School of Data and Computer Science, University of Sun Yat-sen, Guangzhou Higher Education Mega Center, No. 132 Waihuan East Road, Guangzhou 510006, China.
J Healthc Eng. 2018 Mar 15;2018:5694595. doi: 10.1155/2018/5694595. eCollection 2018.
Cardiovascular disease is the first cause of death around the world. In accomplishing quick and accurate diagnosis, automatic electrocardiogram (ECG) analysis algorithm plays an important role, whose first step is QRS detection. The threshold algorithm of QRS complex detection is known for its high-speed computation and minimized memory storage. In this mobile era, threshold algorithm can be easily transported into portable, wearable, and wireless ECG systems. However, the detection rate of the threshold algorithm still calls for improvement. An improved adaptive threshold algorithm for QRS detection is reported in this paper. The main steps of this algorithm are preprocessing, peak finding, and adaptive threshold QRS detecting. The detection rate is 99.41%, the sensitivity (Se) is 99.72%, and the specificity (Sp) is 99.69% on the MIT-BIH Arrhythmia database. A comparison is also made with two other algorithms, to prove our superiority. The suspicious abnormal area is shown at the end of the algorithm and RR-Lorenz plot drawn for doctors and cardiologists to use as aid for diagnosis.
心血管疾病是全球范围内的首要死因。在实现快速准确的诊断中，自动心电图（ECG）分析算法起着重要作用，其第一步是 QRS 检测。QRS 复合波检测的阈值算法以其高速计算和最小化的内存存储而闻名。在移动时代，阈值算法可以轻松应用于便携式、可穿戴和无线 ECG 系统。然而，阈值算法的检测率仍有待提高。本文报道了一种用于 QRS 检测的改进自适应阈值算法。该算法的主要步骤包括预处理、峰值检测和自适应阈值 QRS 检测。在 MIT-BIH 心律失常数据库上，该算法的检测率为 99.41%，灵敏度（Se）为 99.72%，特异性（Sp）为 99.69%。还与另外两种算法进行了比较，以证明我们的优越性。在算法结束时会显示可疑的异常区域，并绘制 RR-Lorenz 图，供医生和心脏病专家作为诊断辅助。
