Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada.
Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Bavaria, Germany.
PLoS One. 2014 Jan 7;9(1):e84018. doi: 10.1371/journal.pone.0084018. eCollection 2014.
Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.
心血管疾病是全球头号死因。目前，便携式电池供电系统，如带有无线 ECG 传感器的移动电话，有可能用于连续的心脏功能评估，可以轻松地融入日常生活。这些便携式即时诊断系统因此有助于发现和治疗心血管疾病。心电图分析的基础是稳健地检测突出的 QRS 复合波，以及其他心电图信号特征。然而，文献中并不清楚哪些心电图分析算法适合在移动设备上实现。我们根据三个评估标准研究当前的 QRS 检测算法：1）对噪声的鲁棒性，2）参数选择，和 3）数值效率，以针对通用的快速鲁棒检测器。此外，现有的 QRS 检测算法可能只在心电图信号的小片段内、在一定的幅度范围内、或在特定类型的心律失常和/或噪声中提供可接受的解决方案。在与最传统算法进行比较的背景下讨论了这些问题，并提出了未来开发适用于电池供电移动设备实现的可靠 QRS 检测方案的建议。
