Carmo Ana Sofia, Abreu Mariana, Fred Ana Luísa Nobre, da Silva Hugo Plácido
Department of Bioengineering, Instituto Superior Tecnico (IST), Universidade de Lisboa, Lisbon, Portugal.
Instituto de Telecomunicações (IT), Lisbon, Portugal.
Front Neuroinform. 2022 May 23;16:837278. doi: 10.3389/fninf.2022.837278. eCollection 2022.
Biosignals represent a first-line source of information to understand the behavior and state of human biological systems, often used in machine learning problems. However, the development of healthcare-related algorithms that are both personalized and robust requires the collection of large volumes of data to capture representative instances of all possible states. While the rise of flexible biosignal acquisition solutions has enabled the expedition of data collection, they often require complicated frameworks or do not provide the customization required in some research contexts. As such, EpiBOX was developed as an open-source, standalone, and automated platform that enables the long-term acquisition of biosignals, passable to be operated by individuals with low technological proficiency. In particular, in this paper, we present an in-depth explanation of the framework, methods for the evaluation of its performance, and the corresponding findings regarding the perspective of the end-user. The impact of the network connection on data transfer latency was studied, demonstrating innocuous latency values for reasonable signal strengths and manageable latency values even when the connection was unstable. Moreover, performance profiling of the EpiBOX user interface (mobile application) indicates a suitable performance in all aspects, providing an encouraging outlook on adherence to the system. Finally, the experience of our research group is described as a use case, indicating a promising outlook regarding the use of the EpiBOX framework within similar contexts. As a byproduct of these features, our hope is that by empowering physicians, technicians, and monitored subjects to supervise the biosignal collection process, we enable researchers to scale biosignal collection.
生物信号是了解人类生物系统行为和状态的一线信息来源，常用于机器学习问题。然而，开发既个性化又强大的医疗保健相关算法需要收集大量数据，以捕捉所有可能状态的代表性实例。虽然灵活的生物信号采集解决方案的兴起加快了数据收集速度，但它们通常需要复杂的框架，或者无法提供某些研究背景所需的定制功能。因此，EpiBOX被开发为一个开源、独立且自动化的平台，能够长期采集生物信号，技术水平较低的人员也可以操作。特别是在本文中，我们深入解释了该框架、其性能评估方法以及从最终用户角度得出的相应结果。研究了网络连接对数据传输延迟的影响，结果表明，即使在连接不稳定的情况下，对于合理的信号强度，延迟值也无害，且延迟值可控。此外，EpiBOX用户界面（移动应用程序）的性能分析表明其在各方面都有合适的性能，这为用户坚持使用该系统提供了令人鼓舞的前景。最后，我们将研究小组的经验作为一个案例进行描述，这表明在类似背景下使用EpiBOX框架具有广阔的前景。作为这些特性的一个附带成果，我们希望通过让医生、技术人员和受监测对象能够监督生物信号采集过程，使研究人员能够扩大生物信号的采集规模。
