Last updated on June 22, 2015. This conference program is tentative and subject to change
Muscle artifacts can be recorded using electromyography (EMG). Various computational methods for the reduction of muscle artifacts in EEG data exist like the ICA algorithm InfoMax and the AMICA algorithm. However, there exists no objective measure to compare different algorithms concerning their performance on EEG data.
We defined a test protocol with specific neck and body movements and measured EEG and EMG simultaneously to compare the InfoMax algorithm and the AMICA algorithm. A novel objective measure enabled to compare both algo- rithms according to their performance. Results showed that the AMICA algorithm outperformed the InfoMax algorithm. In further research, we will continue using the established objective measure to test the performance of other algorithms for the reduction of artifacts.
The variance of recorded human motion was analyzed based on the standard deviations of subject parameters, joint angles, and hand positions. Variances in joint angles were found to be similar in magnitude to root mean squared error of kinematics based motion simulation. To evaluate the relative variance, the forward kinematic solutions of the trials were found after removing subject parameter variance and reducing joint angle variance. The variance in the forward kinematic solution was then compared to the recorded hand position variance. Reductions in subject parameter and joint angle variance produced a proportionally much smaller reduction in the calculated hand position variance. Using the average instead of individual subject parameters had only a small impact on hand position variance. Modifying joint angles to reduce variance had a greater impact on the calculated hand position variance than using average subject parameters, but was still a relatively small change. Future work will focus on using these results to create formalize
HeartCycle research project (partially funded by the European Commission) has developed a personal health system for cardiovascular diseases management with the aim to address this problem. This paper describes the Patient Loop of this solution, including the different components, the adopted user interaction, and the implemented patients’ education and coaching strategy.
The objective of this paper is to evaluate the feasibility of the calculation of features extracted from endocardial acceleration (EA) signals and the potential utility of these features for the intraoperative optimization of CRT. Endocardial intraoperative data from one patient are analyzed for 44 different pacing configurations, including changes in the atrio-ventricular and inter-ventricular delays and different ventricular stimulation sites. The main EA features are extracted for each pacing configuration and analyzed so as to estimate the intra-configuration and inter-configuration variability. Results show the feasibility of the proposed approach and suggest the potential utility of EA for intraoperative monitoring of the cardiac function and defining optimal, adaptive pacing configurations.
We implemented the approach into our surgery simulator and compared the accuracy of the deformation and the computation time among 1) proposed approach, 2) L-FE), and 3) NL-FEM. Finally, we show the effectiveness of our proposed approach.
This muscle model is defined independently from load properties, and muscle elastance is dynamic and reflects changing numbers of crossbridge bonds. The model parameters were extracted from measured force and length data from cat papillary muscle experiments in the literature [Sonnenblick 1962]. The purpose of this paper is to present in some detail how to describe a particular muscle strip from measured force data. The resulting model is tested under a wide range of mechanical conditions, such as isometric and isotonic contractions for normal and varied inotropic state, and muscle velocity is computed for different muscle loads. Computed results compare favorably with similar measurements from the literature. The resulting lumped muscle model is a compact, yet comprehensive functional description of muscle dynamics.
For better results, these models and tools should be adapted to each patient. The first step toward this patient specific adaptation is to define which of the parameters must be identified and what are the signal features most suitable to do so. The sensitivity analysis of the model will enable us to answer this question.
To study the sensitivity of the 26 model parameters, We use the principle of elementary effects as described by Morris. We assume no prior knowledge of the possible variations of the parameters and use uniform distributions bounded by ±20% of their nominal value. As model output we considered not on the simulated EHG signal itself but 5 classical features extracted from the signal.
The results we obtain are the ranking of the model parameters in order of sensitivity. With 4 of the features the list of sensitive parameter is very consistent, however there are some differences in the rankings.
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