Early non-invasive diagnosis of Ischemic Heart Disease (IHD) can often be challenging. HRV-based biomarkers have a potentially important role in risk stratification for individuals with suspected heart disease. However, there is no consensus on the HRV preprocessing steps, particularly on how to properly treat ectopic beats. We aimed to investigate the performance of the models for classification of early IHD versus healthy subjects (HC) based on HRV features extracted from the signals excluding ectopic beats and based on the same features extracted from the signals that contain both ectopic and normal heartbeats. This study encompassed 385 subjects (170 IHD and 215 HC). The models were produced by logistic regression method considering two sets of HRV features. In the first set the features were extracted from the HRV signals containing both normal and ectopic beats, while in the second set the features were extracted after excluding ectopic beats. The classification performance between two models were compared, as well as the values of the features extracted with two preprocessing approaches. The results showed that the model with the input features from RR segments with normal and ectopic beats presented a higher classification accuracy (72.7%) than the model based on features extracted only from normal heart beats (67.8%). The produced nomograms revealed that the most important features were SD2, SDNN, LF, HF, meanRR, LF/HF and SD1. In addition, the evaluation of the feature importance by analysis of produced nomograms and observed significant differences between features extracted with two preprocessing approaches, showed also that the exclusion of the ectopic beats modifies the features' discriminatory power between HC and IHD. In conclusion, our results showed that inclusion of ectopic beats might bring more discriminatory power and help to better identify between early-stage IHD and healthy individuals.