136 research outputs found
Supplemental Material, files - Flexible model predictive control based on multivariable online adjustment mechanism for robust gait generation
No full textSupplemental Material, files for Flexible model predictive control based on multivariable online adjustment mechanism for robust gait generation by Sheng Dong, Zhaohui Yuan, Xiaojun Yu, Muhammad Tariq Sadiq, Jianrui Zhang, Fuli Zhang and Cheng Wang in International Journal of Advanced Robotic Systems</p
Macrovascular Complications and their risk factors in Type 2 Diabetic Patients in Hyderabad, Pakistan.
No full textIntroduction: Diabetes mellitus (DM), is the universally occurring non-communicable disease as well as exemplary health problem affecting peo-ple worldwide.1 The number of cases of DM are rising at an enormous pace irrespective of any age, gender, economic status or ethnicity around the globe.Objective: To evaluate the macro-vascular complications and its correlation with different risk factors among type-2 diabetic patients.Methodology: This cross-sectional study was conducted at Red Crescent General Hospital Latifabad Hyderabad from October 2018 to October 2020. Type 2 diabetics of either gender, between age 20 and 70 years, on diabetic medication visited during the study duration were included in the study. Data related to socio-demographic details and clinical features was collect-ed from the participants using a written questionnaire. Collected data was analyzed using SPSS ver. 22.Results: Significant association (p<0.05) was demonstrated between Coro-nary artery diseases and the risk factors like; age of patient, the duration of diabetes mellitus, diastolic as well as systolic blood pressures, body mass index and serum triglycerides levels. While the statistically significant asso-ciation (p<0.05) of peripheral vascular diseases with the duration of diabe-tes mellitus, systolic blood pressure and serum triglyceride levels. Whereas, cerebrovascular disease was associated (p<0.05) with age, systolic and diastolic BP.Conclusion: The coronary artery disease seems to be most frequent macro-vascular complication among the type 2 diabetic patients. Whereas the risk factors including; advancing age, duration of diabetes mellitus, hyperten-sion, BMI as well as serum triglycerides levels are the most significant fac-tors for these complications.Key Words: Coronary artery disease, Cerebrovascular Disease, Diabetes Mellitus Type 2, Peripheral vascular diseas
Numerical Simulation and Experimental Research on Flow Force and Pressure Stability in a Nozzle-Flapper Servo Valve
No full textIn the nozzle flapper servo valve, the transient flow force on the flapper is the fundamental reason that affects the pressure stability. The pressure pulsation in the pilot stage causes forced vibration of the flapper, and its deviation will directly influence the control pressure difference, which will make the pressure appear unstable. In order to grasp the principle and characteristics of transient flow force and its influence on pressure stability, a mathematical model of flapper displacement and control pressure is derived. For collecting the dynamic changes of the transient flow force and recording the motion behavior of the flapper, a three-dimensional model of the pilot-stage is established. Numerical simulations of turbulence phenomenon analysis are conducted with a variation of flapper displacement ranging from 5 μm to 20 μm. It can be concluded that the change trend of the flapper displacement is similar to the steady-state flow force and the transient flow force pulsation amplitude. Under the same structural parameters, the pulsating frequency of the flow force remains basically constant. The fluctuation of the flow force of the pilot stage will cause the pressure of the servo valve control cavity to vibrate to a certain extent, which is a factor that cannot be ignored that affects the output stability of the servo valve
Motor imagery BCI classification based on novel two-dimensional modelling in empirical wavelet transform
No full textBrain complexity and non-stationary nature of electroencephalography (EEG) signal make considerable challenges for the accurate identification of different motor-imagery (MI) tasks in brain–computer interface (BCI). In the proposed Letter, a novel framework is proposed for the automated accurate classification of MI tasks. First, raw EEG signals are denoised with multiscale principal component analysis. Secondly, denoised signals are decomposed by empirical wavelet transform into different modes. Thirdly, the two-dimensional (2D) modelling of modes is introduced to identify the variations of different signals. Fourthly, a single geometrical feature name as, a summation of distance from each point relative to a coordinate centre is extracted from 2D modelling of modes. Finally, the extracted feature vectors are provided to the feedforward neural network and cascade forward neural networks for classification check. The proposed study achieved 95.3% of total classification accuracy with 100% outcome for subject with very small training samples, which is outperforming existing methods on the same database
A New Framework for Automatic Detection of Motor and Mental Imagery EEG Signals for Robust BCI Systems
No full textNonstationary signal decomposition (SD) is a primary procedure to extract monotonic components or modes from electroencephalogram (EEG) signals for the development of robust brain-computer interface (BCI) systems. This study proposes a novel automated computerized framework for proficient identification of motor and mental imagery (MeI) EEG tasks by employing empirical Fourier decomposition (EFD) and improved EFD (IEFD) methods. Specifically, the multiscale principal component analysis (MSPCA) is rendered to denoise EEG data first, and then, EFD is utilized to decompose nonstationary EEG into subsequent modes, while the IEFD criterion is proposed for a single conspicuous mode selection. Finally, the time- and frequency-domain features are extracted and classified with a feedforward neural network (FFNN) classifier. Extensive experiments are conducted on four multichannel motor and MeI data sets from BCI competitions II and III using a tenfold cross-validation strategy. Results compared with the other existing methods demonstrated that the highest classification accuracies of 99.82% (data set IV-a), 93.33% (data set IV-b), 91.96% (data set III), and 88.08% (data set V) in subject-specific scenarios, while 82.70% (data set IV-a) in the subject-independent framework are achieved for IEFD with FFNN classifiers collectively. The overall exploratory results authenticate that the proposed IEFD-based automated computerized framework not only outperforms the conventional SD methods but is also robust and computationally efficient for the development of subject-dependent and subject-independent BCI systems
Identification of Motor and Mental Imagery EEG in Two and Multiclass Subject-Dependent Tasks Using Successive Decomposition Index
No full textThe development of fast and robust brain–computer interface (BCI) systems requires non-complex and efficient computational tools. The modern procedures adopted for this purpose are complex which limits their use in practical applications. In this study, for the first time, and to the best of our knowledge, a successive decomposition index (SDI)-based feature extraction approach is utilized for the classification of motor and mental imagery electroencephalography (EEG) tasks. First of all, the public datasets IVa, IVb, and V from BCI competition III were denoised using multiscale principal analysis (MSPCA), and then a SDI feature was calculated corresponding to each trial of the data. Finally, six benchmark machine learning and neural network classifiers were used to evaluate the performance of the proposed method. All the experiments were performed for motor and mental imagery datasets in binary and multiclass applications using a 10-fold cross-validation method. Furthermore, computerized automatic detection of motor and mental imagery using SDI (CADMMI-SDI) is developed to describe the proposed approach practically. The experimental results suggest that the highest classification accuracy of 97.46% (Dataset IVa), 99.52% (Dataset IVb), and 99.33% (Dataset V) was obtained using feedforward neural network classifier. Moreover, a series of experiments, namely, statistical analysis, channels variation, classifier parameters variation, processed and unprocessed data, and computational complexity, were performed and it was concluded that SDI is robust for noise, and a non-complex and efficient biomarker for the development of fast and accurate motor and mental imagery BCI systems
A novel computer-aided diagnosis framework for EEG-based identification of neural diseases
No full textRecent advances in electroencephalogram (EEG) signal classification have primarily focused on domain-specific approaches, which impede algorithm cross-discipline capability. This study introduces a new computer-aided diagnosis (CAD) system for the classification of two distinct EEG domains under a unified sequential framework. The key motivation to consider two neural diseases by one framework is to develop a unified algorithm for EEG classification. The main contributions of this study are five-fold. First, EEG signals are decomposed into 10 intrinsic mode functions (IMFs) with the help of empirical wavelet transform. Second, a novel two-dimensional (2D) modeling of IMFs is plotted to visualize the complexity of EEG signals. Third, several new geometrical features are extracted to analyze the dynamic and chaotic essence. Fourth, significant features are selected by binary particle swarm optimization algorithm (B–PSO). Fifth, selected features are fed to the k-nearest neighbor classifier for EEG signal classification purposes. All the experiments are executed on one depression and two epileptic EEG datasets in a leave one out cross-validation strategy. The proposed CAD system provides an average classification accuracy of 93.35% in depression detection, 99.33% for regular against ictal, and 97.33% for interictal versus ictal respectively. The overall empirical analysis authenticates that the proposed CAD outperforms the existing domain-specific methods in terms of classification accuracies and multirole adaptability, thus, can be endorsed as an effective automated neural rehabilitation system
Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain
No full textA widespread brain disorder of present days is depression which influences 264 million of the world’s population. Depression may cause diverse undesirable consequences, including poor physical health, suicide, and self-harm if left untreated. Depression may have adverse effects on the personal, social, and professional lives of individuals. Both neurologists and researchers are trying to detect depression by challenging brain signals of Electroencephalogram (EEG) with chaotic and non-stationary characteristics. It is essential to detect early-stage depression to help patients obtain the best treatment promptly to prevent harmful consequences. In this paper, we proposed a new method based on centered correntropy (CC) and empirical wavelet transform (EWT) for the classification of normal and depressed EEG signals. The EEG signals are decomposed to rhythms by EWT and then CC of rhythms is computed as the discrimination feature and fed to K-nearest neighbor and support vector machine (SVM) classifiers. The proposed method was evaluated using EEG signals recorded from 22 depression and 22 normal subjects. We achieved 98.76%, 98.47%, and 99.05% average classification accuracy (ACC), sensitivity, and specificity in a 10-fold cross-validation strategy by using an SVM classifier. Such efficient results conclude that the method proposed can be used as a fast and accurate computer-aided detection system for the diagnosis of patients with depression in clinics and hospitals.</p
Identification of normal and depression EEG signals in variational mode decomposition domain
No full textEarly detection of depression is critical in assisting patients in receiving the best therapy possible to avoid negative repercussions. Depression detection using electroencephalogram (EEG) signals is a simple, low-cost, convenient, and accurate approach. This paper proposes a six-stage novel method for detecting depression using EEG signals. First, EEG signals are recorded from 44 subjects, with 22 subjects being normal and 22 subjects being depressed. Second, a simple notch filter with EEG signals differencing approach is employed for effective preprocessing. Third, the variational mode decomposition (VMD) approach is implemented for nonlinear and non-stationary EEG signals analysis, resulting in many modes. Fourth, mutual information-based novel modes selection criterion is proposed to select the most informative modes. In the fifth step, a combination of linear and nonlinear features are extracted from selected modes and at last, classification is performed with neural networks. In this study, a novel single feature is also proposed, which is made using Log energy, norm entropies and fluctuation index, which delivers 100% classification accuracy, sensitivity and specificity. By using these features, a novel depression diagnostic index is also proposed. This integrated index would assist in quicker and more objective identification of normal and depression EEG signals. The proposed computerized framework and the DDI can help health workers, large enterprises, and product developers build a real-time system
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