course/application_example/ecg

• ECG
• • Pretrained model
  • Training Parameter description
  • Example
  • • Dataset
    • Data augmentation and Train Model
    • Evaluate Model
    • Infer

ECG

The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities
will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The
basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct
classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories
namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and
time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a
9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG
signals. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify
different types and frequency of arrhythmic heartbeats.

Pretrained model

Model trained by MindSpore:

model	precision	ckpt
ECG_net	...	ckpt

Model trained by PyTorch:

model	precision
ECG_net	0.9403

Training Parameter description

Parameter	Default	Description
graph_mode	True	Graph_mode type
device_target	GPU	Device type
data_path	"/30X_eu_MLIII.csv"	Path of data
label_path	"/30Y_eu_MLIII.csv"	Path of label
batch_size	128	Number of batch size
momentum	0.7	Number of momentum
lr	0.003	Number of learning rate
weight_decay	1e-6	Number of weight_decay
save_checkpoint_steps	1000	Number of steps about saving checkpoint
keep_checkpoint_max	10	The maximum number of saving checkpoint
checkpoint_dir	"/save/"	Path of saving checkpoint
per_print_times	100	Print after per_print_times times

Example

Here, how to use PFLD model will be introduec as following.

Dataset

At first, you should download dataset by yourself.
European ST-T
、 MIT-BIH Arrhythmia
、MIT-BIH ST Change
and Sudden Cardiac Death Holter dataset is supported.

Attention,......
After you get the dataset, make sure your path is as following:

.datasets/
    └── 300W
    |    ├── 300W_annotations
    |    |      └── Mirrors68.txt
    |    └── 300W_images
    |           ├── afw
    |           ├── helen
    |           ├── ibug
    |           └── ifpw
    └── WFLW
    |    ├── WFLW_annotations
    |    |      ├── list_98pt_rect_attr_train_test
    |    |      ├── list_98pt_test
    |    |      └── Mirrors98.txt
    |    └── WFLW_images
    |           ├── 0-Parade
    |           ├── 1-Handshaking
    |           ......
    └── infer_image

Data augmentation and Train Model

Before you start to train the model. Data augmentation is necessary for your dataset and create train data and test
data.

After that you will have another two folders in you ... folder or ... folder.

Attention, ...

Run the train.py to start to train the model.

Attention, when you change the dataset, you have to change target_dataset, model_type, train_file_path the
three parameter.

python train.py --target_dataset 300W --model_type 68_points --train_file_path ./datasets/300W/train_data/list.txt

output:

Epoch:[40/40], step:[127/129], loss:[0.709/0.732], time:418.097ms, lr:0.00001
Epoch:[40/40], step:[128/129], loss:[0.739/0.739], time:417.928ms, lr:0.00001
......
Epoch:[40/40], step:[129/129], loss:[0.729/0.734], time:418.322ms, lr:0.00001

Evaluate Model

After training, you can use test set to evaluate the performance of your model.

Run eval.py to achieve this. The usage of model_type parameter is same as training process.

python eval.py --model_type 68_points

output:

nme:0.0641
ion:0.5747
ipn:0.3195
inference_cost_time: 0.014595

Infer

At last, you can use your own image to test your model. Put your image in the infer_image folder, then run infer.py to
do inference.

python infer.py --model_type 68_points

Result

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