Based on prior knowledge, we can analyze the relationship between the feature "n_r_ecg_p_05" (Paroxysms of atrial fibrillation on ECG at the time of admission to the hospital) and the presence of chronic heart failure (target variable) in patients with myocardial infarction complications.

To perform this analysis, we would need a dataset that includes both the feature and the target variable for a set of patients. However, based on the given feature description, we can infer the following possibilities:

If the value of "n_r_ecg_p_05" is "no", it indicates that there were no paroxysms of atrial fibrillation on ECG at the time of admission to the hospital.

If the value of "n_r_ecg_p_05" is "yes", it indicates that there were paroxysms of atrial fibrillation on ECG at the time of admission to the hospital.

To create the dictionary with the possible values of "n_r_ecg_p_05" for each target class (yes or no), we can use the following format:

```json
{
	"no": ["no"],
	"yes": ["yes"]
}
```
Since the "n_r_ecg_p_05" feature is a categorical variable with two categories ('no' and 'yes'), we include only those values as strings in the respective lists for each target class.