Gomez H, Camacho J, Yelicich B, Moraes L, Biestro A, Puppo C
Institute of Physics, Universidad de la Republica, Uruguay.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2358-61. doi: 10.1109/IEMBS.2010.5627936.
A low cost multimodal monitoring and signal processing platform is presented. A modular and flexible system was developed, aimed to continuous acquisition of several biological variables at patient bed-head and further processing with application specific algorithms. System hardware is made of a six-channel isolation and signal conditioning front-end along with a high resolution analog-to-digital converter board connected to a standard laptop. Whole system hardware is compact and light weight, which ensures portability and ease of use at intensive care units. System software is divided in three modules: Acquisition, Signal Processing and Patients Data Management. The first one allows configuring each acquisition channel parameters, depending on the biological variable connected to it, and to store up to several hours of continuous data. Signal processing module implements novel algorithms for research purposes like dynamic cerebral autoregulation, optimal perfusion pressure, critical closing pressure or pulsatility index. It is flexible enough to easily add new processing algorithms, export data to different formats and create graphical reports. Patients data management module organizes acquired records, which allows selecting cases for new studies based on different criteria like monitored variables or pathological information. In this work, whole system architecture is described and algorithms included into the cerebral hemodynamics toolbox are presented along with experimental results.
本文介绍了一种低成本的多模态监测与信号处理平台。开发了一种模块化且灵活的系统，旨在在患者床头连续采集多个生物变量，并使用特定应用算法进行进一步处理。系统硬件由一个六通道隔离和信号调理前端以及一块连接到标准笔记本电脑的高分辨率模数转换器板组成。整个系统硬件紧凑且重量轻，确保了在重症监护病房的便携性和易用性。系统软件分为三个模块：采集、信号处理和患者数据管理。第一个模块允许根据连接的生物变量配置每个采集通道的参数，并存储长达数小时的连续数据。信号处理模块实现了用于研究目的的新颖算法，如动态脑自动调节、最佳灌注压、临界关闭压或搏动指数。它足够灵活，能够轻松添加新的处理算法、将数据导出为不同格式并创建图形报告。患者数据管理模块整理采集到的记录，允许根据不同标准（如监测变量或病理信息）选择用于新研究的病例。在这项工作中，描述了整个系统架构，并展示了包含在脑血流动力学工具箱中的算法以及实验结果。
