Comparison of Data Compression Methods for Implanted Real-Time Peripheral Nervous System

Research in the development of neuroprostheses aims to restore the loss bodily and motor functionalities. In order to have effective solutions, the physiological signals used to control neuroprostheses must be first measured and then properly reconstructed. Miniaturized electronic enables the realization of implantable devices suitable for the treatment of pathologies that cannot be cured with conventional medicine. However, given the limited data processing and transmission capabilities of microcontroller-based implantable devices, data compression algorithms must be developed with minimal distortion upon reconstructions. Although several compression techniques are available in the literature, a suitable strategy for compression of electroneurographic (ENG) signals has not yet been defined so far. The main goal of this work is to propose a pipeline to perform the compression of biomedical data. The approach is validated on available recordings of ENG signals. As main contribution, we applied different compression algorithms to the recorded ENG signals that were obtained in response to mechanical stimulation of a rat paw. A combination of lossy and lossless techniques was studied by measuring the performance in terms of time required for data compression, compression ratio, and distortion. To evaluate the use in real-time applications, the robustness of the techniques has been tested considering a temporal constraint of 300 ms.