Machine learning - curve categorisation I have curves of the following structure (it is the blue one I am interested in) 
These curves reflect the volume of blood (actually gamma ray counts) in the left ventricle as a function of time (frame number). Data point values for the given curve is attached.
In the analysis of these curves (qualitative) an experienced reporter will grade the first segment (emptying) of the curve (from frame 1 to minimum) as normal, slightly delayed, delay or markedly delayed. This is based on impression of time to minimum and looking at the gradient (i.e. very arbitrary). The second (filling) portion of the curve (minimum to end frame) is also graded using the same scoring system (1-4).
Is it possible to use machine learning in this situation to train the computer to categorise the emptying and filling parts of the curve. i.e. could I use a training set of curves with known emptying and filling scores and then apply this 'knowledge' to new curves. I would like to see how well a machine and experienced reporter agree. I predict not very well as I feel the reporting system is quite arbitrary.
Absolute counts in each frame might vary by an order of magnitude. It will be the gradient and relative time to minima which will affect scoring
Is machine learning a sensible way to tackle this? How do I organise the data and tackle the analysis?
I would use R for this
Frame,Count,Curve
1,9083.06,ED_TAC
2,8998.62,ED_TAC
3,8750.27,ED_TAC
4,8310.03,ED_TAC
5,7721.91,ED_TAC
6,7055.92,ED_TAC
7,6409.06,ED_TAC
8,5864.33,ED_TAC
9,5444.69,ED_TAC
10,5181.14,ED_TAC
11,5150.63,ED_TAC
12,5331.14,ED_TAC
13,5630.62,ED_TAC
14,6085.05,ED_TAC
15,6677.4,ED_TAC
16,7312.65,ED_TAC
17,7820.77,ED_TAC
18,8111.76,ED_TAC
19,8245.61,ED_TAC
20,8348.35,ED_TAC
21,8484.98,ED_TAC
22,8678.54,ED_TAC
23,8800.05,ED_TAC
24,8752.54,ED_TAC

 A: I see no reasons why machine learning wouldn't be applicable in this setting. The main issue here is to define the right set of features to use, what usually requires a good degree of familiarity with the problem and some expert knowledge.
Just reading your explanation was not enough to make me understand how to use the "gradient and relative time to minima" to perform classification,  but clearly your features will be something derived from them. Some random thoughts which occur me:


*

*Rotate the curves to put them over the x-axis and possibly normalize your data (if you rescaled a curve, would you still be able to classify it?). 

*Use polynomial regression to fit the curves (visually it seems fitting a Gaussian would be well enough).

*Use the coefficients of you fitted Gaussian as features.

*Use the 1st and 2nd derivatives of your Gaussian as features.


In summary, the answer for your question is "yes", ML will probably tackle your problem. Though your have to spend some time in the data preparation and feature engineering phases to achieve a good result. Good luck and please tell us if you succeed.
