Based on the given feature description and task, we need to analyze the relationship between the feature "n_r_ecg_p_03" and the presence of chronic heart failure in the patient.

To do this, we can examine the distribution of the "n_r_ecg_p_03" values based on the target variable. Specifically, we need to compare the possible values of "n_r_ecg_p_03" for the target class "no" (no chronic heart failure) and the target class "yes" (chronic heart failure).

Here is the dictionary containing the possible values of "n_r_ecg_p_03" for each target class:

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

Explanation:
- For patients without chronic heart failure ("no" class), the only possible value of "n_r_ecg_p_03" is "no".
- For patients with chronic heart failure ("yes" class), the possible values of "n_r_ecg_p_03" are both "no" and "yes".

Note: It is important to mention that this analysis is based on prior knowledge and assumptions made based on the feature description. The actual relationship between the feature and target can only be confirmed by analyzing the appropriate dataset.