A Convenient Method for Learning to Classify Biomedical and Other Complex Signals based on Neuronal Boolean Complexity

This paper proposes a novel method for fast and compact recognition of biomedical and other complex signals. It is essentially based on decomposing the signal sequences to be recognized into binary strings. Then each binary string is divided into smaller strings corresponding to Boolean functions. Based on complexities for neural implementation of Boolean functions a feature vector is computed by counting the occurrences of functions with certain complexities. Using enough labeled signal sequences, a classifier can be easily trained and then used for the automatic recognition of the signals. To decrease costs, a hardware oriented Extreme Learning Machine is used. Herein, in addition to describing the method, we consider the widely used Bonn dataset for epileptic seizure detection to demonstrate that our method offers good accuracies (more than 97%) with the advantage of very low implementation costs. Given its convenience and simplicity, the proposed method can be easily integrated in low cost portable equipment, to be operated, for instance, in the context of Internet of Things.