Classification of Low-Amplitude ECG Components Using Adaptive Activation Functions of Neural Networks

A promising direction in the development of neural networks for the analysis and classification of biomedical signals is the use of trainable activation functions, known as AAFs (Adaptive Activation Functions). The use of such functions enables heterogeneous data to be adapted, thereby improving classification accuracy. This paper considers the application of AAFs for the classification of low-amplitude components of electrocardiogram (ECG), specifically ventricular late potentials (VLP) and atrial late potentials (ALP), which are important for the early detection of cardiac tachyarrhythmias. To evaluate the impact of AAF on the quality of VLP and ALP detection, two fully connected neural networks with different numbers of hidden layers were developed. The study established that using AAF increases the accuracy of VLP and ALP classification and the speed of neural network model training compared to non-adaptive activation functions. To minimize the problems of “vanishing” or “exploding” gradients in the loss function, as well as the effects of “dead” neurons that arise during neural network training, a new activation function has been developed that normalizes weight coefficients, preventing excessively high or low gradients. Using the developed activation function increases the speed and stability of neural network training. It improves the recognition accuracy of low-amplitude ECG components compared to other activation functions. Using the developed AAF, the highest classification accuracy was obtained for VLP (94.7%) and ALP (91.4%). To simultaneously analyze a large number of activation functions, a coefficient was developed to assess the redundancy of network layers. The proposed coefficient for detecting “bottlenecks” in neural network architectures significantly simplifies the analysis and improvement of neural networks.