Читать книгу Smart Healthcare System Design - Группа авторов - Страница 31

The most main role in creating an EEG signal classification system is generating mathematical representations and reductions of the input data which allow the input signal to be properly differentiated into its respective classes. These mathematical representations of the signal are, in a sense, a mapping of a multidimensional space (the input signal) into a space of fewer dimensions. This dimensional reduction is known as “feature extraction”. Ultimately, the extracted feature set should preserve only the most important information from the original signal [23].

Table 1.3 EEG signal mathematical transform with feature.

Set	Mathematical transform	Feature number
1	Linear predictive codes taps	1–5
2	Fast Fourier transform statics	6–12
3	Mel frequency cepstral coefficients	13–22
4	Log (FFT) analysis	23–28
5	Phase shift correlation	29–36
6	Hilbert transform statics	37–44
7	Wavelet decomposition	45–55
8	1st, 2nd, 3rd derivatives	56–62
9	1st, 2nd, 3rd derivatives	63–67
10	Auto regressive parameters	68–72

Table 1.3 above describes feature classification for EEG signal. First, a feature set optimization algorithm is presented which is used to do a feature set study to reveal the mathematical transforms that are most useful in predicting the preictal state. After this, a set of algorithms are given that became the framework of the seizure on set prediction system described.