Detection of heart condition by time and frequency based template

The phonocardiogram (PCG) is an easy and costless yet powerful tool to detect the heart condition. While the cardiovascular disease is an increasing global threat to humanity, there is a decrease in doctors' capability for diagnosing these diseases by auscultation. In this work, a neural network based automated system is proposed to aid early detection of these diseases. Two template-based feature representations are developed to effectively represent the characteristics of heart sound. These features, extracted from sound files of known cases, are then used to train a neural network. Our experimental results corroborate that the proposed method can efficiently detect the heart condition with good overall classification accuracy. In addition, a graphical user interface and a low cost device is developed which is user-friendly and can be used without much relevant knowledge.