Liszka-Hackzell J J
Orebro Medical Center Hospital, Department of Anesthesiology, Sweden.
J Med Syst. 2001 Aug;25(4):269-76. doi: 10.1023/a:1010779205000.
Digitized data from CTG (cardiotocography) measurements (fetal heart rate and uterine contractions) have been used for categorization of typical heart rate patterns before and during delivery. Short time series of CTG data, about 7 min duration, have been used in the categorization process. In the first part of the study, selected CTG data corresponding to 10 typical cases were used for purely auto associative unsupervised training of a Self-Organizing Map Neural Network (SOM). The network may then be used for objective categorization of CTG patterns through the map coordinates produced by the network. The SOM coordinates were then compared. In the second part of the study, a hybrid neural network consisting of a SOM network and a Back-Propagation network (BP) was trained with data corresponding to a number of basic heart rate patterns as described by eight manually selected indices. Test data (different than the training data) were then used to check the performance of the network. The present study shows that the categorization process, in which neural networks were used, can be reliable and agree well with the manual categorization. Since the categorization by neural networks is very fast and does not involve human efforts, it may be useful in patient monitoring.
来自CTG（胎心监护）测量（胎儿心率和子宫收缩）的数字化数据已被用于分娩前后典型心率模式的分类。在分类过程中使用了约7分钟时长的CTG数据短时间序列。在研究的第一部分，对应10个典型病例的选定CTG数据被用于自组织映射神经网络（SOM）的纯自联想无监督训练。然后，该网络可通过网络产生的映射坐标用于CTG模式的客观分类。接着对SOM坐标进行比较。在研究的第二部分，由一个SOM网络和一个反向传播网络（BP）组成的混合神经网络使用与八个手动选定指标所描述的一些基本心率模式相对应的数据进行训练。然后使用测试数据（与训练数据不同）来检查网络的性能。本研究表明，使用神经网络的分类过程可以是可靠的，并且与人工分类结果吻合良好。由于神经网络分类非常快速且不涉及人工操作，它可能在患者监测中有用。
