A processor implemented method for classification of atrial fibrillation (AF) , the method comprising the steps of: acquiring, by one or more hardware processors, a single lead electrocardiogram (ECG) recorded for a first pre-defined time period being less than a minute ; obtaining, by the one or more hardware processors, a first time series being an R-R interval time series based on R peaks in the acquired ECG ; identifying, by the one or more hardware processors, a region having a second pre-defined time period before each of the R peaks to form a second time series, wherein the second time series is a region corresponding to P wave time series and the second pre-defined time period is in the range of 120-200 milliseconds ; inputting, by the one or more hardware processors, the first time series and the second time series independently to an associated Long short-term memory (LSTM) network , wherein the step of inputting the first time series and the second time series independently to an associated Long short-term memory (LSTM) network is followed by: performing a temporal analyses of the R-R intervals using a Bidirectional LSTM (BiLSTM) network to capture irregular R-R intervals ; and performing a temporal analyses of atrial activities using an associated LSTM network, wherein the atrial activities include absence of P waves or presence of f-waves before a QRS complex in the acquired ECG ; merging, by the one or more hardware processors, output states of the LSTM network associated with the first time series and the second time series along with a pre-defined set of handcrafted statistical features computed from the acquired ECG to create a composite feature set for classification of the AF ; and classifying the AF in the acquired ECG based on the composite feature set using a classifier . acquiring, by one or more hardware processors, a single lead electrocardiogram (ECG) recorded for a first pre-defined time period being less than a minute ; obtaining, by the one or more hardware processors, a first time series being an R-R interval time series based on R peaks in the acquired ECG ; identifying, by the one or more hardware processors, a region having a second pre-defined time period before each of the R peaks to form a second time series, wherein the second time series is a region corresponding to P wave time series and the second pre-defined time period is in the range of 120-200 milliseconds ; inputting, by the one or more hardware processors, the first time series and the second time series independently to an associated Long short-term memory (LSTM) network , wherein the step of inputting the first time series and the second time series independently to an associated Long short-term memory (LSTM) network is followed by: performing a temporal analyses of the R-R intervals using a Bidirectional LSTM (BiLSTM) network to capture irregular R-R intervals ; and performing a temporal analyses of atrial activities using an associated LSTM network, wherein the atrial activities include absence of P waves or presence of f-waves before a QRS complex in the acquired ECG ; performing a temporal analyses of the R-R intervals using a Bidirectional LSTM (BiLSTM) network to capture irregular R-R intervals ; and performing a temporal analyses of atrial activities using an associated LSTM network, wherein the atrial activities include absence of P waves or presence of f-waves before a QRS complex in the acquired ECG ; merging, by the one or more hardware processors, output states of the LSTM network associated with the first time series and the second time series along with a pre-defined set of handcrafted statistical features computed from the acquired ECG to create a composite feature set for classification of the AF ; and classifying the AF in the acquired ECG based on the composite feature set using a classifier . <EOS>