Lewenstein K
Institute of Precision and Biomedical Engineering, University of Technology, Warsaw.
Med Biol Eng Comput. 2001 May;39(3):362-7. doi: 10.1007/BF02345292.
The purpose of the paper is the evaluation of a radial basis function neural network as a tool for computer aided coronary artery disease diagnosis based on the results of the traditional ECG exercise test. The research was performed using 776 data records from an exercise test (297 records from healthy patients and 479 from ill patients) confirmed by coronary arteriography results. Each record described the state of the patient, provided input data for the neural network, included the level and slope of an ST segment of a 12-lead ECG signal made at rest and after effort, heart rate, blood pressure, load during the test, and occurrence of coronary pain, coronary arteriography, correct output pattern for the neural network, and verified the existence (or not) of more than 50% stenosis of the particular coronary vessels. Radial basis function neural networks for coronary artery disease diagnosis were optimised by choosing the type of radial function, the method of training (setting the number of centres and their dimensions), and regularisation. The best network correctly recognised over 97% of cases from a 400-element test set, diagnosing not only the patients' condition (simple 'healthy/unhealthy' diagnosis), but also pointing out individual unhealthy/stenosed vessels.
本文的目的是基于传统心电图运动试验的结果，评估径向基函数神经网络作为计算机辅助冠状动脉疾病诊断工具的性能。该研究使用了776份运动试验数据记录（297份来自健康患者，479份来自患病患者），这些记录均经冠状动脉造影结果证实。每份记录描述了患者的状态，为神经网络提供输入数据，包括静息和运动后12导联心电图信号的ST段水平和斜率、心率、血压、试验期间的负荷以及是否出现冠状疼痛、冠状动脉造影、神经网络的正确输出模式，并验证特定冠状动脉血管是否存在超过50%的狭窄。通过选择径向函数类型、训练方法（设置中心数量及其维度）和正则化，对用于冠状动脉疾病诊断的径向基函数神经网络进行了优化。最佳网络正确识别了来自400个元素测试集的97%以上的病例，不仅诊断出患者的病情（简单的“健康/不健康”诊断），还指出了个别不健康/狭窄的血管。
