Neuropathies are gaining higher relevance in clinical settings, as they risk permanently jeopardizing a person’s life. To support the recovery of patients, the use of fully implanted devices is emerging as one of the most promising solutions. However, these devices, even if becoming an integral part of a fully complex neural nanonetwork system, pose numerous challenges. In this article, we address one of them, which consists of the classification of motor/sensory stimuli. The task is performed by exploring four different types of artificial neural networks (ANNs) to extract various sensory stimuli from the electroneurographic (ENG) signal measured in the sciatic nerve of rats. Different sizes of the data sets are considered to analyze the feasibility of the investigated ANNs for real-time classification through a comparison of their performance in terms of accuracy, F1-score, and prediction time. The design of the ANNs takes advantage of the modelling of the ENG signal as a multiple-input multiple-output (MIMO) system to describe the measures taken by state-of-the-art implanted nerve interfaces. These are based on the use of multi-contact cuff electrodes to achieve nanoscale spatial discrimination of the nerve activity. The MIMO ENG signal model is another contribution of this paper. Our results show that some ANNs are more suitable for real-time applications, being capable of achieving accuracies over 90% for signal windows of 100 and $200\,$ ms with a low enough processing time to be effective for pathology recovery.

中文翻译：

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基于人工神经网络的植入神经接口 ENG 信号实时分类


神经病在临床环境中变得越来越重要，因为它们有永久危害人生命的风险。为了支持患者的康复，使用完全植入的设备正在成为最有前途的解决方案之一。然而，这些设备即使成为完全复杂的神经纳米网络系统的组成部分，也会带来许多挑战。在本文中，我们讨论其中之一，其中包括运动/感觉刺激的分类。该任务是通过探索四种不同类型的人工神经网络（ANN）来从大鼠坐骨神经中测量的神经电图（ENG）信号中提取各种感觉刺激来执行的。考虑不同大小的数据集，通过比较准确度、F1 分数和预测时间方面的性能，分析所研究的 ANN 进行实时分类的可行性。 ANN 的设计利用 ENG 信号建模为多输入多输出 (MIMO) 系统来描述最先进的植入神经接口所采取的措施。这些基于使用多接触袖带电极来实现神经活动的纳米级空间辨别。 MIMO ENG 信号模型是本文的另一个贡献。我们的结果表明，一些 ANN 更适合实时应用，对于 100 和 100 的信号窗口能够实现超过 90% 的准确度。 $200\,$ ms 的处理时间足够短，可以有效地进行病理恢复。