In this project proposed, a novel and an efficient methodology is presented for real-time monitoring of ECG signals. The method involves fast Fourier transform (FFT) based discrete wavelet transform (DWT) for extracting the features from the heart-beats which involves less computational complexity in terms of additions and multiplications operations for higher order filter lengths. These features extracted are recognized using particles warm optimization (PSO) tuned twin support vector machines (TSVM) classifier. The TSVM classifier is four times faster than the standard SVM while the PSO technique is employed to gradually tune the classifier parameters to achieve more accuracy. The proposed methodology is implemented on IoT based micro-controller platform and validated on the benchmark Physionetdata to classify 16 categories of ECG signals. Once an abnormality is detected, the platform generates a pop-up message as a warning and sends the information to remote platform allowing hospitals to take preventive measures. The platform reported a higher overall accuracy of 95.68% than the existing studies. Further, such implementation can be utilized as a warning system in both homecare as well as telemonitoring applications to continuously monitor the cardiac condition of a subject any whereto the state-of-art heart disease diagnosis. This project is implemented with MATLAB software.