Yang T F, Devine B, Macfarlane P W
University of Glasgow, Department of Medical Cardiology, Royal Infirmary, Scotland.
J Electrocardiol. 1993;26 Suppl:90-4.
An investigation into the use of software-based neural networks for the detection of atrial fibrillation was made. At a specific point in the Glasgow 12-lead electrocardiographic interpretation program, a decision has to be made as to whether atrial fibrillation or sinus rhythm with supraventricular or ventricular extrasystoles is present. The same input parameters used for the deterministic logic at that point were also utilized to train a variety of neural networks. Results from a separate test set showed that the sensitivity of detecting atrial fibrillation could be improved using the best of the neural networks. On the other hand, it was felt that the original deterministic logic could be improved by considering adjustments in order that the presence of certain combinations of findings not previously regarded as representing atrial fibrillation would now do so. When the deterministic logic was upgraded in this way, it was found, again using a separate test set, that the revised logic was improved compared to the original, and also gave a performance similar to that of the neural network. It is concluded that the use of a neural network at a specific diagnostic decision point in a rhythm analysis program can be as effective as deterministic logic, which may take several years to perfect.
开展了一项关于使用基于软件的神经网络检测心房颤动的研究。在格拉斯哥12导联心电图解读程序中的特定点，必须做出判断，即是否存在心房颤动或伴有室上性或室性期前收缩的窦性心律。在该点用于确定性逻辑的相同输入参数也被用于训练各种神经网络。来自单独测试集的结果表明，使用最佳神经网络可以提高检测心房颤动的灵敏度。另一方面，人们认为可以通过考虑调整来改进原始的确定性逻辑，以便某些以前不被视为代表心房颤动的发现组合现在可以代表心房颤动。当以这种方式升级确定性逻辑时，再次使用单独的测试集发现，修订后的逻辑与原始逻辑相比有所改进，并且性能与神经网络相似。得出的结论是，在节律分析程序中的特定诊断决策点使用神经网络可以与确定性逻辑一样有效，而确定性逻辑可能需要数年时间才能完善。
