Maglaveras N, Stamkopoulos T, Pappas C, Strintzis M G
Lab of Medical Informatics, Medical School, Aristotelian University, Thessaloniki, Macedonia, Greece.
IEEE Trans Biomed Eng. 1998 Jul;45(7):805-13. doi: 10.1109/10.686788.
A supervised neural network (NN)-based algorithm was used for automated detection of ischemic episodes resulting from ST segment elevation or depression. The performance of the method was measured using the European ST-T database. In particular, the performance was measured in terms of beat-by-beat ischemia detection and in terms of the detection of ischemic episodes. The algorithm used to train the NN was an adaptive backpropagation (BP) algorithm. This algorithm drastically reduces training time (tenfold decrease in our case) when compared to the classical BP algorithm. The recall phase of the NN is then extremely fast, a fact that makes it appropriate for real-time detection of ischemic episodes. The resulting NN is capable of detecting ischemia independent of the lead used. It was found that the average ischemia episode detection sensitivity is 88.62% while the ischemia duration sensitivity is 72.22%. The results show that NN can be used in electrocardiogram (ECG) processing in cases where fast and reliable detection of ischemic episodes is desired as in the case of critical care units (CCU's).
一种基于监督神经网络（NN）的算法被用于自动检测由ST段抬高或压低导致的缺血发作。该方法的性能通过欧洲ST-T数据库进行评估。具体而言，性能评估包括逐搏缺血检测以及缺血发作检测。用于训练神经网络的算法是自适应反向传播（BP）算法。与经典BP算法相比，该算法大幅减少了训练时间（在我们的案例中减少了十倍）。神经网络的召回阶段极其快速，这一事实使其适用于缺血发作的实时检测。所得的神经网络能够独立于所使用的导联检测缺血情况。结果发现，缺血发作检测的平均灵敏度为88.62%，而缺血持续时间灵敏度为72.22%。结果表明，在诸如重症监护病房（CCU）等需要快速可靠检测缺血发作的情况下，神经网络可用于心电图（ECG）处理。
