Notes

Data-Driven Precise Type of Osteosarcoma.
Pertinent aspects involving self-care in kids along with teenagers together with spastic cerebral palsy.
Drowsy driving is one of the major causes in traffic accidents worldwide. Various electroencephalography (EEG)-based feature extraction methods are proposed to detect driving drowsiness, to name a few, spectral power features and fuzzy entropy features. However, most existing studies only concentrate on features in each channel separately to identify drowsiness, making them vulnerable to variability across different sessions and subjects without sufficient data. In this paper, we propose a method called Tensor Network Features (TNF) to exploit underlying structure of drowsiness patterns and extract features based on tensor network. This TNF method first introduces Tucker decomposition to tensorized EEG channel data of training set, then features of training and testing tensor samples are extracted from the corresponding subspace matrices through tensor network summation. The performance of the proposed TNF method was evaluated through a recently published EEG dataset during a sustained-attention driving task. Compared with spectral power features and fuzzy entropy features, the accuracy of TNF method is improved by 6.7% and 10.3% on average with maximum value 17.3% and 29.7% respectively, which is promising in developing practical and robust cross-session driving drowsiness detection system.Accurate and reliable detecting of driving fatigue using Electroencephalography (EEG) signals is a method to reduce traffic accidents. So far, it is natural to cut the part of operating the steering wheel data away for achieving the relatively high accuracy in detecting driving fatigue using EEG data. However, the data segment during operating the steering wheel also contains valuable information. Moreover, operating the steering wheel is a common practice during actual driving. In this study, we utilize the part of data operating the steering wheel to detecting fatigue. The feature used is the spectral band power calculates from the data. For each experiment and each experimental participant, the data and features are divided into sessions and subjects. Using the divided features, this work performs cross-session and cross-subject verification and comparison on the two classification methods of logistic regression and multi-layer perceptron. To compare the effect, the experiment is conducted on the data both operating the steering wheel and not operating the steering wheel. The result shows that the bias between the average accuracy of two types of data is only 2.27%, and the effect of using multi-layer perceptron is 10.37% better than using logistic regression. This proves that the data segment during operating the steering wheel also contains valid information and can be used for driving fatigue detection.Freezing of gait (FOG) is a sudden cessation of locomotion in advanced Parkinson's disease (PD). A FOG episode can lead to falls, decreased mobility, and decreased overall quality of life. Prediction of FOG episodes provides an opportunity for intervention and freeze prevention. A novel method of FOG prediction that uses foot plantar pressure data acquired during gait was developed and evaluated, with plantar pressure data treated as 2D images and classified using a convolutional neural network (CNN). Data from five people with PD and a history of FOG were collected during walking trials. FOG instances were identified and data preceding each freeze were labeled as Pre-FOG. Left and right foot FScan pressure frames were concatenated into a single 60x42 pressure array. Each frame was considered as an independent image and classified as Pre-FOG, FOG, or Non-FOG, using the CNN. From prediction models using different Pre-FOG durations, shorter Pre-FOG durations performed best, with Pre-FOG class sensitivity 94.3%, and specificity 95.1%. These results demonstrated that foot pressure distribution alone can be a good FOG predictor when treating each plantar pressure frame as a 2D image, and classifying the images using a CNN. Furthermore, the CNN eliminated the need for feature extraction and selection.Clinical Relevance- This research demonstrated that foot plantar pressure data can be used to predict freezing of gait occurrence, using a convolutional neural network deep learning technique. selleck products This had the added advantage of eliminating the need for feature extraction and selection.The vestibulo-ocular reflex (VOR) is a dynamic system of the human brain that helps to maintain balance and to stabilize vision during head movement. The video head impulse test (vHIT) is a clinical test that uses lightweight, high-speed video goggles to examine the VOR function by calculating the ratio of eye-movement to head-movement velocities. The main problem with a patient's vHIT is that data coming from the goggles may have artifacts and other noise. This paper proposes an impulse classification network (ICN) using a one-dimensional convolutional neural network that can detect noisy data and classify human VOR impulses. Our ICN found actual classes of a patient's impulses with 95% accuracy.Clinical Relevance-ICN is a high-performance classification method that works on a patient's vHIT impulse data by identifying abnormalities and artifacts. This method is an advanced clinical decision support system that can help doctors quickly make decisions.Researchers have been using signal processing based methods to assess speech from Parkinson's disease (PD) patients and identify the contrasting features in comparison to speech from healthy controls (HC). selleck products The methodologies follow conventional approach of segmenting speech over a fixed window (≈25ms to 30ms) followed by feature extraction and classification. The proposed methodology uses MFCCs extracted from pitch synchronous and fixed window (25ms) based speech segments for classification using fine Gaussian support vector machines (SVM). Three word utterances with three different vowel sounds are used for this analysis. Clustering experiments are aimed at identifying two clusters and class labels (PD/HC) are assigned based on number of participants from the respective class in the cluster. The features are divided into 9 groups based on the vowel content to evaluate the effect of different vowel sounds. Principal component analysis (PCA) is used for dimensionality reduction along with a 10-fold cross-validation.
Website: https://www.selleckchem.com/products/chloroquine-phosphate.html