Deep-Learning-Based Estimation of Spatial QRS-T angle

Welcome to the DeepLearningSpatialQRS-Tangle repository! 🤓

If you're here, you probably saw our open-access research paper: "Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs", published on 20^th July 2022. In this repository, you'll be able to find code and any relevant documentation mentioned in the paper.

When using this resource, please cite:

Santos Rodrigues A, Augustauskas R, Lukoševičius M, Laguna P, Marozas V. Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs. Sensors. 2022; 22(14):5414. https://doi.org/10.3390/s22145414

A few important notes before we start: - The code is written in MATLAB and Python. The required versions and toolboxes are specified in each section. - Some of the code was implemented by other authors, who are stated/cited accordingly throughout this README.md and the .m files. We provide all relevant citations in the Refs.bib file to simplify referencing and citation. - The prefix "fn" in a filename indicates a MATLAB function (fnFunctionName.m). In every function file, you'll find: - A description of the function; - Any applicable literature; - Summary of input/output variables; - A list of additional .mfiles.

It looks roughly like this: Matlab function [out_A] = fnFunctionName(in_A, in_B)

Data

Physionet's PTB-XL dataset is the 12-lead clinical ECG dataset used in our research work. You can download the dataset here.

When using this database, please cite:

Wagner, P., Strodthoff, N., Bousseljot, R.-D., Kreiseler, D., Lunze, F.I., Samek, W., Schaeffter, T. (2020), PTB-XL: A Large Publicly Available ECG Dataset. Scientific Data. https://doi.org/10.1038/s41597-020-0495-6

Training and Validation Sets

Section 4.3. of our research paper describes the data splitting process into training and validation datasets. Each dataset is disclosed on the files Training_Dataset.csv and Validation_Dataset.csv in the folder Split Datasets. Only the high-quality recordings suitable for analysis (see Section 4.1.1.) are listed. In each table, you'll find:

| ecgid | patientid | sex | scpcodes | class | qrstangle | qrst_range | |:--- |:--- |:--- |:--- |:--- |:--- |:--- | |16968 | 2089 | 1 | {'NORM': 100.0, 'SR': 0.0} | ["Norm"] | 21.081 | [20:25[ | | ... | ... | ... | ... | ... | ... | ... | ... | | 1427 | 20044 | 0 | {'ASMI': 100.0, 'ISCAL': 100.0, 'SR': 0.0} |["MI","STTC"] | 152.1103 | [150:155[ | | ... | ... | ... | ... | ... | ... | ... | ... |

The attributes ecg_id, patient_id, sex and scp_codes correspond to the same attributes in the file ptbxl_database.csv provided by the original documentation of the PTB-XL dataset, whereas qrst_angle is the estimated QRS-T angle $\alpha$ (°) using the conventional approach and qrst_range is the range of $\alpha$ = [0:5:180]° as described in the paper.

You can easily load the .csv files in the MATLAB environment with the readtable function: Matlab Train_Data = readtable("Training_Dataset.csv"); Val_Data = readtable("Validation_Dataset.csv");

How to read the raw ECG files in MATLAB?

After downloading the raw files from the PTB-XL dataset page, you can open the .dat files in MATLAB: Matlab % Switch the environment/directory cd([YOUR_DATA_DIR]) % Read file fid = fopen([recording_name, '.dat']); % Get the raw ECG and convert to volts ecg_raw = transpose(fread(fid, [12 inf], 'int16'))./1000; % Close file fclose(fid); % Get back to working environment cd(WORKING_DIR); Alternatively, you can use our function fnFileReaderECG.m. You can also use Physionet's WFDB Software Package, but we do not recommend it (it is excruciantingly slow).