An adaptive backpropagation neural network achieved an average ischemia episode detection sensitivity of 88.62% and an ischemia duration sensitivity of 72.22% using the European ST-T database.
Does an adaptive backpropagation neural network accurately and rapidly detect ischemic episodes in ECG data?
An adaptive backpropagation neural network can provide fast and reliable automated detection of ischemic episodes from ECG data, making it suitable for real-time monitoring in critical care units.
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).
Maglaveras et al. (Wed,) conducted a other in Ischemic episodes. Adaptive backpropagation neural network algorithm vs. Classical backpropagation algorithm was evaluated on Ischemia episode detection sensitivity and ischemia duration sensitivity. An adaptive backpropagation neural network achieved an average ischemia episode detection sensitivity of 88.62% and an ischemia duration sensitivity of 72.22% using the European ST-T database.