Biosignal based analysis of various psychosomatic events like stress, pain, etc. becomes popular among researchers nowadays because of its salient independent nature. Application of biosignals like Electrocardiogram (ECG), Photoplethysmogram (PPG), Electrodermal activities (EDA), Electroencephalogram (EEG) etc. are being investigated for the purpose using different classical modern machine learning tools. Wearable sensors for such biosignals followed by AI enabled processors for detection or classification of various physiological events leads to real-time continuous monitoring of abnormalities. In this regard PPG becomes very prospective for its easy acquisition protocol. However PPG suffers a critical issue due to its noise prone nature resulting from movement and electrode contact noise. Thus signal quality is important for any PPG based analysis. The present work proposes a simple signal quality assessment (SQA) method based on its peak-to-peak interval (PPI). SQA basically estimates some Heart Rate (HR) features and compares them with hard thresholds. The section of PPG strip is disregarded for further analysis if the HR features are beyond the predefined values. The algorithm is tested on publicly available CLAS database. It is noticed that the SQA algorithm enhances the common statistical features of PPG as compared with benchmark ECG signal.