ECG-Segment-LSTM

环境：python3 + pytorch1.0.0

ENV:python3 + pytorch1.0.0

依赖的包：wfdb、pickle、numpy、scipy、matplotlib

Independent Package:wfdb、pickle、numpy、scipy、matplotlib

使用数据库：https://physionet.org/physiobank/database/qtdb/

Used database Url:https://physionet.org/physiobank/database/qtdb/

参考链接：https://github.com/niekverw/Deep-Learning-Based-ECG-Annotator

Ref Rep:https://github.com/niekverw/Deep-Learning-Based-ECG-Annotator

本工程通过LSTM模型实现了对ECG信号的波形分割，共6个波形：背景、P波、PQ段、QR段、RS段、ST段，分别对应标签0~5

This project achieved ECG signal wave segmentation,by using LSTM net.There are six waves:background/P Segment/PQ Seg/QR Seg/RS Seg/ST Seg,label is 0~6.

Getting Start

• 下载qt数据集至qtdb文件夹， 应该包含.hed .q1c等后缀格式文件

  download qtdb,including .hed .q1c files

• 运行python qtdatabase.py 会在qtdb_pkl文件下生成train_data.pkl和val_data.pkl

  run python qtdatabase.py, it will generate train_data.pkl and val_data.pkl in folder "qtdb_pkl"

• 运行python model_lstm.py 训练LSTM模型，模型存储在ckpt文件下

  run python model_lstm.py to training LSTM Net, its result will be in folder "ckpt"

一个心跳的各个波形的标注

The characteristic waves of a heart beat

阅读源代码和注释

Please to read the source code and annotations

Output

下图为预测结果与标签对比，对于一组红色的线，上面那条为label，下面那条为predict val

The figure shows the predict and label, for example, a couple of red lines, the upper is label, the lower is predict

下面为连续heart beat的预测结果图,见代码multi_beats分支

several continue heart beat segment, please download branch multi_beats

Result

我们使用了M±SD(ms)指标

method	P-peak	Q-pose	R-peak	S-pose	T-peak
our	0.34±2.92	0.03±0.84	0.03±0.32	0.18±1.00	-0.06±1.29
RAN	-0.4±10.1	-0.7±10.9	NA	-4.8±13.1	-3.0±10.5
CNN	3.9±14.2	-0.3±14	NA	-6.6±15.2	-4.5±17.2

我们使用了带有容忍误差(ms)的detection ACC的指标

容忍误差(ms)	P-peak	Q-pose	R-Peak	S-pose	T-Peak
0.8	75.96%	84.24%	98.74%	75.95%	72.37%
1.2	88.03%	93.70%	99.58%	87.39%	85.29%
1.6	94.01%	96.95%	99.58%	93.17%	90.86%

Network

2层双向LSTM构成特征提取层，2个使用了dropout的全连接层，最后一个softmax的输出层

two-layers bi-LSTM + two Linear layer with dropout + softmax output