Jekova Irena, Mitev Petar
Centre of Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G Bonchev str. bl. 105, 1113 Sofia, Bulgaria.
Physiol Meas. 2002 Nov;23(4):629-34. doi: 10.1088/0967-3334/23/4/303.
The recent development and increased application of automatic external defibrillators have prescribed very strong requirements towards ventricular fibrillation (VF) and fast ventricular tachycardia (VT > 180 bpm) detection from the surface electrocardiogram (ECG). We attempted to use informative parameters from several existing analysis methods and from a method developed in-house. A set of nine parameters was derived initially, with four of them being selected after statistical assessment. Detection of VF against non-shockable rhythms was obtained using the K-nearest neighbours classification method, with 98.6% specificity and 96.7% sensitivity. The detection accuracy remained high after inclusion of VT episodes above and below 180 bpm to shockable and non-shockable rhythms respectively and after the addition of noise. Test signals were taken from the well-known ECG signal databases of the American Heart Association and the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH-'cudb' and 'vfdb' files).
近期自动体外除颤器的发展及其应用的增加，对从体表心电图（ECG）检测心室颤动（VF）和快速室性心动过速（VT>180次/分钟）提出了非常严格的要求。我们尝试使用几种现有分析方法以及一种自主研发方法中的信息参数。最初得出了一组九个参数，经过统计评估后选择了其中四个。使用K近邻分类方法对VF与不可电击节律进行检测，特异性为98.6%，敏感性为96.7%。将180次/分钟以上和以下的VT发作分别纳入可电击和不可电击节律以及添加噪声后，检测准确率仍然很高。测试信号取自美国心脏协会以及麻省理工学院-贝斯以色列医院著名的ECG信号数据库（MIT-BIH的“cudb”和“vfdb”文件）。
