Christov I, Bortolan G
Centre of Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria.
Physiol Meas. 2004 Oct;25(5):1281-90. doi: 10.1088/0967-3334/25/5/017.
Detection and classification of ventricular complexes from a limited number of ECG leads is of considerable importance in critical care or operating room patient monitoring. Beat-to-beat detection allows the heart rhythm evolution to be followed and various types of arrhythmia to be recognized. A quantitative analysis is proposed of pattern recognition parameters for classification of normal QRS complexes and premature ventricular contractions (PVC). Twenty-six parameters have been defined: the width of the QRS complex, three vectorcardiogram parameters and 11 from two ECG leads. These parameters include: amplitudes of positive and negative peaks, area of positive and negative waves, various time-interval durations, amplitude and angle of the QRS vector, etc. They are measured for all QRS complexes annotated as 'normals' and 'PVCs' from the 48 ECG recordings of the MIT-BIH arrhythmia database. Neural networks (NN) are shown to be a useful instrument for the analysis of large quantities of parameters. Separate ranking of any parameter and homogeneous group ranking (amplitude, area, interval, slope and vector) were performed. From the two ECG leads, the first three ranked parameter groups for clustering of PVCs are amplitude, slope and interval, while for N clustering they are vector, amplitude and area. Considering the entire parameter set, we obtained N = 99.7% correct detection of normal QRS complexes and PVC = 98.5% of premature ventricular complexes. The study also shows that simultaneous analysis of two ECG channels yields better accuracy compared to using a single channel: the improvement is 0.1% in the classification of N beats and 4.5% for PVC beats.
在重症监护或手术室患者监测中，从有限数量的心电图导联检测和分类心室复合波具有相当重要的意义。逐搏检测能够跟踪心律演变并识别各种类型的心律失常。本文提出了一种用于正常QRS复合波和室性早搏（PVC）分类的模式识别参数定量分析方法。已定义了26个参数：QRS复合波宽度、三个心电向量图参数以及来自两个心电图导联的11个参数。这些参数包括：正负峰的幅度、正负波的面积、各种时间间隔持续时间、QRS向量的幅度和角度等。它们是从麻省理工学院 - 贝斯以色列女执事医疗中心心律失常数据库的48份心电图记录中，针对所有标注为“正常”和“PVC”的QRS复合波进行测量的。神经网络（NN）被证明是分析大量参数的有用工具。对任何参数进行单独排名以及对同类参数组（幅度、面积、间隔、斜率和向量）进行排名。从两个心电图导联来看，用于PVC聚类的前三个排名参数组是幅度、斜率和间隔，而用于正常聚类的是向量、幅度和面积。考虑整个参数集，我们对正常QRS复合波的正确检测率为N = 99.7%，对室性早搏复合波的检测率为PVC = 98.5%。该研究还表明，与使用单个通道相比，同时分析两个心电图通道可提高准确性：在正常搏动分类中提高了0.1%，在PVC搏动分类中提高了4.5%。
