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 ONEThe 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 1.14dataset 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 & train file is classify_cnn_keras_noReg_crb.py & classify_lstm_keras_noReg_crb.pyPhysioNet_Dataset is deep learning model which train & validate from PhysioNet CinC 2016 datasets with Tensorflow version 1.14PhysioNet's Deep learning model & train file is classify_cnn_keras_noreg_crb.py & 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 & Abnormal dataFig3 is the code for draw Time-domain & CWT & STFT data using one example dataThe original audio files without any pre-processing can be requested by contacting the authors Mi-Hyung Moon(sophiamoon@daum.net) or Young-Sin Kim(kysin@postech.ac.kr)