# Ecg_emotional

**Repository Path**: mo0/ecg_emotional

## Basic Information

- **Project Name**: Ecg_emotional
- **Description**: 心率异常率情感分析
- **Primary Language**: Unknown
- **License**: AGPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-04-18
- **Last Updated**: 2024-09-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

读取训练模型

```
x_test1 = [ 0.86799905,-0.86718388,-0.93522319,-1.41415684,-1.40287325,-0.08261542,-1.4141857 ,-0.90575292,-1.40281823,-0.89339841,-1.09547377,0.32289227,1.41421356]

x_test1 = [x_test1]
 #读取测试
with open('model/all_svm.pkl', 'rb') as fr:
 new_svm = pickle.load(fr)
 print (new_svm.predict(x_test))   #有两个 [ [ ]  ] 就行
```






### Install

* pip install heartpy
* pip install numpy
* pip install matplotlib
* pip install pandas
* pip install scipy
* pip install sklearn

 **说明**
data文件夹内数据心电采样频率514HZ,皮电采样频率50HZ

### Links

heartpy:https://python-heart-rate-analysis-toolkit.readthedocs.io/en/latest/quickstart.html

MIT-BIT db:  https://archive.physionet.org/cgi-bin/atm/ATM

注: signal类型选择MLII

![MIT-BIT](README.assets/MIT-BIT.png)



关于心电特征提取和皮电特征提取

```
#引入相关库和SkinVector.py文件
import heartpy as hp
import SkinVector

#心电特征提取,measure是一个字典
data = hp.scale_data(data_ecg)
fs = 514  # 采样率
working_data, measures = hp.process(data, fs)

#皮电特征提取,vector是一个字典
vector = SkinVector.get_vector(data_skin)

```







关于CSV文件读取和写入




```
import CsvAccess
#用法
list1 = CsvAccess.dict_key_to_list('number','label',measures,vector)
print(list1)

#用法
list2 = CsvAccess.dict_value_to_list(1,'first',measures,vector)
print(list2)

#写入文件
CsvAccess.write('B.csv',heards_list)

#写入文件
CsvAccess.write('B.csv',vaule_list)

#读取文件某一行
print( CsvAccess.read('B.csv',0) )
```

关于特征值归一化问题

```
#提取特征值在0到1区间
#如果提取错误返回NAN值,请检查原数据的正确性
```