  This paper aims to classify a single PCG recording as normal or abnormal for
computer-aided diagnosis. The proposed framework for this challenge has four
steps: preprocessing, feature extraction, training and validation. In the
preprocessing step, a recording is segmented into four states, i.e., the first
heart sound, systolic interval, the second heart sound, and diastolic interval
by the Springer Segmentation algorithm. In the feature extraction step, the
authors extract 324 features from multi-domains to perform classification. A
back propagation neural network is used as predication model. The optimal
threshold for distinguishing normal and abnormal is determined by the
statistics of model output for both normal and abnormal. The performance of the
proposed predictor tested by the six training sets is sensitivity 0.812 and
specificity 0.860 (overall accuracy is 0.836). However, the performance reduces
to sensitivity 0.807 and specificity 0.829 (overall accuracy is 0.818) for the
hidden test set.
