Based on the given feature "n_r_ecg_p_04" - Frequent premature ventricular contractions on ECG at the time of admission to the hospital, we need to analyze its relationship with the target variable "chronic heart failure" to generate the desired dictionary.

To conduct the analysis, we can start by examining if there is any pattern or trend in the occurrences of chronic heart failure according to different values of "n_r_ecg_p_04". We can do this by examining the distribution of "n_r_ecg_p_04" values for each target class ("yes" and "no").

Here is the analysis result and the generated dictionary:

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

Explanation:
- For the target class "no" (chronic heart failure not present), the feature "n_r_ecg_p_04" has two possible values: "no" and "yes". This indicates that patients without chronic heart failure had both "no" and "yes" values for frequent premature ventricular contractions on their ECG at the time of admission to the hospital.
- For the target class "yes" (chronic heart failure present), the feature "n_r_ecg_p_04" also has two possible values: "no" and "yes". This suggests that patients with chronic heart failure had both "no" and "yes" values for frequent premature ventricular contractions on their ECG at the time of admission to the hospital.

Note: Since all possible values of "n_r_ecg_p_04" are included in the dictionary for both target classes, the lists for each target class are not empty.