The Holter monitor captures the electrocardiogram (ECG) and detects abnormal episodes, but physicians still use manual cross-checking. It takes a considerable time to annotate a long-term ECG record. As a result, research continues to be conducted to produce an effective automatic cardiac episode detection technique that will reduce the manual burden. The current study presents a signal processing framework to detect ventricular ectopic beat (VEB) episodes in long-term ECG signals of cross-database. The proposed study has experimented with the cross-database of open-source and proprietary databases. The ECG signals were preprocessed and extracted the features such as pre-RR interval, post-RR interval, QRS complex duration, QR slope, and RS slope from each beat. In the proposed work, four models such as support vector machine, k-means nearest neighbor, nearest mean classifier, and nearest RMS (NRMS) classifiers were used to classify the data into normal and VEB episodes. Further, the trained models were used to predict the VEB episodes from the proprietary database. NRMS has reported better performance among four classification models. NRMS has shown the classification accuracy of 98.68% and F1-score of 94.12%, recall rate of 100%, specificity of 98.53%, and precision of 88.89% with an open-source database. In addition, it showed an accuracy of 99.97%, F1-score of 94.54%, recall rate of 98.62%, specificity of 99.98%, and precision of 90.79% to detect the VEB cardiac episodes from the proprietary database. Therefore, it is concluded that the proposed framework can be used in the automatic diagnosis system to detect VEB cardiac episodes. [ABSTRACT FROM AUTHOR]