Gomes Pedro R, Soares Filomena O, Correia J H, Lima C S
Faculty of Engineering and Technologies of University Lusiada, Largo Tinoco de Sousa, 4760-108 V. N. Famalicao Portugal.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4670-3. doi: 10.1109/IEMBS.2010.5626456.
This article is concerned with the classification of ECG pulses by using state of the art Continuous Density Hidden Markov Models (CDHMM's). The ECG signal is simultaneously observed at three different level of focus by means of the Wavelet Transform (WT). The types of beat being selected are normal (N), premature ventricular contraction (V) which is often precursor of ventricular arrhythmia, two of the most common class of supra-ventricular arrhythmia (S), named atrial fibrillation (AF), atrial flutter (AFL), and normal rhythm (N). Both MLII and V1 derivations are used. Run time classification errors can be detected at the decoding stage if the classification of each derivation is different. These pulses are selected for a posterior physician analysis. Experimental results were obtained in real data from MIT-BIH Arrhythmia Database and also in data acquired from a developed low-cost Data-Acquisition System.
本文关注的是利用先进的连续密度隐马尔可夫模型（CDHMM）对心电图脉冲进行分类。通过小波变换（WT）在三个不同聚焦水平上同时观察心电图信号。所选择的心跳类型包括正常（N）、室性早搏（V，通常是室性心律失常的先兆）、两种最常见的室上性心律失常类型（S），即心房颤动（AF）、心房扑动（AFL）以及正常心律（N）。同时使用MLII和V1导联。如果每个导联的分类不同，在解码阶段可以检测到运行时分类错误。这些脉冲被选出来供医生进行后续分析。实验结果是通过麻省理工学院 - 贝斯以色列女执事医疗中心心律失常数据库的真实数据以及从一个开发的低成本数据采集系统获取的数据得到的。
