Make sure you extract features from both time and frequency domain. In frequency domain, use windowing to extract features at different frequency bands. This might help in further reducing false positives.

Perform PCA to your data and look at the scatter for PC1 and PC2. You should see some groups in the scatter. If you see one big scatter then, you might have to consider collecting more data.

also;

Did you try to include into training period of your classifier those false?

It will be deciding how to change the sensitivity and specificity of your classifier after including noise or background dataset.

Thank you Seyed Yahya Moradi for your valuable reply.

Adding to Seyed Yahya Moradi answer you can reduce false minima by using probabilistic update as well.

Thank you Rehan for your answer.