Deep convolutional neural networks and mel-frequency spectral coefficients achieved an overall score of 84.15% for recognizing normal-abnormal phonocardiographic signals.
Can deep convolutional neural networks and mel-frequency spectral coefficients accurately recognize normal-abnormal phonocardiographic signals?
Deep convolutional neural networks combined with mel-frequency spectral coefficients can effectively classify normal and abnormal heart sounds with high accuracy.
Intensive care unit patients are heavily monitored, and several clinically-relevant parameters are routinely extracted from high resolution signals. OBJECTIVE: The goal of the 2016 PhysioNet/CinC Challenge was to encourage the creation of an intelligent system that fused information from different phonocardiographic signals to create a robust set of normal/abnormal signal detections. APPROACH: Deep convolutional neural networks and mel-frequency spectral coefficients were used for recognition of normal-abnormal phonocardiographic signals of the human heart. This technique was developed using the PhysioNet.org Heart Sound database and was submitted for scoring on the challenge test set. MAIN RESULTS: The current entry for the proposed approach obtained an overall score of 84.15% in the last phase of the challenge, which provided the sixth official score and differs from the best score of 86.02% by just 1.87%.
Maknickas et al. (Thu,) conducted a other in Normal-abnormal phonocardiographic signals. Deep convolutional neural networks and mel-frequency spectral coefficients was evaluated on Overall score for recognition of normal-abnormal phonocardiographic signals. Deep convolutional neural networks and mel-frequency spectral coefficients achieved an overall score of 84.15% for recognizing normal-abnormal phonocardiographic signals.