Effects of precise cardio sounds on the success rate of phonocardiography

Code and Dataset for 'Effects of precise cardio sounds on the success rate of phonocardiography' on PLOS ONE<br>The protocol for this study was approved by the Institutional Review Board for Seoul St.Mary’s Hospital (approval no. KC17TESI0684 ).<br>Infrasound_Dataset is deep learning model which train &amp; validate from our infrasound datasets with Tensorflow version 1.14dataset file is .hdf5 files which can load with python.The structure of the dataset file is shown below.normal data: 0, 1, sex_data, age_data, fold_label, flatten data(2d CWT)abnormal data: 1, 0, sex_data, age_data, fold_label, flatten data(2d CWT)Reference Files is a list of labels for datasets, separated by measurement period.Infrasound dataset's Deep learning model &amp; train file is classify_cnn_keras_noReg_crb.py &amp; classify_lstm_keras_noReg_crb.pyPhysioNet_Dataset is deep learning model which train &amp; validate from PhysioNet CinC 2016 datasets with Tensorflow version 1.14PhysioNet's Deep learning model &amp; train file is classify_cnn_keras_noreg_crb.py &amp; classify_lstm_keras_noreg_crb.pyThe code_heartsound.m file performs a Continuous Wavelet Transform (CWT) on an audio file and saves the results in a CSV fileFig2 is the code for draw FFT results to compare Normal &amp; Abnormal dataFig3 is the code for draw Time-domain &amp; CWT &amp; STFT data using one example dataThe original audio files without any pre-processing can be requested by contacting the authors Mi-Hyung Moon([e-mail]) or Young-Sin Kim([e-mail])