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

The protocol for this study was approved by the Institutional Review Board for Seoul St.Mary’s Hospital (approval no. KC17TESI0684 ).

Infrasound_Dataset is deep learning model which train & validate from our infrasound datasets with Tensorflow version 1.14
dataset 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 & train file is classify_cnn_keras_noReg_crb.py & classify_lstm_keras_noReg_crb.py
PhysioNet_Dataset is deep learning model which train & validate from PhysioNet CinC 2016 datasets with Tensorflow version 1.14
PhysioNet's Deep learning model & train file is classify_cnn_keras_noreg_crb.py & classify_lstm_keras_noreg_crb.py
The code_heartsound.m file performs a Continuous Wavelet Transform (CWT) on an audio file and saves the results in a CSV file
Fig2 is the code for draw FFT results to compare Normal & Abnormal data
Fig3 is the code for draw Time-domain & CWT & STFT data using one example data
The 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])