MichaelMMeskhi:

Say my input is 290 features and output is 1 value.


Sounds more like a pooling layer or linear to me.
The defining feature of conv layers is shift invariance, but so when you only have 1 output, it is hard to intuit about shift invariance.
That said, you could look into using the usual conv layer + pooling approach, just avoid doing softmax / sigmoid / … at the end if you want regression.
I would probably look at scaling weights such that activations so are in a sane range using trial and error.
Best regards
Thomas