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Analysis of autonomic nervous pattern in hypertension based on short-term heart rate variability

BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2021年第1期66卷 71-79页

作者： Zhang, Ruiqi Hua, Zhengchun Chen, Chen Liu, Guangyuan Wen, Wanhui Southwest Univ Sch Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

Physiological studies have found that the autonomic nervous system plays an important role in controlling blood pressure values. This paper, based on machine learning approaches, analysed short-term heart rate variability to determine differences in autonomic nervous function between hypertensive patients and normal population. The electrocardiogram (ECG) of hypertensive patients are 137 ECG recordings provided by Smart Health for Assessing the Risk of Events via ECG (SHAREE database). The RR intervals of healthy subjects include the data of 18 subjects from the MIT-BIH Normal Sinus Rhythm Database (nsrdb) and 54 subjects from the Normal Sinus Rhythm RR Interval Database (nsr2db). In this paper, each RR segment includes continuous 500 beats. Seventeen features were extracted to distinguish the hypertensive heart beat rhythms from the normal ones, and Kolmogorov-Smirnov test and sequential backward selection (SBS) were applied to get the best feature combinations. In addition, support vector machine (SVM), k-nearest neighbor (KNN) and random forest (RF) were applied as classifiers in the study. The performance of each classifier was evaluated independently using the leave-one-subject-out validation method. The best predictive model was based on RF and enabled to identify hypertensive patients by five features with an accuracy of 86.44%. The best five HRV features are sample entropy (SampEn), very low frequency spectral powers (VLF), root mean square of successful differences (RMSSD), ratio of low frequency spectral powers and high frequency spectral powers (LF/HF) and vector angle index (VAI). The results of the study show sympathetic overactivity and decreased parasympathetic tone in hypertensive patients.

关键词： autonomic nervous system hypertension machine learning short-term HRV analysis

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Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition

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IEEE TRANSACTIONS ON SIGNAL AND informatION PROCESSING OVER NETWORKS 2024年 10卷 513-526页

作者： Cheng, Huqiang Liao, Xiaofeng Li, Huaqing Lu, Qingguo Zhao, You Chongqing Univ Coll Comp Sci Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

Distributed optimization is manifesting great potential in multiple fields, e.g., machine learning, control, resource allocation, etc. Existing decentralized optimization algorithms require sharing explicit state information among the agents, which raises the risk of private information leakage. To ensure privacy security, combining information security mechanisms, such as differential privacy and homomorphic encryption, with traditional decentralized optimization algorithms is a commonly used means. However, this may either sacrifice optimization accuracy or incur a heavy computational burden. To overcome these shortcomings, we develop a novel privacy-preserving decentralized optimization algorithm, named PPSD, that combines gradient tracking with a state decomposition mechanism. Specifically, each agent decomposes its state associated with the gradient into two substates. One substate is used for interaction with neighboring agents, and the other substate containing private information acts only on the first substate and thus is entirely agnostic to other agents. When the objective function is smooth and satisfies the Polyak-& Lstrok;ojasiewicz (PL) condition, PPSD attains an R-linear convergence rate. Moreover, the algorithm can preserve the normal agents' private information from being leaked to honest-but-curious attackers. Simulations further confirm the results.

关键词： Optimization Privacy Heuristic algorithms Linear programming Convergence Machine learning algorithms information processing Privacy protection decentralized optimization push-pull state decomposition

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Mixed- Degree Spherical Simplex-Radial Cubature Kalman Filter

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MATHEMATICAL PROBLEMS IN ENGINEERING 2017年第1期2017卷 1-9页

作者： Wang, Shiyuan Feng, Yali Duan, Shukai Wang, Lidan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

Conventional low degree spherical simplex-radial cubature Kalman filters often generate low filtering accuracy or even diverge for handling highly nonlinear systems. The high-degree Kalman filters can improve filtering accuracy at the cost of increasing computational complexity;nevertheless their stability will be influenced by the negative weights existing in the high-dimensional systems. To efficiently improve filtering accuracy and stability, a novel mixed-degree spherical simplex-radial cubature Kalman filter (MSSRCKF) is proposed in this paper. The accuracy analysis shows that the true posterior mean and covariance calculated by the proposed MSSRCKF can agree accurately with the third-order moment and the second-order moment, respectively. Simulation results show that, in comparison with the conventional spherical simplex-radial cubature Kalman filters that are based on the same degrees, the proposedMSSRCKF can performsuperior results fromthe aspects of filtering accuracy and computational complexity.

关键词： CUBATURE formulas KALMAN filtering nonlinear systems COMPUTATIONAL complexity ANALYSIS of covariance

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Memristive pulse coupled neural network with applications in medical image processing

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NEUROCOMPUTING 2017年 227卷 149-157页

作者： Zhu, Song Wang, Lidan Duan, Shukai Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

Medical imaging has become an integral part of modern medical technology increasingly. The image information provided by multimode imaging technology can complement each other. Fusing computed tomography (CT) and magnetic resonance imaging (MRI) images can combine their unique information to diagnose brain diseases by radiation therapy. And medical imaging process is easy to produce noise, which will impact doctors on diagnosis. So the medical image de-noising has important significance. In addition, the image edge extraction is helpful to the clinical diagnosis. So this paper constructs a tnemristive pulse coupled neural network (M-PCNN) for medical image processing. The memristance of Gale memristor decays exponentially with time, which can be used to adjust the threshold of pulse coupled neural network (PCNN) online. Integrating the memorability of memristor into PCNN makes the network have biological function. And the introduction of nanoscale memristor can also significantly reduce the scale of PCNN, which may promote the development of neural network hardware implementation. Numerical simulation verifies that the superiority of using this network in medical image fusion, image de-noising and image edge extraction.

关键词： Medical image fusion Memristor Pulse coupled neural network Image de-noising Image edge extraction

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The Nystrom minimum kernel risk-sensitive loss algorithm with k-means sampling

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2020年第14期357卷 10082-10099页

作者： Zhang, Tao Wang, Shiyuan Huang, Xuewei Wang, Lin Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

The minimum kernel risk-sensitive loss (MKRSL) algorithm has been developed to improve the filtering accuracy and robustness of kernel least mean square (KLMS) in non-Gaussian noises. However, the linear growth of network size in MKRSL leads to a huge burden on time consumption and memory requirement. To curb this growth issue, a novel Nystrom minimum kernel risk-sensitive loss with k-means sampling (NysMKRSL-KM) algorithm is proposed by using the Nystrom method combined with k-means sampling to approximate the kernel matrix of MKRSL in this paper. The proposed NysMKRSL-KM algorithm with low time and storage complexity achieves a comparable performance to kernel adaptive filters (KAFs). In addition, the energy conservation relation and the sufficient condition of NysMKRSL-KM are obtained for performing theoretical analysis and guaranteeing the mean square convergence, respectively. The steady-state excess mean square errors (EMSEs) of NysMKRSL-KM for different noises are therefore derived for evaluating accuracy theoretically. Monte Carlo simulations are conducted to validate the theoretical analysis results and superiorities of the proposed NysMKRSL-KM algorithm. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： Monte Carlo methods

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Distributed Online Private Learning of Convex Nondecomposable Objectives

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第2期11卷 1716-1728页

作者： Cheng, Huqiang Liao, Xiaofeng Li, Huaqing Chongqing Univ Coll Comp Sci Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

In the machine learning domain, datasets share several new features, including distributed storage, high velocity, and privacy concerns, which naturally requires the development of distributed privacy-preserving algorithms. Moreover, nodes (e.g., learners, sensors, GPUs, mobiles, etc.) in the real networks usually process tasks in real time, which inevitably requires nodes to have online learning capabilities. Therefore, we deal with a general distributed constrained online learning problem with privacy over time-varying networks, where a class of nondecomposable objectives are considered. Under this setting, each node only controls a part of the global decision, and the goal of all nodes is to collaboratively minimize the global cost over a time horizon $T$ while guarantees the security of the transmitted information. For such problems, we first design a novel generic algorithm framework, named as DPSDA, of differentially private distributed online learning using the Laplace mechanism and the stochastic variants of dual averaging method. Note that in the dual updates, all nodes of DPSDA employ the noise-corrupted gradients for more generality. Then, we propose two algorithms, named as DPSDA-C and DPSDA-PS, under this framework. In DPSDA-C, the nodes implement a circulation-based communication in the primal updates so as to alleviate the disagreements over time-varying undirected networks. In addition, for the extension to time-varying directed ones, the nodes implement the broadcast-based push-sum dynamics in DPSDA-PS, which can achieve average consensus over arbitrary directed networks. Theoretical results show that both algorithms attain an expected regret upper bound in O(root T) when the objective function is convex, which matches the best utility achievable by cutting-edge algorithms. Finally, numerous numerical experiments on both synthetic and real-world datasets verify the effectiveness of our algorithms.

关键词： Differential privacy nondecomposable problem distributed online learning time-varying networks

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A multi-wing butterfly chaotic system and its implementation

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INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 2017年第11期45卷 1873-1884页

作者： Ma, Junpeng Wang, Lidan Duan, Shukai Xu, Yaming Southwest Univ Sch Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

It has been a common brief that the principle for generating multi-wing attractor from a Lorenz-like chaotic system is to construct an even symmetric function for the system. In this pursuit, research effort on exploring the possibility of using non-even symmetric function is scant, if any. This paper attempts to reveal the intrinsic relation between such a system function and its generated multi-wing attractor in a chaotic system. It will be shown that a rather simple asymmetric function applying to the Shimizu-Morioka system can indeed generate a multi-wing butterfly chaotic attractor. Then, some basic properties, compound structure, bifurcation diagram, Lyapunov exponent spectrum, and dynamical analysis of the new chaotic system are investigated in detail. Finally, an electronic circuit is designed and its experimental results are presented, which well match the numerical simulation results, thereby verifying the feasibility of the new method. Copyright (c) 2017 John Wiley & Sons, Ltd.

关键词： asymmetric function dynamical analysis multi-wing butterfly attractor circuit implementation

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A novel memristive Hopfield neural network with application in associative memory

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NEUROCOMPUTING 2017年 227卷 142-148页

作者： Yang, Jiu Wang, Lidan Wang, Yan Guo, Tengteng Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

Memristor is a nanoscale electronic device that exhibits the synaptic characteristics in artificial neural network. Some valuable memristor-based synaptic circuits have been presented. However, the circuitry implementations of some simple neural network are still rarely involved before. This paper contributes to construct a novel memristive Hopfield neural network circuit. On one hand, an improved memristor bridge circuit is employed to realize synaptic operation which better performs zero, positive and negative synaptic weights without requiring any switches and inverters, and Pspice implementation scheme is also considered. On the other hand, the proposed bridge circuit greatly simplifies the structure of neural network, and reduces the conversion process between current and voltage signal. Furthermore, the associative memory in binary and color images is demonstrated ori the basis of the proposed memristive network. A series of numerical simulations are designed to verify associative memory capability, and experimental results demonstrate the effectiveness of the proposed neural network via the cases of single-associative memory and multi-associative memory.

关键词： Memristor Neural network Associative memory Memristor bridge circuit Synaptic weight

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A Modified Variational Bayesian Noise Adaptive Kalman Filter

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circuits SYSTEMS AND SIGNAL PROCESSING 2017年第10期36卷 4260-4277页

作者： Wang, Shi-Yuan Yin, Chao Duan, Shu-Kai Wang, Li-Dan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

Kalman filter suffers from performance degradation when applied to dynamic systems with unknown noise statistics. To address this problem, the variational Bayesian noise adaptive Kalman filter (VB-AKF) jointly estimates the state and noise using the variational Bayesian approximation method. In this paper, a modified variational Bayesian noise adaptive Kalman filter (VB-MAKF) is proposed by designing a novel dynamic model for tracking the variances of measurement noise. In the proposed dynamic model, the change in estimated noise variance is utilized to control a continuous and bounded function, which is specifically designed to follow the change in real noise variance, adaptively. We see from the numerical simulations that, in comparison with VB-AKF, the proposed VB-MAKF can achieve higher estimation accuracy of noise variances and thus provide higher estimation accuracy of states.

关键词： State estimation Kalman filter Noise adaptive filter Variational Bayesian methods

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A finite-time projection neural network to solve the joint optimal dispatching problem of CHP and wind power

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NEURAL COMPUTING & APPLICATIONS 2022年第10期34卷 7405-7417页

作者： Wei, Boyu He, Xing Southwest Univ Coll Elect & Informat Engn Chongqing Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing Peoples R China

This paper constructs an optimal scheduling model for combined heat and power generation units with heat storage and wind power generation considering carbon transaction costs and optimizes the output of each unit to reduce wind curtailment rate, carbon emissions, and total operating costs. In the case of considering transmission loss, the optimal scheduling model subject to actual operation constraints is expressed as a non-convex optimization problem. Based on a sufficient condition, the equivalent problem transformed from the original optimization problem is expressed as a convex problem, and a finite-time reduced-dimensional projection neural network with time-varying parameters is proposed to solve the problem. The proposed neural network can reach convergence in a limited time. The Lyapunov function is used to prove the convergence of the algorithm. Finally, the effectiveness of the designed neural network is verified by numerical simulation. Compared with reduced-dimensional projection neural network and finite-time fixed-parameter reduced-dimensional projection neural network, our neural network has a faster convergence speed.

关键词： Microgrid Combined heat and power with wind power Finite-time projection neural network Non-convex optimization Convergence speed

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