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

Based on the given feature description, the feature "n_r_ecg_p_06" represents the persistent form of atrial fibrillation on the electrocardiogram (ECG) at the time of admission to the hospital. It is a categorical variable with two possible categories: "no" and "yes".

To determine if the patient has chronic heart failure, we need to analyze the relationship between the "n_r_ecg_p_06" feature and the myocardial infarction complications data.

In this case, we can see that the feature categories are directly related to the target class "chronic heart failure". If the "n_r_ecg_p_06" value is "no", it indicates the absence of persistent atrial fibrillation on the ECG. On the other hand, if the value is "yes", it indicates the presence of persistent atrial fibrillation on the ECG.

Therefore, we can create a dictionary with the feature values for each target class as follows:
- For the target class "no", the possible values of the "n_r_ecg_p_06" feature are ["no"].
- For the target class "yes", the possible values of the "n_r_ecg_p_06" feature are ["yes"].

Note that in this case, there are no values of "n_r_ecg_p_06" that are hard to predict or unknown.