Integer sub-optimal Karhunen-Loeve Transform for multi-channel lossless EEG compression

In this paper, a method for approximating the Karhunen-Loeve Transform (KLT) for the purpose of multi-channel lossless electroencephalogram (EEG) compression is proposed. The approximation yields a near optimal transform for the case of Markov process, but significantly reduces the computational complexity. The sub-optimal KLT is further parameterized by a ladder factorization, rendering a reversible structure under quantization of coefficients called IntSKLT. The IntSKLT is then used to compress the multi-channel EEG signals. Lossless coding results show that the degradation in compression ratio using the IntSKLT is about 3% when the computational complexity is reduced by more than 60%.