How to extract features of individual spikes in non-negative signal? I have data of non-negative (in the sense there's no signal below baseline) spiking waveforms, which are in the form of a 1D array of numbers:



Spikes that cross some threshold are considered real signal events and I want to be able to measure the properties of individual events such as their amplitude, rise-time, decay-time, duration, and half-width. 

The signals are pretty clean so I imagine I don't need some fancy machine learning algorithm. Would a wavelet transform approach give me the results I need? If not, what's the best and simplest approach here?
Continuous wavelet transforms can provide information and parameters for sparse, piece-wise regular signals. One example related to yours is present in The Continuous Wavelet Transform in MRS, A. Suvichakorn, C. Lemke, A.  Schuck, J.-P. Antoine, as exemplified in the following picture.