ST Holter

This repo is the official implementation of "Using Multi-Task Learning-based Framework to Detect ST-segment and J-point Deviation from Holter".

This including the whole train code of our proposed transformed based model.

This two Holter ECG datasets (R-ECG and E-ECG) from Ruijing Hospital collected for this study will be available after approval by the corresponding author.

Introduction

We proposed a Transformer based structure to precisely detect the location and deviation of ST-segment and J point on 12 leads Holter ECG data in a beat level and provide cardiologists more accurate information about myocardial ischemia.

dataset preparation

1. To train denoise model, manual generate train and val csv in following path
   It should only contain normal ECG feather paths, The noise csv is provied by this this repo:

./data/noise/train_denoise.csv
./data/noise/val_denoise.csv

the denoise ECG data is saving in feather, each feather file contains 12 leads. the example feather files are proven in ./data/noise/

2. To train segmentation model, manual generate train and val csv in following path
   the example csv is provided, contain the data path.

./data/segmentation/train_segmentation.csv
./data/segmentation/val_segmentation.csv

the segmentation ECG data is saving in csv, each feather file contains 12 leads. the example feather files are proven in ./data/segmentation/

train the model

the model is training with config file, which setting the dataset and hypermeter once set the config,

to train denoise

python3 train_unet_denoise.py --config ./configs/denoise.yaml

to train segmentation

python3 train_unet_semseg.py --config ./configs/segmentation.yaml