Advanced Deep Learning for ECG Anomaly Detection in Imbalanced Data

Segmentation of ECG to obtain significant and relevant features has been an inevitable step to reduce the dimensionality of dataset in automated heart disease diagno- sis systems.Accurate and speedy classification of heart beats is required to reduce high mortality rate which is prevalent due to cardiovascular diseases(CVD). Nonstationarity and high variability exhibited by ECG signal leads to increase in com- plexity of analysis in time and frequency domain.Challenges in processing are further enhanced due to imbalanced and vague datasets.Deep learning based methods have been used in litera- ture to combat the problem of imbalanced datasets.This paper employs an effective recurrent neural network with long short term memory layers(LSTM) to classify the heart beats into two classes.It has been observed that LSTM network can effectively extract the sequential timing information in the input ECG samples.To remove the imbalance in the datasets,oversampling and focal loss based weight balancing techniques have been used which eventually enhance the accuracy of classification. MIT- BIH database has been used for experimental evaluation.The proposed approach ,LSTM network with oversampling tech- nique,provides an accuracy of 99.54% which is far better as compared to the traditional approaches which yield accuracy around 95%.Moreover this method is insensitive to quality of ECG signals due to the involvement of fuzzification procedure in the initial steps.Deployment of the proposed method for biosignal telemetry or pharmaceutical research to assist the physicians in their work is a possible future advancement in this domain.