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Perceptual affirmation regarding appear surroundings duplication in the plane mock-up.
Single-use EMG-force models (i.e., a new model is trained each time the electrodes are donned) are used in various areas, including ergonomics assessment, clinical biomechanics, and motor control research. For one degree of freedom (1-DoF) tasks, input-output (black box) models are common. Recently, black box models have expanded to 2-DoF tasks. To facilitate efficient training, we examined parameters of black box model training methods in 2-DoF force-varying, constant-posture tasks consisting of hand open-close combined with one wrist DoF. We found that approximately 40-60 s of training data is best, with progressively higher EMG-force errors occurring for progressively shorter training durations. Surprisingly, 2-DoF models in which the dynamics were universal across all subjects (only channel gain was trained to each subject) generally performed 15-21% better than models in which the complete dynamics were trained to each subject. In summary, lower error EMG-force models can be formed through diligent attention to optimization of these factors.Edge information is essential for object recognition and motion detection. It is reported that photoreceptors, horizontal cells and bipolar cells in the outer retina involved for edge detection. Moreover, it is known that the center and the surround receptive field structure found in the bipolar cell layer is thought to be related to an initial process of edge detection. In the present study, we constructed retinal network models including photoreceptors, horizontal cells, and bipolar cells using single-compartment neurons to investigate those contributions for edge detection. We simulate fixation of a natural image with changing the size of the horizontal cell receptive field and confirmed that the constructed model successfully extracts edges in the image. Furthermore, most of the edge in the scene is extracted when the size of the horizontal cell receptive field matched with that reported in anatomical evidence. To evaluate the performance of edge detection, we compare the result of edge detection with the Canny algorithm. As a result, we conformed that the model well detects fine edges similar to the Canny edge detection.Closed-loop controlled drug dosing has the potential of revolutionizing clinical anesthesia. However, inter-patient variability in drug sensitivity poses a central challenge to the synthesis of safe controllers. Identifying a full individual pharmacokinetic-pharmacodynamic (PKPD) model for this synthesis is clinically infeasible due to limited excitation of PKPD dynamics and presence of unmodeled disturbances. This work presents a novel method to mitigate inter-patient variability. It is based on 1) partitioning an a priori known model set into subsets; 2) synthesizing an optimal robust controller for each subset; 3) classifying patients into one of the subsets online based on demographic or induction phase data; 4) applying the associated closed-loop controller. The method is investigated in a simulation study, utilizing a set of 47 clinically obtained patient models. Results are presented and discussed.Clinical relevance-The proposed method is easy to implement in clinical practice, and has potential to reduce the impact from surgical stimulation disturbances, and to result in safer closed-loop anesthesia with less risk of under- and over dosing.Automatic electrocardiogram (ECG) analysis for pacemaker patients is crucial for monitoring cardiac conditions and the effectiveness of cardiac resynchronization treatment. However, under the condition of energy-saving remote monitoring, the low-sampling-rate issue of an ECG device can lead to the miss detection of pacemaker spikes as well as incorrect analysis on paced rhythm and non-paced arrhythmias. To solve the issue, this paper proposed a novel system that applies the compressive sampling (CS) framework to sub-Nyquist acquire and reconstruct ECG, and then uses multi-dimensional feature-based deep learning to identify paced rhythm and non-paced arrhythmias. Simulation testing results on ECG databases and comparison with existing approaches demonstrate its effectiveness and outstanding performance for pacemaker ECG analysis.Bundle branch block (BBB) is one of the most common cardiac disorder, and can be detected by electro-cardiogram (ECG) signal in clinical practice. Conventional methods adopted some kinds of hand-craft features, whose discriminative power is relatively low. On the other hand, these methods were based on the supervised learning, which required the high cost heartbeat annotation in the training. In this paper, a novel end-to-end deep network was proposed to classify three types of heartbeat right BBB (RBBB), left BBB (LBBB) and others with a multiple instance learning based training strategy. #link# We trained the proposed method on the China Physiological Signal Challenge 2018 database (CPSC) and tested on the MIT-BIH Arrhythmia database (AR). The proposed method achieved an accuracy of 78.58%, and sensitivity of 84.78% (LBBB), 51.23% (others) and 99.72% (RBBB), better than the baseline methods. Experimental results show that our method would be a good choice for the BBB classification on the ECG dataset with record-level labels instead of heartbeat annotations.Premature ventricular contraction (PVC) is associated to the risk of ventricular dysfunction and cardiovascular events. Its diagnosis depends on a long-time monitoring, and computational tools for PVC recognition can provide significant assistance to specialists. For this purpose, we present an automatic algorithm for the recognition PVC beat based on long-term 12-lead ECG.A total of 249 patients with PVC were included in this study. Initially, a novel QRS onset detection function was used to automatically extract QRS complexes from massive original ECG data. Then, non-personalized but shared QRS-width features of 12-lead QRS complexes were extracted and fed to a binary classifier based on SVM. In EI1 to verify the model, 17, 512 normal beats and 17, 690 PVC beats extracted from 35 patients were used for training, and another 215 normal beats and 291 PVC beats selected randomly from the remaining 214 patients were used for testing.As a result, the achieved accuracy, sensitivity, specificity in training data and testing data are 98.
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