459 results found
Adjei T, von Rosenberg W, Goverdovsky V, et al., Pain prediction from ECG in vascular surgery, IEEE Journal of Translational Engineering in Health and Medicine, ISSN: 2168-2372
Varicose vein surgeries are routine outpatient procedures,which areoften performed under local anaesthesia. The use of local anaesthesiaboth minimises the risk to patientsand is cost effective, however, a number of patientsstill experience pain during surgery. Surgical teams must therefore decide to administer eithera general or local anaestheticbasedon theirsubjective qualitative assessment of patient anxietyand sensitivity to pain, without any means to objectively validate their decision. To this end, we develop a 3-dimensionalpolynomial surface fit,of physiological metrics and numerical pain ratings from patients, in order tomodel thelink between the modulation ofcardiovascular responses and painin varicose vein surgeries. Spectral and structural complexity features found inheart rate variabilitysignals,recorded immediately prior to 17 varicose vein surgeries,areused as pain metrics. The so obtained pain prediction model isvalidated through aleave-one-out validation, and achievedaKappa coefficient of 0.72 (substantialagreement) and an area below a receiver operating curve of 0.97(almost perfect accuracy). This proof-of-concept study conclusively demonstrates the feasibility ofthe accurate classification of pain sensitivity, andintroduces a mathematicalmodel to aidcliniciansin the objective administration ofthe safest and most cost-effective anaesthetic toindividual patients.
Nakamura T, Goverdovsky V, mandic D, In-ear EEG biometrics for feasible and readily collectable real-world person authentication, IEEE Transactions on Information Forensics and Security, ISSN: 1556-6013
Nakamura T, adjei T, alqurashi Y, et al., Complexity science for sleep stage classification from EEG, IEEE International Joint Conference on Neural Networks (IJCNN) 2017, Publisher: IEEE
Automatic sleep stage classification is an importantparadigm in computational intelligence and promises consider-able advantages to the health care. Most current automatedmethods require the multiple electroencephalogram (EEG) chan-nels and typically cannot distinguish the S1 sleep stage fromEEG. The aim of this study is to revisit automatic sleep stageclassification from EEGs using complexity science methods. Theproposed method applies fuzzy entropy and permutation entropyas kernels of multi-scale entropy analysis. To account for sleeptransition, the preceding and following 30 seconds of epoch datawere used for analysis as well as the current epoch. Combiningthe entropy and spectral edge frequency features extracted fromone EEG channel, a multi-class support vector machine (SVM)was able to classify 93.8% of 5 sleep stages for the SleepEDFdatabase [expanded], with the sensitivity of S1 stage was 49.1%.Also, the Kappa’s coefficient yielded 0.90, which indicates almostperfect agreement.
Ahmed MU, Chanwimalueang T, Thayyil S, et al., 2017, A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis, ENTROPY, Vol: 19, ISSN: 1099-4300
Alqurashi Y, Moss J, Nakamura T, et al., 2017, The Efficacy Of In-Ear Electroencephalography (eeg) To Monitor Sleep Latency And The Impact Of Sleep Deprivation, International Conference of the American-Thoracic-Society (ATS), Publisher: AMER THORACIC SOC, ISSN: 1073-449X
Chanwimalueang T, Aufegger L, Adjei T, et al., 2017, Stage call: Cardiovascular reactivity to audition stress in musicians, PLOS ONE, Vol: 12, ISSN: 1932-6203
Goverdovsky V, von Rosenberg W, Nakamura T, et al., 2017, Hearables: Multimodal physiological in-ear sensing., Sci Rep, Vol: 7
Future health systems require the means to assess and track the neural and physiological function of a user over long periods of time, and in the community. Human body responses are manifested through multiple, interacting modalities - the mechanical, electrical and chemical; yet, current physiological monitors (e.g. actigraphy, heart rate) largely lack in cross-modal ability, are inconvenient and/or stigmatizing. We address these challenges through an inconspicuous earpiece, which benefits from the relatively stable position of the ear canal with respect to vital organs. Equipped with miniature multimodal sensors, it robustly measures the brain, cardiac and respiratory functions. Comprehensive experiments validate each modality within the proposed earpiece, while its potential in wearable health monitoring is illustrated through case studies spanning these three functions. We further demonstrate how combining data from multiple sensors within such an integrated wearable device improves both the accuracy of measurements and the ability to deal with artifacts in real-world scenarios.
Kanna S, Mandic DP, 2017, Self-stabilising adaptive three-phase transforms via widely linear modelling, ELECTRONICS LETTERS, Vol: 53, Pages: 875-876, ISSN: 0013-5194
Li Z, Xia Y, Pei W, et al., 2017, Noncircular Measurement and Mitigation of I/Q Imbalance for OFDM-Based WLAN Transmitters, IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, Vol: 66, Pages: 383-393, ISSN: 0018-9456
Rehman NU, Abbas SZ, Asif A, et al., 2017, Translation invariant multi-scale signal denoising based on goodness-of-fit tests, SIGNAL PROCESSING, Vol: 131, Pages: 220-234, ISSN: 0165-1684
Xia Y, He Y, Wang K, et al., 2017, A Complex Least Squares Enhanced Smart DFT Technique for Power System Frequency Estimation, IEEE TRANSACTIONS ON POWER DELIVERY, Vol: 32, Pages: 1270-1278, ISSN: 0885-8977
Xia Y, Mandic DP, 2017, Complementary Mean Square Analysis of Augmented CLMS for Second-Order Noncircular Gaussian Signals, IEEE SIGNAL PROCESSING LETTERS, Vol: 24, Pages: 1413-1417, ISSN: 1070-9908
Xia Y, Mandic DP, 2017, A Full Mean Square Analysis of CLMS for Second-Order Noncircular Inputs, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 65, Pages: 5578-5590, ISSN: 1053-587X
Xiang M, Took CC, Mandic DP, 2017, Cost-effective quaternion minimum mean square error estimation: From widely linear to four-channel processing, SIGNAL PROCESSING, Vol: 136, Pages: 81-91, ISSN: 0165-1684
von Rosenberg W, Chanwimalueang T, Adjei T, et al., 2017, Resolving Ambiguities in the LF/HF Ratio: LF-HF Scatter Plots for the Categorization of Mental and Physical Stress from HRV, FRONTIERS IN PHYSIOLOGY, Vol: 8, ISSN: 1664-042X
Akansu AN, Malioutov D, Palomar DP, et al., 2016, Introduction to the Issue on Financial Signal Processing and Machin Learning for Electronic Trading, IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, Vol: 10, Pages: 979-981, ISSN: 1932-4553
Chanwimalueang T, Aufegger L, von Rosenberg W, et al., 2016, MODELLING STRESS IN PUBLIC SPEAKING: EVOLUTION OF STRESS LEVELS DURING CONFERENCE PRESENTATIONS, IEEE International Conference on Acoustics, Speech, and Signal Processing, Publisher: IEEE, Pages: 814-818, ISSN: 1520-6149
Cichocki A, Lee N, Oseledets I, et al., 2016, Tensor Networks for Dimensionality Reduction and Large-Scale Optimization Part 1 Low-Rank Tensor Decompositions, FOUNDATIONS AND TRENDS IN MACHINE LEARNING, Vol: 9, Pages: I-+, ISSN: 1935-8237
Douglas SC, Mandic DP, 2016, STABILITY ANALYSIS OF THE LEAST-MEAN-MAGNITUDE-PHASE ALGORITHM, IEEE International Conference on Acoustics, Speech, and Signal Processing, Publisher: IEEE, Pages: 4935-4939, ISSN: 1520-6149
Enshaeifar S, Took CC, Sanei S, et al., 2016, NOVEL QUATERNION MATRIX FACTORISATIONS, IEEE International Conference on Acoustics, Speech, and Signal Processing, Publisher: IEEE, Pages: 3946-3950, ISSN: 1520-6149
Goverdovsky V, Looney D, Kidmose P, et al., 2016, In-Ear EEG From Viscoelastic Generic Earpieces: Robust and Unobtrusive 24/7 Monitoring, IEEE SENSORS JOURNAL, Vol: 16, Pages: 271-277, ISSN: 1530-437X
Hemakom A, Goverdovsky V, Aufegger L, et al., 2016, QUANTIFYING COOPERATION IN CHOIR SINGING: RESPIRATORY AND CARDIAC SYNCHRONISATION, IEEE International Conference on Acoustics, Speech, and Signal Processing, Publisher: IEEE, Pages: 719-723, ISSN: 1520-6149
Hemakom A, Goverdovsky V, Looney D, et al., 2016, Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain-computer interface applications, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, Vol: 374, ISSN: 1364-503X
Jaksic V, Mandic DP, Karoumi R, et al., 2016, Estimation of nonlinearities from pseudodynamic and dynamic responses of bridge structures using the Delay Vector Variance method, PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, Vol: 441, Pages: 100-120, ISSN: 0378-4371
Jaksic V, Mandic DP, Ryan K, et al., 2016, A comprehensive study of the delay vector variance method for quantification of nonlinearity in dynamical systems, ROYAL SOCIETY OPEN SCIENCE, Vol: 3, ISSN: 2054-5703
Kanna S, Mandic DP, 2016, Steady-State Behavior of General Complex-Valued Diffusion LMS Strategies, IEEE SIGNAL PROCESSING LETTERS, Vol: 23, ISSN: 1070-9908
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