Based on prior knowledge, we can analyze the relationship between the feature "n_r_ecg_p_06" and the task of determining if the patient has chronic heart failure.

Given that the feature represents the presence or absence of persistent form of atrial fibrillation on ECG at the time of admission to the hospital, we can make the following assumptions:

1. If the value of "n_r_ecg_p_06" is "no", it indicates that the patient does not have persistent form of atrial fibrillation on ECG at the time of admission to the hospital.
2. If the value of "n_r_ecg_p_06" is "yes", it indicates that the patient has persistent form of atrial fibrillation on ECG at the time of admission to the hospital.

To create the dictionary, we need to consider the possible values of feature "n_r_ecg_p_06" for each target class.

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

In this case, since the feature is categorical and we are only concerned with whether or not the patient has chronic heart failure, we only include the specific values of "n_r_ecg_p_06" that correspond to each target class.