Blind source separation (BSS) algorithms, such as gradient convolution kernel compensation (gCKC), can efficiently and accurately decompose high-density surface electromyography (HD-sEMG) signals into constituent motor unit (MU) action potential trains. Once the separation matrix is blindly estimated on a signal interval, it is also possible to apply the same matrix to subsequent signal segments. Nonetheless, the trained separation matrices are sub-optimal in noisy conditions and require that incoming data undergo computationally expensive whitening. One unexplored alternative is to instead use the paired HD-sEMG signal and BSS output to train a model to predict MU activations within a supervised learning framework. A gated recurrent unit (GRU) network was trained to decompose both simulated and experimental unwhitened HD-sEMG signal using the output of the gCKC algorithm. The results on the experimental data were validated by comparison with the decomposition of concurrently recorded intramuscular EMG signals. The GRU network outperformed gCKC at low signal-to-noise ratios, proving superior performance in generalising to new data. Using 12 seconds of experimental data per recording, the GRU performed similarly to gCKC, at rates of agreement of 92.5% (84.5% -97.5%) and 94.9% (88.8% -100.0%) respectively for GRU and gCKC against matched intramuscular sources.
PURPOSE. Our aim was to assess retinal venous diameter and segmented retinal layer thickness variation in acute systemic hypoxia with and without acetazolamide and to relate these changes to high altitude headache (HAH), as a proxy for intracerebral pathophysiology. METHODS. A total of 20 subjects participated in a 4-day ascent to the Margherita Hut (4,559 m) on Monte Rosa in the Italian Alps. Each participant was randomized to either oral acetazolamide 250 mg twice daily or placebo. A combination of digital imaging and optical coherence tomography was used to measure retinal vessel diameter and retinal layer thickness. Clinically-assessed HAH was recorded. RESULTS. A total of 18 participants had usable digital and OCT images, with 12 developing HAH. Significant thickening was seen only in the two inner layers of the retina, the retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL) at P ¼ 0.012 and P ¼ 0.010, respectively, independent of acetazolamide. There was a significant positive correlation between HAH and both retinal venous diameter (T ¼ 4.953, P ¼ 0.001) and retinal artery diameter (T ¼ 2.865, P ¼ 0.015), with both unaffected by acetazolamide (F ¼ 0.439, P ¼ 0.518). CONCLUSIONS. Retinal venous diameter correlates positively with HAH, adding further evidence for the proposed venous outflow limitation mechanism. The inner layers of the retina swelled disproportionately when compared to the outer layers under conditions of systemic hypoxia. Acetazolamide does not appear to influence altitudinal changes of retinal layers and vasculature.
Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces.
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