Summary of Artificial Neural Networks-based Real-time Classification Of Eng Signals For Implanted Nerve Interfaces, by Antonio Coviello et al.
Artificial Neural Networks-based Real-time Classification of ENG Signals for Implanted Nerve Interfaces
by Antonio Coviello, Francesco Linsalata, Umberto Spagnolini, Maurizio Magarini
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The researchers aim to develop fully implanted devices that can support the recovery of patients with neuropathies. One challenge they address is the classification of motor/sensory stimuli using artificial neural networks (ANNs). The study explores four types of ANNs, analyzing their performance in terms of accuracy, F1-score, and prediction time for real-time classification. The ANNs are designed to model the electroneurographic (ENG) signal measured in rat sciatic nerves as a multiple-input multiple-output (MIMO) system. The results show that some ANNs achieve accuracies over 90% for signal windows of 100-200ms, making them suitable for real-time applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Neuropathies are serious health issues that can have long-lasting effects on people’s lives. To help patients recover, doctors are working on devices that can be fully implanted in the body. One important part of this is being able to tell the difference between different kinds of motor and sensory stimuli. Researchers are using artificial neural networks (ANNs) to do this. They tested four types of ANNs to see how well they could classify these stimuli. The results show that some ANNs can be very accurate, achieving over 90% accuracy in just a few hundred milliseconds. |
Keywords
* Artificial intelligence * Classification * F1 score
