Search results for "Heart Sounds"
showing 4 items of 4 documents
An open access database for the evaluation of heart sound algorithms
2016
In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases…
Classification of Heart Sounds Using Convolutional Neural Network
2020
Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connected layers that are usually used before the classification layer were replaced with a global averag…
Automatic screening of cardiac disorders using wavelet analysis of heart sound
2017
Body auscultation is a dominant method for physical examination of human heart using conventional stethoscope. This clinical method is non invasive and efficient but it requires a medical expert to interpret the heart sound for assessment of cardiac disorders. This paper presents analysis of heart sounds in wavelet domain for automated screening of cardiac disorders. Heart sound signal is transformed in wavelet domain to find out discrimination between heart sounds recorded from healthy and anomalous patients. Discriminatory features extracted from wavelet coefficients of heart sound are subjected to machine learning for screening of cardiac disorders automatically. The proposed method for …
An innovative approach towards E-health in development of tele auscultation system for heart using GSM mobile communication technology
2013
Health is one of the major issues for normal existence of human life and it is a global agenda to increase the health care facilities for the peoples who don't have an immediate access to these facilities and are living in rural and undeveloped areas. From the beginning of twenty first century field of E-health has been developed rapidly to encounter these problems properly. The healthcare infrastructure gaining betterment day by day under the shadow of E-health technologies, where using mobile phones are becoming an efficient tool in monitoring and transmitting different physiological signals. Cardiac sound is an initiative physiological parameter that is helpful for diagnosing changes in …