Real time electrocardiogram QRS detection using combined adaptive threshold.
Real time electrocardiogram QRS detection using combined adaptive threshold.
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DOI：
10.1186/1475-925x-3-28
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发表时间：
2004-08-27
影响因子：
3.9
通讯作者：
Christov II
中科院分区：
文献类型：
--
作者：
Christov II
QRS和室性搏动检测是心电信号处理和分析的基本步骤。已提出和使用的方法种类繁多，具有很高的正确检测率。然而，这一问题仍然悬而未决，特别是在噪声较高的心电信号中，提出了一种实时检测方法，该方法基于对多个心电导联中的一个导联的求和微分心电的绝对值与自适应阈值的比较。该阈值结合了三个参数：自适应转换速率值，当高频噪声出现时上升的第二个值，以及旨在避免丢失低幅度节拍的第三个值。开发了两个算法：算法1在当前节拍检测，算法2另外还有RR间期分析组件。无论分辨率和采样频率如何，算法都会自动调整阈值和加权常数。它们与任意数量的L导联一起工作，自同步到QRS或心搏斜率，并适应心搏间期。这些算法由一位独立的专家进行了测试，从而排除了可能的作者的影响，使用了MIT-BIH心律失常数据库的所有48条全长心电记录。结果表明，算法1的灵敏度Se=99.69%，特异度Sp=99.65%，算法2的灵敏度Se=99.74%，特异度Sp=99.65%。
QRS and ventricular beat detection is a basic procedure for electrocardiogram (ECG) processing and analysis. Large variety of methods have been proposed and used, featuring high percentages of correct detection. Nevertheless, the problem remains open especially with respect to higher detection accuracy in noisy ECGs A real-time detection method is proposed, based on comparison between absolute values of summed differentiated electrocardiograms of one of more ECG leads and adaptive threshold. The threshold combines three parameters: an adaptive slew-rate value, a second value which rises when high-frequency noise occurs, and a third one intended to avoid missing of low amplitude beats. Two algorithms were developed: Algorithm 1 detects at the current beat and Algorithm 2 has an RR interval analysis component in addition. The algorithms are self-adjusting to the thresholds and weighting constants, regardless of resolution and sampling frequency used. They operate with any number L of ECG leads, self-synchronize to QRS or beat slopes and adapt to beat-to-beat intervals. The algorithms were tested by an independent expert, thus excluding possible author's influence, using all 48 full-length ECG records of the MIT-BIH arrhythmia database. The results were: sensitivity Se = 99.69 % and specificity Sp = 99.65 % for Algorithm 1 and Se = 99.74 % and Sp = 99.65 % for Algorithm 2. The statistical indices are higher than, or comparable to those, cited in the scientific literature.