PCG is one of the most widely used physiological signals for cardiac monitoring due to its simplicity. PCG segmentation involves identifying or classifying S1 and S2 sounds that correspond to systolic and diastolic periods respectively. There are many PCG segmentation algorithms that can identify the start and end time instants of S1 and S2 sounds. However, most of the algorithms distinguish S1 and S2 sounds based on the time duration threshold, which may fail for abnormal patients. In order to solve this ambiguity, multi-channel approaches are used. In this work, a hardware for simultaneous acquisition of PCG and PPG (from the fingertip of the subject) is developed. Then by using the proposed low complex PCG and PPG processing algorithms, the correlation of occurrence of S2 event of PCG with the systolic peak point and foot point of PPG is analyzed. The correlation parameters of PCG and PPG are reported and validated with the existing multi-channel approaches. The simulation results performed on real-time database demonstrates that the proposed S2 sound detection method achieved the sensitivity of 98.23%, positive predictivity of 99.64%, and the accuracy of 94.50%. The detection error rate of proposed S2 sound detection algorithm is 7.07%. © 2017 IEEE.