Data and code: Prediction of Paroxysmal Atrial Fibrillation based on Time- frequency Analysis Network and related studies (Manuscript)

Published: 28 December 2022| Version 1 | DOI: 10.17632/ycm5dpgc67.1
Contributor:
lei liu

Description

These data and codes were used in the submitted manuscript "Prediction of Paroxysmal Atrial Fibrillation based on Time- frequency Analysis Network and related studies", which includes all data and codes for this study. The AFPDB database used in this study is a public dataset and therefore not uploaded here, the download link is https://physionet.org/content/afpdb/1.0.0/. Data: It contains the clinical data from Shandong Provincial Hospital (SPHD) used in this study, and the data have been removed from sensitive information. It includes the original .edt data file and the .csv annotation file, all 24h ECG records. Since Mendeley only has 10G of space available, only a portion of the raw data were uploaded. To avoid lack of space and to ensure that all data is uploaded, we also upload the processed .m data files, which are all the data for this study. 'data_n.m' and 'data_p.m' are data from the AFPDB database, and 'data_paf.m' and 'data_nor.m' are the data in SPHD. ' T-Fmaps' folder is the generated ECG time-frequency map and RR interval time-frequency map. Code: 'edtReader.m' is the code to read the .edt file; 'ConstructTFANet.mlx' is the code to construct TFANet; 'TFANet.m ' is the TFANet network; 'PAFpredict.m' is the code for prediction experiments; 'PAFpredictcross' is the code for cross-patient experiments. No 5 cross-patient experiments were uploaded separately, and only the input data needed to be changed at runtime. The remaining files are functions for denoising and normalization, functions for generating time-frequency maps, code for predicting PAF using RR intervals, predicting PAF based on different time durations, predicting PAF using three classical CNN models, deep learning visualization, and so on. The running environment is Matlab R2022b.

Files

Steps to reproduce

ECG data were obtained from the clinical data of Shandong Provincial Hospital, and the model of dynamic ECG machine used was Baihui CT-083S. The AFPDB database used in this study is a public dataset and therefore not uploaded here, the download link is https://physionet.org/content/afpdb/1.0.0/. Data: It contains the clinical data from Shandong Provincial Hospital (SPHD) used in this study, and the data have been removed from sensitive information. It includes the original .edt data file and the .csv annotation file, all 24h ECG records. Since Mendeley only has 10G of space available, only a portion of the raw data were uploaded. To avoid lack of space and to ensure that all data is uploaded, we also upload the processed .m data files, which are all the data for this study. 'data_n.m' and 'data_p.m' are data from the AFPDB database, and 'data_paf.m' and 'data_nor.m' are the data in SPHD. ' T-Fmaps' folder is the generated ECG time-frequency map and RR interval time-frequency map. Code: 'edtReader.m' is the code to read the .edt file; 'ConstructTFANet.mlx' is the code to construct TFANet; 'TFANet.m ' is the TFANet network; 'PAFpredict.m' is the code for prediction experiments; 'PAFpredictcross' is the code for cross-patient experiments. No 5 cross-patient experiments were uploaded separately, and only the input data needed to be changed at runtime. The remaining files are functions for denoising and normalization, functions for generating time-frequency maps, code for predicting PAF using RR intervals, predicting PAF based on different time durations, predicting PAF using three classical CNN models, deep learning visualization, and so on. The running environment is Matlab R2022b.

Institutions

Shandong University

Categories

Paroxysmal Atrial Fibrillation, Clinical Prediction Model, ECG Database, Deep Learning

Funding

National Natural Science Foundation of China

82072014

National Key Research and Development Program of China

2019YFE010670

Natural Science Foundation of Shandong Province

ZR2020MF028

License