Artificial Neural Networks
Artificial neural networks (ANNs) are computational predictive analytical systems inspired by the human nervous system. These consist of a high number of computational nodes (called neurons) that spread across various layers. In simplest terms, it is comprised of three basic layers with distinct functions.
  1. An input layer, in which data (usually multidimensional vector) is ingested and distributed to the hidden layer.
  2. A hidden layer, which makes decisions to assign random weights within each node to determine if it detriments or improves the output (referred to as learning). When multiple hidden layers are stacked together to perform a complex pattern recognition task, it is referred to as DL. There are two fundamental learning approaches, supervised and unsupervised learning. Supervised learning is most often used for image-based pattern recognition tasks. In this form, for every training set (e.g., a single digital ECG), input vectors are associated to one or more pre-assigned labels 13. In contrast unsupervised learning does not have pre-selected labels, but rather seeks to find clusters of salient features from the data itself.
  3. An output layer where, in the case of supervised learning, a system output is compared to the preset label output and backpropagated to the previous layers with the goal of reducing classification error as much as possible by tuning the weights.
Convolutional neural networks (CNN) are similar to traditional ANN, but are particularly well suited for image recognition tasks, and thus reduce the parameters required to set up the model. The capability of these models to analyze subtle details from abstract data is remarkable and far superior to humans. More details on these models are described elsewhere.14