A processor-implemented method for determining a quality of a photoplethysmogram, PPG, signal, the method comprising receiving a plurality of PPG signals from a device, via one or more hardware processors, wherein the plurality of PPG signals are associated with a plurality of one or more subjects; filtering the plurality PPG signals to obtain a plurality of filtered PPG signals, via the one or more hardware processors, wherein the plurality of PPG signals are filtered using a filtering technique; extracting a plurality of heart frequency components and a plurality of breathing frequency components, via the one or more hardware processors, from the plurality of PPG signals and the plurality of filtered PPG signals, based on an extraction technique; determining a plurality of metrices via the one or more hardware processors, using the plurality of heart frequency components and the plurality of breathing frequency components for a pre-defined time interval, wherein the plurality of metrices comprise a mean heart component ( H m), a mean breathing component ( B m), a change in the heart component ( P 1), and a change in the breathing component ( P 2) and wherein the H m and the B m is an average value of the plurality of heart frequency component and the plurality of breathing frequency component for the plurality of PPG signals and the plurality of filtered PPG signals, and the P 1 and the P 2 is the percentage change in the plurality of heart frequency component and plurality of breathing frequency component for the PPG signals and the plurality of filtered PPG signals;estimating a plurality of optimum thresholds, via the one or more hardware processors, using the plurality of metrices based on a differential evolutionary optimization technique, wherein the differential evolutionary optimization technique includes estimating the plurality of optimal thresholds ( ω 1, ω 2, ω 3, ω 4) based on a pre-defined performance parameter F-Score of classifying training samples into good and bad PPG classes, the training data containing PPG segments annotated as either good or bad, and the differential evolutionary optimization technique expressed as f ω 1 ω 2 ω 3 ω 4 = min ω 1, ω 2, ω 3, ω 4 1 − F subject to 0 ≤ ω 1 ω 2 ω 3 ω 4 ≤ 1, wherein, ω 1 is optimum threshold for H m, ω 2 is optimum threshold for B m, ω 3 is optimum threshold for P 1, and ω 4 is optimum threshold for P 2; anddetermining quality of an input PPG signal at real time via one or more hardware processors, wherein the quality of the plurality of input PPG signal is determined as one of bad signal and a good signal based on a PPG signal classification technique using the plurality of optimum thresholds. receiving a plurality of PPG signals from a device, via one or more hardware processors, wherein the plurality of PPG signals are associated with a plurality of one or more subjects; filtering the plurality PPG signals to obtain a plurality of filtered PPG signals, via the one or more hardware processors, wherein the plurality of PPG signals are filtered using a filtering technique; extracting a plurality of heart frequency components and a plurality of breathing frequency components, via the one or more hardware processors, from the plurality of PPG signals and the plurality of filtered PPG signals, based on an extraction technique; determining a plurality of metrices via the one or more hardware processors, using the plurality of heart frequency components and the plurality of breathing frequency components for a pre-defined time interval, wherein the plurality of metrices comprise a mean heart component ( H m), a mean breathing component ( B m), a change in the heart component ( P 1), and a change in the breathing component ( P 2) and wherein the H m and the B m is an average value of the plurality of heart frequency component and the plurality of breathing frequency component for the plurality of PPG signals and the plurality of filtered PPG signals, and the P 1 and the P 2 is the percentage change in the plurality of heart frequency component and plurality of breathing frequency component for the PPG signals and the plurality of filtered PPG signals;estimating a plurality of optimum thresholds, via the one or more hardware processors, using the plurality of metrices based on a differential evolutionary optimization technique, wherein the differential evolutionary optimization technique includes estimating the plurality of optimal thresholds ( ω 1, ω 2, ω 3, ω 4) based on a pre-defined performance parameter F-Score of classifying training samples into good and bad PPG classes, the training data containing PPG segments annotated as either good or bad, and the differential evolutionary optimization technique expressed as f ω 1 ω 2 ω 3 ω 4 = min ω 1, ω 2, ω 3, ω 4 1 − F subject to 0 ≤ ω 1 ω 2 ω 3 ω 4 ≤ 1, wherein, ω 1 is optimum threshold for H m, ω 2 is optimum threshold for B m, ω 3 is optimum threshold for P 1, and ω 1 is optimum threshold for H m, ω 2 is optimum threshold for B m, ω 3 is optimum threshold for P 1, and ω 4 is optimum threshold for P 2; anddetermining quality of an input PPG signal at real time via one or more hardware processors, wherein the quality of the plurality of input PPG signal is determined as one of bad signal and a good signal based on a PPG signal classification technique using the plurality of optimum thresholds.