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Recognition of patient groups with sleep related disorders using bio-signal processing and deep learning

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arXiv 2021年

作者： Jarchi, Delaram Andreu-Perez, Javier Kiani, Mehrin Vysata, Oldrich Kuchynka, Jiri Prochazka, Ales Sanei, Saeid Smart Health Technologies Group School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Embedded and Intelligent Systems Laboratory School of Computer Science and Electronics University of Essex ColchesterCO4 3SQ United Kingdom Department of Computing and Control Engineering University of Chemistry and Technology in Prague Prague 6166 28 Czech Republic Department of Neurology Faculty of Medicine in Hradec Králové Charles University Hradec Králové500 05 Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6160 00 Czech Republic School of Science and Technology Nottingham Trent University NottinghamNG11 8NS United Kingdom

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem. © 2021, CC BY-NC-ND.

关键词： Wavelet transforms

Synchronization of quadratic integrate-and-fire spiking neurons: Constant versus voltage-dependent couplings

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Synchronization of quadratic integrate-and-fire spiking neur...

IEEE Annual Conference on Decision and control

作者： Matin Jafarian Karl H. Johansson Division of Decision and Control School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

This paper studies synchronization of a network of hybrid quadratic integrate-and-fire spiking neurons communicating over a complete graph and interconnected by means of bidirectional electrical couplings. Synchronization of the network of identical neurons with a common and constant coupling strength is studied using a Lyapunov-based argument for sufficiently large coupling strength. In addition, a voltage-dependent coupling law is proposed. It is assumed that each neuron is coupled to each of its neighbors by a coupling law which depends on the voltage of its neighboring neuron. For the voltage-dependent case, a sufficient condition for synchronization of two interconnected neurons is presented. Moreover, a comparison between the two mechanisms is given. Simulation results are provided to verify the theoretical analysis.

关键词： Neurons Synchronization Couplings Trajectory Integrated circuit modeling Synapses Lyapunov methods

AdaBoosting Neural Network for Short-Term Wind Speed Forecasting Based on Seasonal Characteristics Analysis and Lag Space Estimation

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computer Modeling in engineering & sciences 2018年第3期114卷 277-293页

作者： Haijian Shao Xing Deng School of Computer Science and Engineering Jiangsu University of Science and TechnologyJiangsuZhenjiang212003China School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityJiangsu210096NanjingChina

High accurary in wind speed forcasting remains hard to achieve due to wind’s random distribution nature and its seasonal ***,intermittent and nonstationary usually cause the portion problem of the wind speed *** characteristics of wind speed means that its feature distribution is *** typically results that the persistence of excitation for modeling can not be guaranteed,and may severely reduce the possibilities of high precise forecasting *** this paper,we proposed two effective solutions to solve the problems caused by the randomness and seasonal characteristics of the wind speed.(1)Wavelet analysis is used to extract the robust components of time series and reduce the influence of randomness.(2)Based on the energy distribution about the extracted amplitude and associated frequency,seasonal characteristics of wind speed are analyzed based on self-similarity in periodogram under scales range generated by wavelet ***,the original dataset is reasonably divided into subsest which can effectively reflect the seasonal distribution characteristics of wind *** addition,two strategies are given to optimal model structure and improve the forecasting accuracy:(1)The forecasting model’s lag space is approximately estimated by the Lipschitz quotient to improve the generality ability of the feedforward neural network.(2)The forecasting accuracy and model robustness are further improved by the wavelet decomposition combined with AdaBoosting neural ***,experimental evaluation based on the dataset from National Renewable Energy Laboratory(NREL)is given to demonstrate the performance of the proposed approach.

关键词： Wind speed forecasting seasonal characteristics analysis wavelet analysis lipschitz quotient

Secure distributed filtering for unstable dynamics under compromised observations

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arXiv 2019年

作者： He, Xingkang Ren, Xiaoqiang Sandberg, Henrik Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

In this paper, we consider a secure distributed filtering problem for linear time-invariant systems with bounded noises and unstable dynamics under compromised observations. A malicious attacker is able to compromise a subset of the agents and manipulate the observations arbitrarily. We first propose a recursive distributed filter consisting of two parts at each time. The first part employs a saturation-like scheme, which gives a small gain if the innovation is too large. The second part is a consensus operation of state estimates among neighboring agents. A sufficient condition is then established for the boundedness of estimation error, which is with respect to network topology, system structure, and the maximal compromised agent subset. We further provide an equivalent statement, which connects to 2s-sparse observability in the centralized framework in certain scenarios, such that the sufficient condition is feasible. Numerical simulations are finally provided to illustrate the developed results. Copyright © 2019, The Authors. All rights reserved.

关键词： Invariance

Occupancy grid for static environment perception in series automotive applications

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IFAC-PapersOnLine 2019年第8期52卷 148-153页

作者： Jakub Porebski Krzysztof Kogut Paweł Markiewicz Paweł Skruch AGH University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Cracow Poland Aptiv Services Poland S.A. Cracow Poland Aptiv Services Poland S.A. Cracow Poland Silesian University of Technology Faculty of Automatic Control and Computer Science Gliwice Poland

This paper presents a comprehensive review of state-of-the-art occupancy grid mapping approaches for highway and urban automotive applications. The primary objective is to identify most suitable algorithm candidates for series applications. The occupancy grid functional performance requirements for automotive are given. A new high level grid fusion architecture is introduced. The innovative element is a second stage filtering step allowing better performance in case of highly uncertain inputs. An application of the architectures to an example sensor suite is proposed.

关键词： Autonomous Vehicles Map building Sensor integration perception control architectures in automotive control General automobile road-environment strategies Information sensor fusion Bayesian methods Estimation filtering

Stability analysis of networked control systems with multi-parameter constraints

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Stability analysis of networked control systems with multi-p...

第三十八届中国控制会议

作者： Si-Wei Qiao Xiao-Lin Xu Jie Wu Ling-Yan Wang Xi-Sheng Zhan College of Mechatronics and Control Engineering Hubei Normal University College of Computer Science and Technology Hubei Normal University

In this paper, the stability of networked control systems with channel noise, bandwidth, packet dropouts and delay constraints are studied. The stability of networked control system is analyzed by frequency domain method. The performance of a communication network is reflected by time delay, noise, packet dropouts and network bandwidth. By frequency domain analysis and spectrum decomposition technique, the minimum SNR expression of stable networked control systems is *** addition, the minimum value of system stability is determined by the time delay, packet dropouts, bandwidth of the communication network, and the locations of unstable poles and non-minimum phase zeros of the given model. The final results show the relationship between the stability of the networked control systems, the structural characteristics of the given model(such as the position of non-minimum phase zero and unstable poles) and the parameters of the communication network(delay, packet dropouts and bandwidth). Finally, the effectiveness of the proposed method is illustrated by a numerical example.

关键词： networked control systems stability analysis signal to noise ratio time-delay channel noise

A sliding mode controller design for thermal comfort in buildings

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engineering Transactions 2019年第4期67卷 475-489页

作者： Skruch, Pawel Dlugosz, Marek AGH University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Automatic Control and Robotics Al. Mickiewicza 30 Kraków30-059 Poland

One of the factors determining comfort in buildings is the indoor air temperature of the rooms. A control system, part of the home automation system, should stabilise air temperature to the desired level, despite various disturbances such as the presence of random or occasional sources of heat. Inaccurate models of the dynamics of air temperature changes in buildings prescribe the use of robust control methods, a type of which is the sliding mode controller. This article presents the application of a sliding mode controller (SMC) to home automation systems, designed to control air temperature inside a building. The sliding-mode controller makes use of sliding surfaces, which are defined by the assumed trajectory and the system output. The control law is designed in such a way that the trajectory of the system tends to the sliding surface from any initial point and remains on it after reaching the sliding surface. In this article, a model at air temperature change dynamics inside a building is presented. The modelling approach relies on the lumped-parameter methodology, in which distributed physical properties are represented by lumped parameters (such as thermal capacity or resistance). The model takes into account the loss of heat through conduction and ventilation, as well as internal heat gain. The parameters of the model can be obtained easily from the thermal properties of the construction materials. Theoretical considerations were applied in simulation experiments and the results of these experiments confirm the performance improvement achieved by the proposed solutions. © 2019, Polish Academy of sciences. All rights reserved.

关键词： Atmospheric temperature

Ternary Compression for Communication-Efficient Federated Learning

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arXiv 2020年

作者： Xu, Jinjin Du, Wenli Jin, Yaochu He, Wangli Cheng, Ran The Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China The Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Learning over massive data stored in different locations is essential in many real-world applications. However, sharing data is full of challenges due to the increasing demands of privacy and security with the growing use of smart mobile devices and IoT devices. Federated learning provides a potential solution to privacy-preserving and secure machine learning, by means of jointly training a global model without uploading data distributed on multiple devices to a central server. However, most existing work on federated learning adopts machine learning models with full-precision weights, and almost all these models contain a large number of redundant parameters that do not need to be transmitted to the server, consuming an excessive amount of communication costs. To address this issue, we propose a federated trained ternary quantization (FTTQ) algorithm, which optimizes the quantized networks on the clients through a self-learning quantization factor. Theoretical proofs of the convergence of quantization factors, unbiasedness of FTTQ, as well as a reduced weight divergence are given. On the basis of FTTQ, we propose a ternary federated averaging protocol (T-FedAvg) to reduce the upstream and downstream communication of federated learning systems. Empirical experiments are conducted to train widely used deep learning models on publicly available datasets, and our results demonstrate that the proposed T-FedAvg is effective in reducing communication costs and can even achieve slightly better performance on non-IID data in contrast to the canonical federated learning algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Learning algorithms

Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction

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arXiv 2019年

作者： Amian, Mehdi Soltaninejad, Mohammadreza Control and Intelligent Center of Excellence School of Electrical and Computer Engineering University of Tehran Iran School of Computer Science University of Nottingham United Kingdom

In this study, an automated three dimensional (3D) deep segmentation approach for detecting gliomas in 3D pre-operative MRI scans is proposed. Then, a classification algorithm based on random forests, for survival prediction is presented. The objective is to segment the glioma area and produce segmentation labels for its different sub-regions, i.e. necrotic and the non-enhancing tumor core, the peritumoral edema, and enhancing tumor. The proposed deep architecture for the segmentation task encompasses two parallel streamlines with two different resolutions. One deep convolutional neural network is to learn local features of the input data while the other one is set to have a global observation on whole image. Deemed to be complementary, the outputs of each stream are then merged to provide an ensemble complete learning of the input image. The proposed network takes the whole image as input instead of patch-based approaches in order to consider the semantic features throughout the whole volume. The algorithm is trained on BraTS 2019 which included 335 training cases, and validated on 127 unseen cases from the validation dataset using a blind testing approach. The proposed method was also evaluated on the BraTS 2019 challenge test dataset of 166 cases. The results show that the proposed methods provide promising segmentations as well as survival prediction. The mean Dice overlap measures of automatic brain tumor segmentation for validation set were 0.84, 0.74 and 0.71 for the whole tumor, core and enhancing tumor, respectively. The corresponding results for the challenge test dataset were 0.82, 0.72, and 0.70, respectively. The overall accuracy of the proposed model for the survival prediction task is %52 for the validation and %49 for the test dataset. Copyright © 2019, The Authors. All rights reserved.

关键词： Tumors