Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide. The preliminary diagnosis of CVDs is generally performed using an electrocardiogram(ECG). While ECG provides a wide range of cardiac information about different CVDs, the requirement of multiple lead attachments to the body makes the conventional ECG measurement method inconvenient for long-term continuous cardiac monitoring. Moreover, the detection of cardiac abnormalities in ambulatory and home environments needs the incorporation of automatic ECG measurement techniques in wearable devices. Despite various trials towards including ECG measurement in wearable devices, photoplethysmography(PPG) is the most suitable approach. Hence, this study proposes a novel architecture for PPG to ECG reconstruction that uses a Dual-Critic Generative Adversarial Network (GAN). Utilizing Wasserstein-Loss and Discrete Wavelet Transform (DWT) in frequency domain criticism provides a better reconstruction of ECG than other state-of-the-art methods. Our hybrid model yields a root-mean-squared error (RMSE), mean absolute error of heart rate (MAE_HR), and percentage root mean square(PRD) difference between the desired and generated signals obtained are 0.134, 3.75, and 6.064 respectively. The utilization of DWT in frequency domain discriminator and Wasserstein loss helps decrease the metrics values as compared with the values available with different state of the art methods.