropy is introduced for the quantification of structural complexity of real world signals, based on an improved Second Order Difference Plot and Shannon Entropy. In comparison with existing nonlinear techniques, including Real Sum Angle index, Phase Entropy, Gridded Distribution Entropy and Cosine Similarity Entropy, the proposed method offers the advantages of a minimal number of tunable parameters, lower requirement of data size, wider application field and large relaxation of computational load. Simulations on real world physiological data support the approach.
Rebello A, Konstantinidis K, Xu YL, et al., 2023, Tensor Completion for Efficient and Accurate Hyperparameter Optimisation in Large-Scale Statistical Learning, ISSN: 1520-6149
Hyperparameter optimisation is a prerequisite for state-of-the- art performance in machine learning, with current strategies including Bayesian optimisation, hyperband, and evolutionary methods. While such methods have been shown to improve performance, none of these is designed to explicitly take advantage of the underlying data structure. To this end, we introduce a completely different approach for hyperparameter optimisation, based on low-rank tensor completion. This is achieved by first forming a multi-dimensional tensor which comprises performance scores for different combinations of hyperparameters. Based on the realistic underlying assumption that the so-formed tensor has a low-rank structure, reliable estimates of the unobserved validation scores of combinations of hyper- parameters are next obtained through tensor completion, from only a fraction of the known elements in the tensor. Through extensive experimentation on various datasets and learning models, the proposed method is shown to exhibit competitive or superior performance to state-of-the-art hyperparameter optimisation strategies. Distinctive advantages of the proposed method include its ability to simultaneously handle any hyper- parameter type (kind of optimiser, number of neurons, number of layer, etc.), its relative simplicity compared to competing methods, as well as the ability to suggest multiple optimal combinations of hyperparameters.
Scalzo B, Stankovic L, Dakovic M, et al., 2023, A class of doubly stochastic shift operators for random graph signals and their boundedness, NEURAL NETWORKS, Vol: 158, Pages: 83-88, ISSN: 0893-6080
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- Citations: 1
Talebi SP, Godsill SJ, Mandic DP, 2023, Filtering Structures or α-Stable Systems, IEEE CONTROL SYSTEMS LETTERS, Vol: 7, Pages: 553-558, ISSN: 2475-1456
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- Citations: 6
Menguc EC, Acir N, Mandic DPP, 2023, A Class of Online Censoring Based Quaternion-Valued Least Mean Square Algorithms, IEEE SIGNAL PROCESSING LETTERS, Vol: 30, Pages: 244-248, ISSN: 1070-9908
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- Citations: 1
Ibunu S, Moore K, Took CC, et al., 2023, Weight sharing for single-channel LMS, 22nd IEEE Statistical Signal Processing Workshop (SSP), Publisher: IEEE, Pages: 185-189, ISSN: 2373-0803
Lo Giudice M, Ferlazzo E, Mammone N, et al., 2022, Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects, INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, Vol: 19
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- Citations: 1
Roa-Vicens J, Xu YL, Silva R, et al., 2022, Graph and tensor-train recurrent neural networks for high-dimensional models of limit order books, Pages: 207-213
Recurrent neural networks (RNNs) have proven to be particularly effective for the paradigms of learning and modelling time series. However, sequential data of high dimensions are considerably more difficult and computationally expensive to model, as the number of parameters required to train the RNN grows exponentially with data dimensionality. This is also the case with time series from limit order books, the electronic registries where prices of securities are formed in public markets. To this end, tensorization of neural networks provides an efficient method to reduce the number of model parameters, and has been applied successfully to high-dimensional series such as video sequences and financial time series, for example, using tensor-train RNNs (TTRNNs). However, such TTRNNs suffer from a number of shortcomings, including: (i) model sensitivity to the ordering of core tensor contractions; (ii) training sensitivity to weight initialization; and (iii) exploding or vanishing gradient problems due to the recurrent propagation through the tensor-train topology. Recent studies showed that embedding a multi-linear graph filter to model RNN states (Recurrent Graph Tensor Network, RGTN) provides enhanced flexibility and expressive power to tensor networks, while mitigating the shortcomings of TTRNNs. In this paper, we demonstrate the advantages arising from the use of graph filters to model limit order book sequences of high dimension as compared with the state-of-the-art benchmarks. It is shown that the combination of the graph module (to mitigate problematic gradients) with the radial structure (to make the tensor network architecture flexible) results in substantial improvements in output variance, training time and number of parameters required, without any sacrifice in accuracy.
Powezka K, Pettipher A, Hemakom A, et al., 2022, A Pilot Study of Heart Rate Variability Synchrony as a Marker of Intraoperative Surgical Teamwork and Its Correlation to the Length of Procedure, SENSORS, Vol: 22
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- Citations: 1
Menguc EC, Xiang M, Mandic DP, 2022, Online censoring based complex-valued adaptive filters, SIGNAL PROCESSING, Vol: 200, ISSN: 0165-1684
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- Citations: 4
Haliassos A, Konstantinidis K, Mandic DP, 2022, Supervised Learning for Nonsequential Data: A Canonical Polyadic Decomposition Approach, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol: 33, Pages: 5162-5176, ISSN: 2162-237X
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- Citations: 2
Normahani P, Sounderajah V, Mandic D, et al., 2022, Machine learning-based classification of arterial spectral waveforms for the diagnosis of peripheral artery disease in the context of diabetes: A proof-of-concept study, VASCULAR MEDICINE, Vol: 27, Pages: 450-456, ISSN: 1358-863X
Sau A, Ibrahim S, Ahmed A, et al., 2022, Artificial intelligence-enabled electrocardiogram to distinguish cavotricuspid isthmus dependence from other atrial tachycardia mechanisms, European Heart Journal – Digital Health, Vol: 3, Pages: 405-414, ISSN: 2634-3916
Aims:Accurately determining atrial arrhythmia mechanisms from a 12-lead electrocardiogram (ECG) can be challenging. Given the high success rate of cavotricuspid isthmus (CTI) ablation, identification of CTI-dependent typical atrial flutter (AFL) is important for treatment decisions and procedure planning. We sought to train a convolutional neural network (CNN) to classify CTI-dependent AFL vs. non-CTI dependent atrial tachycardia (AT), using data from the invasive electrophysiology (EP) study as the gold standard.Methods and results:We trained a CNN on data from 231 patients undergoing EP studies for atrial tachyarrhythmia. A total of 13 500 five-second 12-lead ECG segments were used for training. Each case was labelled CTI-dependent AFL or non-CTI-dependent AT based on the findings of the EP study. The model performance was evaluated against a test set of 57 patients. A survey of electrophysiologists in Europe was undertaken on the same 57 ECGs. The model had an accuracy of 86% (95% CI 0.77–0.95) compared to median expert electrophysiologist accuracy of 79% (range 70–84%). In the two thirds of test set cases (38/57) where both the model and electrophysiologist consensus were in agreement, the prediction accuracy was 100%. Saliency mapping demonstrated atrial activation was the most important segment of the ECG for determining model output.Conclusion:We describe the first CNN trained to differentiate CTI-dependent AFL from other AT using the ECG. Our model matched and complemented expert electrophysiologist performance. Automated artificial intelligence-enhanced ECG analysis could help guide treatment decisions and plan ablation procedures for patients with organized atrial arrhythmias.
Kisil I, Calvi GG, Konstantinidis K, et al., 2022, Accelerating Tensor Contraction Products via Tensor-Train Decomposition [Tips & Tricks], IEEE SIGNAL PROCESSING MAGAZINE, Vol: 39, Pages: 63-70, ISSN: 1053-5888
Xiao H, Chanwimalueang T, Mandic DP, 2022, Multivariate Multiscale Cosine Similarity Entropy and Its Application to Examine Circularity Properties in Division Algebras, ENTROPY, Vol: 24
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- Citations: 1
Thornton M, Mandic D, Reichenbach T, 2022, Robust decoding of the speech envelope from EEG recordings through deep neural networks, JOURNAL OF NEURAL ENGINEERING, Vol: 19, ISSN: 1741-2560
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- Citations: 3
Liu J, Xu D, Zhang H, et al., 2022, On hyper-parameter selection for guaranteed convergence of RMSProp, COGNITIVE NEURODYNAMICS, ISSN: 1871-4080
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- Citations: 1
Took CC, Mandic D, 2022, Weight sharing for LMS algorithms: Convolutional neural networks inspired multichannel adaptive filtering, DIGITAL SIGNAL PROCESSING, Vol: 127, ISSN: 1051-2004
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- Citations: 4
Davies HJ, Williams I, Mandic DP, 2022, Tracking Cognitive Workload in Gaming with In-Ear [Formula: see text]., Annu Int Conf IEEE Eng Med Biol Soc, Vol: 2022, Pages: 4913-4916
The feasibility of using in-ear [Formula: see text] to track cognitive workload induced by gaming is investigated. This is achieved by examining temporal variations in cognitive workload through the game Geometry Dash, with 250 trials across 7 subjects. The relationship between performance and cognitive load in Dark Souls III boss fights is also investigated followed by a comparison of the cognitive workload responses across three different genres of game. A robust decrease in in-ear [Formula: see text] is observed in response to cognitive workload induced by gaming, which is consistent with existing results from memory tasks. The results tentatively suggest that in-ear [Formula: see text] may be able to distinguish cognitive workload alone, whereas heart rate and breathing rate respond similarly to both cognitive workload and stress. This study demonstrates the feasibility of low cost wearable cognitive workload tracking in gaming with in-ear [Formula: see text], with applications to the play testing of games and biofeedback in games of the future.
Prochazka A, Charvat J, Vysata O, et al., 2022, Incremental deep learning for reflectivity data recognition in stomatology, NEURAL COMPUTING & APPLICATIONS, Vol: 34, Pages: 7081-7089, ISSN: 0941-0643
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- Citations: 3
Li S, Mandic D, 2022, Von Mises-Fisher Elliptical Distribution, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, ISSN: 2162-237X
Zhang X, Xia Y, Li C, et al., 2022, A full second-order statistical analysis of strictly linear and widely linear estimators with MSE and Gaussian entropy criteria, SIGNAL PROCESSING, Vol: 192, ISSN: 0165-1684
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- Citations: 1
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