Benchmarking of the BITalino biomedical toolkit against an established gold standard

[摘要] The low-cost multimodal platform BITalino is being increasingly used for educational and research purposes. However, there is still a lack of well-structured work comparing data acquired by this toolkit against a reference device, using established experimental protocols. This work intends to fill the said gap by benchmarking the performance of BITalino against the BioPac MP35 Student Lab Pro device. This work followed a methodical experimental protocol to acquire data from the two devices simultaneously. Four physiological signals were acquired: electrocardiography, electromyography, electrodermal activity and electroencephalography. Root mean square error and coefficient of determination were computed to analyse differences between BITalino and BioPac. Electrodermal activity signals were very similar for the two devices, even without applying any major signal processing techniques. For electrocardiography, a simple morphological comparison also revealed high similarity between devices, and this similarity increased after a common segmentation procedure was followed. Regarding electromyography and electroencephalography data, the approach consisted of comparing features extracted using common post-processing methods. The differences between BITalino and BioPac were again small. Overall, the results presented here show a close similarity between data acquired by the BITalino and by the reference device. This is an important validation step for all researchers working with this multimodal platform.

[学科分类] 肠胃与肝脏病学

[关键词] physiology;feature extraction;electroencephalography;medical signal processing;electromyography;data acquisition;electrocardiography;medical signal detection;mean square error methods;BITalino biomedical toolkit;educational research purposes;BioPac MP35 Student Lab Pro device;methodical experimental protocol;electrodermal activity signals;signal processing techniques;electroencephalography data;physiological signal acquisition;root mean square error;electromyography data;post-processing methods;electrocardiography