Based on my prior knowledge, I can analyze the relationship between the feature "n_p_ecg_p_12" and the task of determining the presence of chronic heart failure in a patient with myocardial infarction complications.

To analyze this relationship, I would look at the distribution of the values of "n_p_ecg_p_12" for both the "yes" and "no" target classes. If there is a significant difference in the distribution of values between the two target classes, it might indicate a relationship between the feature and the presence of chronic heart failure.

To create the dictionary, I will categorize the values of "n_p_ecg_p_12" based on the target class and store them in lists. If any category has no values for a particular target class, it will be excluded from the dictionary.

Here is the dictionary with the possible values of "n_p_ecg_p_12" for the target classes:

```json
{
	"no": ["no"],  
	"yes": ["yes"]
}
```

In this case, the feature "n_p_ecg_p_12" only has two possible values ('no' and 'yes'), and both of these values are present for the target classes "no" and "yes" respectively.