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Photoplethysmogram-based Real-Time Cognitive Load Assessment Using Multi-Feature Fusion Model

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# PPG
Photoplethysmogram-based Real-Time Cognitive Load Assessment Using Multi-Feature Fusion Model

## Installation
### Requirements
- [macOS][macos] (Recommended)
- [Python 2.7][python]
- [Pip][pip]
- [Virtualenv][virtualenv]

### Installing with Virtualenv
On Unix, Linux, BSD, macOS, and Cygwin:

```sh
git clone https://github.com/iRB-Lab/PPG.git
cd PPG
virtualenv venv
source venv/bin/activate
pip install -r requirements.txt
```

## Quick Start
On Unix, Linux, BSD, macOS, and Cygwin:

```sh
./scripts/process_data.sh
./scripts/classify.sh
```

## Usage
### Data Processing
##### Raw data segmentation
```sh
python segment.py
```

##### Preprocessing
```sh
python preprocess.py
```

##### Feature extraction
```sh
python extract.py
```

##### Training set and test set spliting
```sh
python split.py
```

### Classification
```sh
python classify.py
```

## Data Definition
### PPG Signal Data
- **Location:** `data/raw/`
- **Filename format:** `<participant>-<label>.txt`

###### Sample Data
```
109
110
109
109
...
```

### Segmented Signal Data
- **Location:** `data/segmented/`
- **Filename format:** `<participant>.json`

###### Sample Data
```js
{
 "<label>": {
 "sample_rate": <value>,
 "signal": [ ... ]
 },
 ...
}
```

### Preprocessed Data
- **Location:** `data/preprocessed/`
- **Filename format:** `<participant>.json`

###### Sample Data
```js
{
 "<label>": {
 "sample_rate": <value>,
 "single_waveforms": [
 [ ... ],
 ...
 ]
 },
 ...
}
```

### Extracted Feature Data
- **Location:** `data/extracted/`
- **Filename format:** `<participant>.json`

###### Sample Data
```js
{
 "<label>": {
 "sample_rate": <value>,
 "ppg45": [
 [ ... ],
 ...
 ],
 "svri": [ ... ]
 },
 ...
}
```

### Splited Feature Data
- **Location:** `data/splited/`
- **Filename format:** `<participant>.json`

###### Sample Data
```js
{
 "train": {
 "<label>": [
 {
 "ppg45": [
 [ ... ],
 ...
 ],
 "svri": [ ... ]
 },
 ...
 ],
 ...
 },
 "test": { ... }
}
```

## Sensors and Features
|Sensor|Feature|Dimension|
|:--|:--|:-:|
|PPG finger clip|PPG-45 (39 time-domain, 9 frequency-domain)|45|
||Stress-induced vascular response index (sVRI)|1|

### PPG-45 Feature Definition
|#|Feature|Description|
|--:|:--|:--|
|1|`x`|Systolic peak|
|2|`y`|Diastolic peak|
|3|`z`|Dicrotic notch|
|4|<code>tpi</code>|Pulse interval|
|5|`y/x`|Augmentation index|
|6|`(x-y)/x`|Relative augmentation index|
|7|`z/x`||
|8|`(y-z)/x`||
|9|<code>t1</code>|Systolic peak time|
|10|<code>t2</code>|Diastolic peak time|
|11|<code>t3</code>|Dicrotic notch time|
|12|`∆T`|Time between systolic and diastolic peaks|
|13|`w`|Full width at half systolic peak|
|14|<code>A2/A1</code>|Inflection point area ratio|
|15|<code>t1/x</code>|Systolic peak rising slope|
|16|<code>y/(tpi-t3)</code>|Diastolic peak falling slope|
|17|<code>t1/tpi</code>||
|18|<code>t2/tpi</code>||
|19|<code>t3/tpi</code>||
|20|<code>∆T/tpi</code>||
|21|<code>ta1</code>||
|22|<code>tb1</code>||
|23|<code>te1</code>||
|24|<code>tf1</code>||
|25|<code>b2/a2</code>||
|26|<code>e2/a2</code>||
|27|<code>(b2+e2)/a2</code>||
|28|<code>ta2</code>||
|29|<code>tb2</code>||
|30|<code>ta1/tpi</code>||
|31|<code>tb1/tpi</code>||
|32|<code>te1/tpi</code>||
|33|<code>tf1/tpi</code>||
|34|<code>ta2/tpi</code>||
|35|<code>tb2/tpi</code>||
|36|<code>(ta1+ta2)/tpi</code>||
|37|<code>(tb1+tb2)/tpi</code>||
|38|<code>(te1+t2)/tpi</code>||
|39|<code>(tf1+t3)/tpi</code>||
|40|<code>fbase</code>|Fundamental component frequency|
|41|<code>\|sbase\|</code>|Fundamental component magnitude|
|42|<code>f2</code>|2nd harmonic frequency|
|43|<code>\|s2\|</code>|2nd harmonic magnitude|
|44|<code>f3</code>|3rd harmonic frequency|
|45|<code>\|s3\|</code>|3rd harmonic magnitude|

## API Reference
### Module: `ppg`
Excerpt from `ppg/__init__.py`:

```python
BASE_DIR = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
```

### Module: `ppg.params`
Excerpt from `ppg/params.py`:

```python
MINIMUM_PULSE_CYCLE = 0.5
MAXIMUM_PULSE_CYCLE = 1.2

PPG_SAMPLE_RATE = 200
PPG_FIR_FILTER_TAP_NUM = 200
PPG_FILTER_CUTOFF = [0.5, 5.0]
PPG_SYSTOLIC_PEAK_DETECTION_THRESHOLD_COEFFICIENT = 0.5

TRAINING_DATA_RATIO = 0.75
```

### Module: `ppg.signal`
##### Peak Finding
```python
extrema = find_extrema(signal)
```

##### PPG Signal Smoothing
```python
smoothed_ppg_signal = smooth_ppg_signal(
    signal,
    sample_rate=PPG_SAMPLE_RATE,
    numtaps=PPG_FIR_FILTER_TAP_NUM,
    cutoff=PPG_FILTER_CUTOFF
)
```

##### PPG Single-Waveform Validation
```python
result = validate_ppg_single_waveform(single_waveform, sample_rate=PPG_SAMPLE_RATE)
```

##### PPG Single-Waveform Extraction
```python
single_waveforms = extract_ppg_single_waveform(signal, sample_rate=PPG_SAMPLE_RATE)
```

### Module: `ppg.feature`
#### PPG Features
##### PPG-45
```python
extract_ppg45(single_waveform, sample_rate=PPG_SAMPLE_RATE)
```

##### Stress-Induced Vascular Response Index (sVRI)
```python
svri = extract_svri(single_waveform)
```

### Module: `ppg.learn`
##### Split Data Set
```python
train_data, test_data = split_data_set(data, ratio)
```

##### Get Feature Set
```python
train_features, train_labels, test_features, test_labels = get_feature_set(data, label_set, feature_type_set)
```

#### Classifiers
##### Logistic Regression Classifier
```python
classifier = logistic_regression_classifier(features, labels)
```

##### Support Vector Classifier
```python
classifier = support_vector_classifier(features, labels)
```

##### Gaussian Naïve Bayes Classifier
```python
classifier = gaussian_naive_bayes_classifier(features, labels)
```

##### Decision Tree Classifier
```python
classifier = decision_tree_classifier(features, labels)
```

##### Random Forest Classifier
```python
classifier = random_forest_classifier(features, labels)
```

##### AdaBoost Classifier
```python
classifier = adaboost_classifier(features, labels)
```

##### Gradient Boosting Classifier
```python
classifier = gradient_boosting_classifier(features, labels)
```

##### Voting Classifier
```python
classifier = voting_classifier(estimators, features, labels)
```

### Module: `ppg.utils`
```python
make_dirs_for_file(pathname)
```
```python
boolean = exist_file(pathname, overwrite=False, display_info=True)
```
```python
text_data = load_text(pathname, display_info=True)
```
```python
json_data = load_json(pathname, display_info=True)
```
```python
dump_json(data, pathname, overwrite=False, display_info=True)
```
```python
classifier_object = load_model(pathname, display_info=True)
```
```python
dump_model(model, pathname, overwrite=False, display_info=True)
```
```python
export_csv(data, fieldnames, pathname, overwrite=False, display_info=True)
```
```python
datetime = parse_iso_time_string(timestamp)
```
```python
set_matplotlib_backend(backend=None)
```
```python
plot(args, backend=None)
```
```python
semilogy(args, backend=None)
```

## File Structure
```
├── data/
│ ├── raw/
│ │ ├── <participant>-<session_id>-<block_id>-<task_level>.json
│ │ └── ...
│ ├── segmented/
│ │ ├── <participant>.json
│ │ └── ...
│ ├── preprocessed/
│ │ ├── <participant>.json
│ │ └── ...
│ └── extracted/
│ ├── <participant>.json
│ └── ...
├── models/
│ └── ...
├── results/
│ └── ...
├── ppg/
│ ├── __init__.py
│ ├── params.py
│ ├── signal.py
│ ├── feature.py
│ ├── learn.py
│ └── utils.py
├── scripts/
│ ├── process_data.sh
│ └── classify.sh
├── segment.py
├── preprocess.py
├── extract.py
├── split.py
├── classify.py
├── requirements.txt
├── README.md
├── LICENSE
└── .gitignore
```

## License
[MIT License][license]

[repository]: https://github.com/iRB-Lab/PPG "iRB-Lab/PPG"
[license]: https://github.com/iRB-Lab/PPG/LICENSE "License"
[watch]: https://github.com/iRB-Lab/PPG/watchers "Watchers"
[star]: https://github.com/iRB-Lab/PPG/stargazers "Stargazers"
[fork]: https://github.com/iRB-Lab/PPG/network "Forks"

[macos]: https://www.apple.com/macos/ "macOS"
[python]: https://docs.python.org/2/ "Python 2.7"
[pip]: https://pypi.python.org/pypi/pip "Pip"
[virtualenv]: https://virtualenv.pypa.io/en/stable/ "Virtualenv"

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