Lund K, Christiansen E H, Lund B, Pedersen A K
Department of Cardiology, Skejby University Hospital, Aarhus, Denmark.
Med Biol Eng Comput. 1998 Jul;36(4):438-44. doi: 10.1007/BF02523211.
There is a growing interest in the analysis of beat-to-beat variations of the morphology (BBM) of cardiac waves in electrocardiograms (ECG). Such analyses are confronted with the low BBM-to-noise ratio. An ECG clustering technique is introduced that brings the benefits of signal averaging to BBM analysis and recovers the beat-to-beat pattern of BBM. ECG clustering aligns waves and sorts them into clusters. The precision of the alignment was enhanced by sub-sample alignment. Kohonen's self-organising neural networks identified the clusters of the cardiac waves during training. The subsequent clustering of a wave results in a label for the closest cluster, a distance to the cluster and optimal alignment. Furthermore, ECG clustering avoids base-line variations and amplitude modulation sufficiently to be applied to the QRS wave in the raw ECG. The technique is demonstrated on 14 subjects with coronary heart disease and no myocardial infarction, myocardial infarction, or inducible ventricular tachycardia. ECG clustering is a general-purpose technique for beat-to-beat analysis, where the variations are cyclic as in the sinus rhythm. Results show that beat-to-beat variations in the QRS morphology are in general cyclic, with a main period of about four cardiac cycles. All calculations were performed with the Cardio software.
人们对心电图（ECG）中心脏波形态的逐搏变化（BBM）分析的兴趣日益浓厚。此类分析面临着较低的BBM与噪声比。本文介绍了一种ECG聚类技术，该技术将信号平均的优势引入BBM分析，并恢复了BBM的逐搏模式。ECG聚类对波形进行对齐并将它们分类到不同的簇中。通过子样本对齐提高了对齐的精度。在训练过程中，Kohonen自组织神经网络识别出心脏波的簇。随后对一个波形进行聚类会得到最接近簇的标签、到该簇的距离以及最佳对齐。此外，ECG聚类能够充分避免基线变化和幅度调制，从而可应用于原始ECG中的QRS波。该技术在14名患有冠心病但无心肌梗死、心肌梗死或诱发性室性心动过速的受试者身上得到了验证。ECG聚类是一种用于逐搏分析的通用技术，其中变化是周期性的，如在窦性心律中。结果表明，QRS形态的逐搏变化总体上是周期性的，主要周期约为四个心动周期。所有计算均使用Cardio软件进行。
