On the use of hybrid neural networks and non-linear invariants for prediction of electrocardiograms

Presents the results found when exploring the ability of a particular neural network to model and predict electrocardiograms, a kind of signal believed to be mathematically chaotic. Two concepts are embedded in the design of the presented model: Lyapunov exponents and harmonic generators. The term “harmonic generator” is used to describe a 3-node, fully connected recurrent neural network trained to produce a sine wave with a specific frequency and amplitude. Harmonic generators are able to reproduce accurately sine trajectories for long periods of time without using any external inputs, Our network called the hybrid-complex neural network was able to represent some of the dynamics of the system, showing fairly good short-term prediction and some oscillation during the long-term prediction, even when the external inputs came from previous predictions of the network. These characteristics are not observed in plain feed-forward or recurrent neural networks