Classification of normal and abnormal heart sound recordings through robust feature selection

We aim to develop a reliable and robust algorithm that accurately analyses a single short PCG recording (10-60s) from a single precordial location to determine the presence of heart abnormality for the Physionet/ Computing-in-Cardiology 2016 challenge. We extract timing information for the fundamental Heart Sounds i.e. S1 and S2 using Hidden Markov Model based Springer's improved version of Schmidt's method. These values are then used to generate statistical features set in temporal, frequency, time-frequency and wavelet domain. We choose the optimal feature set out of the pool of overall 54 features using mutual information based minimum Redundancy Maximum Relevance (mRMR) technique. In order to cope with bad signals, we also check the signal quality of the PCG signal. Signals are rejected for further normal abnormal classification when the outside/background noise has rendered them useless for processing. Then, non-linear radial basis function based Support Vector Machine (SVM) classifier along with ensemble based methods is used to train with the reduced optimal feature sets, on a balanced training set chosen from the group of all PCG datasets. Our algorithm is tested with hidden Physionet Challenge 2016 datasets and performance achieved is: Sensitivity (Se) = 0.7749, Specificity (Sp) = 0.7891 and Overall Score calculated as mean (Se, Sp) = 0.7820.