Elgendi Mohamed
Department of Obstetrics & Gynecology, University of British Columbia and BC Children's & Women's Hospital, Vancouver, BC V6H 3N1, Canada.
School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
Diseases. 2018 Feb 24;6(1):18. doi: 10.3390/diseases6010018.
In the digital medicine field, biosignals, such as those of an electrocardiogram (ECG), are collected regularly for screening and diagnosis, and there continues to be an increasingly substantial shift towards collecting long-term ECG signals for remote monitoring, e.g., in smart homes. ECG signal collection is quite simple and only requires the use of inexpensive sensors, an active Internet connection, and a mobile device that acts as the medium between the sensors and the Internet (e.g., a mobile phone or laptop). Despite the ease and convenience of remote ECG data collection and transmission, the amount of time and energy required for the related remote computational processes remains a major limitation. This short note discusses a biosignal approach that uses fewer biomedical data for screening and diagnosis that is, compared to current data collection methods, equally, if not more, efficient.
在数字医学领域，生物信号，如心电图（ECG）信号，会被定期收集用于筛查和诊断，并且越来越多地转向收集长期心电图信号用于远程监测，例如在智能家居中。心电图信号收集非常简单，只需要使用价格低廉的传感器、活跃的互联网连接以及作为传感器和互联网之间媒介的移动设备（如手机或笔记本电脑）。尽管远程心电图数据收集和传输轻松便捷，但相关远程计算过程所需的时间和精力仍然是一个主要限制。本短文讨论了一种生物信号方法，该方法使用较少的生物医学数据进行筛查和诊断，与当前的数据收集方法相比，即便没有更高效率，至少也是同样高效的。
