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The HMM methods are the central methodology or stage in the SSA Protocol, particularly in the gene finders, and sometimes with the CCC protocol or implementation, in that the other stages can be dropped or merged with the HMM stage in many incarnations. For example, in some CCC analysis situations the tFSA methods could be totally eliminated in favor of the more accurate (but time consuming) HMM‐based approaches to the problem, with signal states defined or explored in more or less the same setting, but with the optimized Viterbi path solution taken as the basis for the signal acquisition.

The HMM features, and other features (from NN, wavelet, or spike profiling, etc.) can be fused and selected via use of various data fusion methods, such as a modified Adaboost selection (from [1, 3], and ssss1). The HMM‐based feature extraction provides a well‐focused set of “eyes” on the data, no matter what its nature, according to the underpinnings of its Bayesian statistical representation. The key is that the HMM not be too limiting in its state definition, while there is the typical engineering trade‐off on the choice of number of states, N, which impacts the order of computation via a quadratic factor of N in the various dynamic programming calculations (comprising the Viterbi and Baum–Welch algorithms among others).