Comparison Analysis of GLCM and PCA on Parkinson's Disease Using Structural MRI

Parkinson disease (PD) is a neurological disorder where the dopaminergic neurons experience deterioration. It is caused from the death of the dopamine neurons present in the substantia nigra i.e., the mid part of the brain. The symptoms of this disease emerge slowly, the onset of the earlier stages shows some non-motor symptoms and with time motor symptoms can also be gauged. Parkinson is incurable but can be treated to improve the condition of the sufferer. No definite method for diagnosing PD has been concluded yet. However, researchers have suggested their own framework out of which MRI gave better results and is also a non-invasive method. In this study, the MRI images are used for extracting the features. For performing the feature extraction techniques Gray Level Co-occurrence Matrix and Principal Component Analysis are performed and are analysed. Feature extraction reduces the dimensionality of data. It aims to reduce the feature of data by generating new features from the original one.

CluSem: Accurate Clustering-based Ensemble Method to Predict Motor Imagery Tasks from Multi-channel EEG Data

Background: The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal task in the biosignal classification process in brain-computer interface (BCI) applications. Currently, this bio-engineering-based technology is being employed by researchers in various fields to develop cutting-edge applications. The classification of real-time MI-EEG signals is the most challenging task in these applications. The prediction performance of the existing classification methods is still limited due to the high dimensionality and dynamic behaviors of the real-time EEG data. Proposed Method: To enhance the classification performance of real-time BCI applications, this paper presents a new clustering-based ensemble technique called CluSem to mitigate this problem. We also develop a new brain game called CluGame using this method to evaluate the classification performance of real-time motor imagery movements. In this game, real-time EEG signal classification and prediction tabulation through animated balls are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices. Results: Our results demonstrate that CluSem is able to improve the classification accuracy between 5% and 15% compared to the existing methods on our collected as well as the publicly available EEG datasets. The source codes used to implement CluSem and CluGame are publicly available at https://github.com/MdOchiuddinMiah/MI-BCI_ML.

Addition of low-dose sufentanil to ropivacaine for reducing shivering and visceral traction pain during cesarean section

Objective To investigate the efficacy of low-dose sufentanil for preventing shivering and visceral traction pain during cesarean section under spinal anesthesia. Methods This was a prospective, randomized, controlled study. A total of 112 full-term parturients who underwent elective caesarean delivery were randomly divided into two groups. Group R received 0.75% isobaric ropivacaine intrathecally and group RS received 0.75% isobaric ropivacaine plus 5 µg sufentanil intrathecally. Results There were no significant differences in the maximum sensory block time, motor block time, duration of the surgery, and heart rate, mean arterial pressure, and blood oxygen saturation before and 1, 5, and 10 minutes after spinal anesthesia, and at the end of the surgery between the two groups. Shivering was significantly more common in group R (n = 30) than in group RS (n = 8). The incidence of visceral traction pain in group R (46.43%) was significantly higher than that in group RS (14.29%). There was no significant difference in the newborns’ Apgar scores between the groups. Conclusion Adding low-dose sufentanil to ropivacaine can significantly reduce the incidence of shivering and visceral traction pain after spinal anesthesia.

Parkinson’s Patients’ Tolerance for Risk and Willingness to Wait for Potential Benefits of Novel Neurostimulation Devices: A Patient-Centered Threshold Technique Study

Background. Parkinson’s disease (PD) is neurodegenerative, causing motor, cognitive, psychological, somatic, and autonomic symptoms. Understanding PD patients’ preferences for novel neurostimulation devices may help ensure that devices are delivered in a timely manner with the appropriate level of evidence. Our objective was to elicit preferences and willingness-to-wait for novel neurostimulation devices among PD patients to inform a model of optimal trial design. Methods. We developed and administered a survey to PD patients to quantify the maximum levels of risks that patients would accept to achieve potential benefits of a neurostimulation device. Threshold technique was used to quantify patients’ risk thresholds for new or worsening depression or anxiety, brain bleed, or death in exchange for improvements in “on-time,” motor symptoms, pain, cognition, and pill burden. The survey elicited patients’ willingness to wait to receive treatment benefit. Patients were recruited through Fox Insight, an online PD observational study. Results. A total of 2740 patients were included and a majority were White (94.6%) and had a 4-year college degree (69.8%). Risk thresholds increased as benefits increased. Threshold for depression or anxiety was substantially higher than threshold for brain bleed or death. Patient age, ambulation, and prior neurostimulation experience influenced risk tolerance. Patients were willing to wait an average of 4 to 13 years for devices that provide different levels of benefit. Conclusions. PD patients are willing to accept substantial risks to improve symptoms. Preferences are heterogeneous and depend on treatment benefit and patient characteristics. The results of this study may be useful in informing review of device applications and other regulatory decisions and will be input into a model of optimal trial design for neurostimulation devices.

Efficient Estimator of Rotor Temperature Designing for Electric and Hybrid Powertrain Platform

This paper presents an efficient method of estimation of rotor cage temperature for induction machine design, applied for electric and hybrid vehicles. This factor influences the torque produced by the induction machine with a field-oriented control algorithm. Equipping sensors to measure the temperature of a rotation component is expensive and is not representative of mass production. The approach of estimation of rotor cage temperature is based on the good knowledge of motor parameters and the estimation of the flux of the machine. For an accuracy inductance taking account of the saturation, the no-load test can be performed. The machine flux will be estimated taking account of the voltage drop of the system on the test-bench. The rapid prototyping in a real-time motor control platform will be presented that integrates this estimator of rotor temperature. We finally show the experimental testing results compared to the measurement of the rotor cage on a prototype asynchronous low-cost motor designing for battery electric city cars.

History of Mindfulness and Psychology

Mindfulness practice and protocols—often referred to as mindfulness-based interventions (MBIs)—have become increasingly popular in every sector of society, including healthcare, education, business, and government. Due to this exponential growth, thoughtful reflection is needed to understand the implications of, and interactions between, the historical context of mindfulness (insights and traditions that have been cultivated over the past 25 centuries) and its recent history (the adaptation and applications within healthcare, therapeutic and modern culture, primarily since the 1980s). Research has shown that MBIs have significant health benefits including decreased stress, insomnia, anxiety, and panic, along with enhancing personal well-being, perceptual sensitivity, processing speed, empathy, concentration, reaction time, motor skills, and cognitive performance including short- and long-term memory recall and academic performance. As with any adaptation, skillful decisions have to be made about what is included and excluded. Concerns and critiques have been raised by clinicians, researchers, and Buddhist scholars about the potential impact that the decontextualization of mindfulness from its original roots may have on the efficacy, content, focus, and delivery of MBIs. By honoring and reflecting on the insights, intentions, and work from both historical and contemporary perspectives of mindfulness, the field can support the continued development of effective, applicable, and accessible interventions and programs.

Fault Diagnosis of a Rotor and Ball-Bearing System Using DWT Integrated with SVM, GRNN, and Visual Dot Patterns

In this study, a set of methods for the inspection of a working motor in real time was proposed. The aim was to determine if ball-bearing operation is normal or abnormal and to conduct an inspection in real time. The system consists of motor control and measurement systems. The motor control system provides a set fixed speed, and the measurement system uses an accelerometer to measure the vibration, and the collected signal data are sent to a PC for analysis. This paper gives the details of the decomposition of vibration signals, using discrete wavelet transform (DWT) and computation of the features. It includes the classification of the features after analysis. Two major methods are used for the diagnosis of malfunction, the support vector machines (SVM) and general regression neural networks (GRNN). For visualization and to input the signals for visualization, they were input into a convolutional neural network (CNN) for further classification, as well as for the comparison of performance and results. Unique experimental processes were established with a particular hardware combination, and a comparison with commonly used methods was made. The results can be used for the design of a real-time motor that bears a diagnostic and malfunction warning system. This research establishes its own experimental process, according to the hardware combination and comparison of commonly used methods in research; a design for a real-time diagnosis of motor malfunction, as well as an early warning system, can be built thereupon.