Abnormality in the heart rhythm owing to premature ventricular contraction (PVC) often causes fatal cardiac consequences. Normally, early detection of PVC uses long-term Electrocardiogram (ECG) monitoring (Holter) techniques. In recent days, Photoplethysmography (PPG) based approaches are also being adopted for PVC detection. Primarily, the lower cost and effortless acquisition of PPG makes it suitable for longterm continuous monitoring applications. However, the PPG-based PVC detection method has not been standardized yet and it is an open research problem to date. In this research, a less complicated and automated PVC detection method is proposed that uses PPG signal-based analysis only. Instead of any computationally intense time-plane features, the overall morphology of each of the PPG beats is quantified using two simple statistical parameters. Variations of these two parameters are then employed as features to identify the abrupt morphological changes caused by PVC. The proposed features are also used to identify and eliminate noisy data segments and minimize the rate of false detections. Finally, a simple threshold-based criterion is used to identify the presence of PVC beats among the normal beats. After evaluation over the PPG signal records obtained from the MIMIC dataset, the proposed method exhibits sensitivity, specificity and accuracy of 99.23%, 99.68% and 99.05%, respectively. Compared to other ECG or PPG-based methods, the methodological simplicity and the overall noteworthy outcome associated with the proposed PPG-based PVC detection technique show immense potential for implementation in personalized health monitoring applications.