Exploitation of precise timing capabilities of single board computer for transcranial magnetic stimulationShow others and affiliations
2020 (English)In: Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2020Conference paper, Published paper (Refereed)
Abstract [en]
An early Alzheimer Disease (AD) diagnosis is fundamental for maximizing the effectiveness of treatment administration. Unfortunately, distinguish AD from other neurodegenerative dementias, such as Frontotemporal Dementia (FTD) is not trivial. Transcranial Magnetic Stimulation (TMS) emerged as an effective non-invasive, easy to apply and not time-consuming solution. TMS-based techniques generally require expensive ad hoc clinical equipment that suffer from poor flexibility and user friendliness in defining the specific diagnostic protocol. In this work, a low-cost BeagleBone Black single board computer (BBB-SBC) has been used to implement all the functionalities required to manage a TMS-based instrument. Timeliness of signal generation is guaranteed by dedicated programmable real-time units hosted by the BBB-SBC System on Chip. Web-based interface, complemented by IoT-like features, provide a high degree of versatility and permit the execution of many different diagnostic protocols. In particular, the experimental validation confirms timing error in the sub-microsecond range, more than enough for the considered application. © 2020 IEEE.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2020.
Keywords [en]
Alzheimer disease, electromyography, FPGA, Raspberry Pi, transcranial magnetic stimulation, Diagnosis, Multimedia systems, Neurodegenerative diseases, System-on-chip, Timing circuits, Diagnostic protocols, Experimental validations, Frontotemporal dementias, Signal generation, Single board computers, User friendliness, Web-based interface, Functional electric stimulation
Identifiers
URN: urn:nbn:se:miun:diva-41515DOI: 10.1109/SAS48726.2020.9220045Scopus ID: 2-s2.0-85095606256ISBN: 9781728148427 (print)OAI: oai:DiVA.org:miun-41515DiVA, id: diva2:1536268
Conference
2020 IEEE Sensors Applications Symposium, SAS 2020
2021-03-102021-03-102021-04-27Bibliographically approved