You will find several questions and answers about negentropy if you search Matlab Answers for negentropy.
Negentropy is not the reverse of entropy. Suppose your signal is y. The negentropy of the signal, J(y), is the entropy of a Gaussian noise signal with the same mean and variance as y, minus the entropy of y.
where 
= entropy of y. A Gaussian signal has maximum possible entropy, so J(y) will be non-negative. The bigger J(y) is, the more non-Gaussian y is. You said you already have code to measure the entropy of your signal. Make a Gaussian signal, 
, with the same mean and variance as y, and use your code to measure its entropy, and the entropy of your signal (y), and compute J(y). Here's how to make a Gaussian signal with the same mean and variance as y:
= entropy of y. A Gaussian signal has maximum possible entropy, so J(y) will be non-negative. The bigger J(y) is, the more non-Gaussian y is. You said you already have code to measure the entropy of your signal. Make a Gaussian signal, , with the same mean and variance as y, and use your code to measure its entropy, and the entropy of your signal (y), and compute J(y). Here's how to make a Gaussian signal with the same mean and variance as y:yGauss=randn(size(y))*std(y)+mean(y);
Good luck.
