A system of detecting arrhythmia using photoplethysmogram signals, said system comprising: a mobile communication device; an image capturing device coupled with the mobile communication device, adapted for extracting photoplethysmogram signals from a user, wherein the photoplethysmogram signals are extracted as a video stream by the image capturing device coupled with the mobile communication device; a feature extraction module adapted for extracting cardiac parameter from the extracted photoplethysmogram signals, wherein the extracted cardiac parameter is heart rate; an abnormality detection module adapted for identifying presence of cardiac abnormalities by detecting premature ventricular contraction and ventricular flutter from the extracted cardiac parameters, wherein the presence of abnormalities in the extracted photoplethysmogram signals is identified by calculating Hurst exponent of the stream of heart rates derived from the extracted PPG signal as reinforcement filtering of detecting premature ventricular contraction and ventricular flutter; a closeness criteria evaluation module adapted for analysing the extracted cardiac parameters to investigate statistical trend and to perform statistical closeness approximation of the extracted photoplethysmogram signals; a decision module adapted for predicting and subsequently classifying type of arrhythmia, wherein the analysing, predicting and subsequently classifying type of arrhythmia based on the extracted features further comprises: finding train of heart rates at each valid cardiac cycle for given PPG signal; finding a feature vector from mean heart rate values; finding the classification outcome set from the heart rate series, using k-nn classifier; classifying the classification outcome set using binary classifier k-means clustering technique; and deciding the type of arrhythmia based on the classification. a mobile communication device; an image capturing device coupled with the mobile communication device, adapted for extracting photoplethysmogram signals from a user, wherein the photoplethysmogram signals are extracted as a video stream by the image capturing device coupled with the mobile communication device; a feature extraction module adapted for extracting cardiac parameter from the extracted photoplethysmogram signals, wherein the extracted cardiac parameter is heart rate; an abnormality detection module adapted for identifying presence of cardiac abnormalities by detecting premature ventricular contraction and ventricular flutter from the extracted cardiac parameters, wherein the presence of abnormalities in the extracted photoplethysmogram signals is identified by calculating Hurst exponent of the stream of heart rates derived from the extracted PPG signal as reinforcement filtering of detecting premature ventricular contraction and ventricular flutter; a closeness criteria evaluation module adapted for analysing the extracted cardiac parameters to investigate statistical trend and to perform statistical closeness approximation of the extracted photoplethysmogram signals; a decision module adapted for predicting and subsequently classifying type of arrhythmia, wherein the analysing, predicting and subsequently classifying type of arrhythmia based on the extracted features further comprises: finding train of heart rates at each valid cardiac cycle for given PPG signal; finding a feature vector from mean heart rate values; finding the classification outcome set from the heart rate series, using k-nn classifier; classifying the classification outcome set using binary classifier k-means clustering technique; and deciding the type of arrhythmia based on the classification. finding train of heart rates at each valid cardiac cycle for given PPG signal; finding a feature vector from mean heart rate values; finding the classification outcome set from the heart rate series, using k-nn classifier; classifying the classification outcome set using binary classifier k-means clustering technique; and deciding the type of arrhythmia based on the classification.