PPG

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

Installation

Requirements

macOS (Recommended)
Python 2.7
Pip
Virtualenv

Installing with Virtualenv

On Unix, Linux, BSD, macOS, and Cygwin:

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:

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

Usage

Data Processing

Raw data segmentation

python segment.py

Preprocessing

python preprocess.py

Feature extraction

python extract.py

Training set and test set spliting

python split.py

Classification

python classify.py

Data Definition

PPG Signal Data

Location: data/raw/
Filename format: <participant>-<label>.txt

Sample Data

Segmented Signal Data

Location: data/segmented/
Filename format: <participant>.json

Sample Data

{
  "<label>": {
    "sample_rate": <value>,
    "signal": [ ... ]
  },
  ...
}

Preprocessed Data

Location: data/preprocessed/
Filename format: <participant>.json

Sample Data

{
  "<label>": {
    "sample_rate": <value>,
    "single_waveforms": [
      [ ... ],
      ...
    ]
  },
  ...
}

Extracted Feature Data

Location: data/extracted/
Filename format: <participant>.json

Sample Data

{
  "<label>": {
    "sample_rate": <value>,
    "ppg45": [
        [ ... ],
        ...
    ],
    "svri": [ ... ]
  },
  ...
}

Splited Feature Data

Location: data/splited/
Filename format: <participant>.json

Sample Data

{
  "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	`t_pi`	Pulse interval
5	`y/x`	Augmentation index
6	`(x-y)/x`	Relative augmentation index
7	`z/x`
8	`(y-z)/x`
9	`t₁`	Systolic peak time
10	`t₂`	Diastolic peak time
11	`t₃`	Dicrotic notch time
12	`∆T`	Time between systolic and diastolic peaks
13	`w`	Full width at half systolic peak
14	`A₂/A₁`	Inflection point area ratio
15	`t₁/x`	Systolic peak rising slope
16	`y/(t_pi-t₃)`	Diastolic peak falling slope
17	`t₁/t_pi`
18	`t₂/t_pi`
19	`t₃/t_pi`
20	`∆T/t_pi`
21	`t_a1`
22	`t_b1`
23	`t_e1`
24	`t_f1`
25	`b₂/a₂`
26	`e₂/a₂`
27	`(b₂+e₂)/a₂`
28	`t_a2`
29	`t_b2`
30	`t_a1/t_pi`
31	`t_b1/t_pi`
32	`t_e1/t_pi`
33	`t_f1/t_pi`
34	`t_a2/t_pi`
35	`t_b2/t_pi`
36	`(t_a1+t_a2)/t_pi`
37	`(t_b1+t_b2)/t_pi`
38	`(t_e1+t₂)/t_pi`
39	`(t_f1+t₃)/t_pi`
40	`f_base`	Fundamental component frequency
41	`\|s_base\|`	Fundamental component magnitude
42	`f₂`	2^nd harmonic frequency
43	`\|s₂\|`	2^nd harmonic magnitude
44	`f₃`	3^rd harmonic frequency
45	`\|s₃\|`	3^rd harmonic magnitude

API Reference

Module: `ppg`

Excerpt from ppg/__init__.py:

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

Module: `ppg.params`

Excerpt from ppg/params.py:

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

extrema = find_extrema(signal)

PPG Signal Smoothing

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

result = validate_ppg_single_waveform(single_waveform, sample_rate=PPG_SAMPLE_RATE)

PPG Single-Waveform Extraction

single_waveforms = extract_ppg_single_waveform(signal, sample_rate=PPG_SAMPLE_RATE)

Module: `ppg.feature`

PPG Features

PPG-45

extract_ppg45(single_waveform, sample_rate=PPG_SAMPLE_RATE)

Stress-Induced Vascular Response Index (sVRI)

svri = extract_svri(single_waveform)

Module: `ppg.learn`

Split Data Set

train_data, test_data = split_data_set(data, ratio)

Get Feature Set

train_features, train_labels, test_features, test_labels = get_feature_set(data, label_set, feature_type_set)

Classifiers

Logistic Regression Classifier

classifier = logistic_regression_classifier(features, labels)

Support Vector Classifier

classifier = support_vector_classifier(features, labels)

Gaussian Naïve Bayes Classifier

classifier = gaussian_naive_bayes_classifier(features, labels)

Decision Tree Classifier

classifier = decision_tree_classifier(features, labels)

Random Forest Classifier

classifier = random_forest_classifier(features, labels)

AdaBoost Classifier

classifier = adaboost_classifier(features, labels)

Gradient Boosting Classifier

classifier = gradient_boosting_classifier(features, labels)

Voting Classifier

classifier = voting_classifier(estimators, features, labels)

Module: `ppg.utils`

make_dirs_for_file(pathname)

boolean = exist_file(pathname, overwrite=False, display_info=True)

text_data = load_text(pathname, display_info=True)

json_data = load_json(pathname, display_info=True)

dump_json(data, pathname, overwrite=False, display_info=True)

classifier_object = load_model(pathname, display_info=True)

dump_model(model, pathname, overwrite=False, display_info=True)

export_csv(data, fieldnames, pathname, overwrite=False, display_info=True)

datetime = parse_iso_time_string(timestamp)

set_matplotlib_backend(backend=None)

plot(args, backend=None)

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

muskbing / ppg Goto Github PK

ppg's Introduction

PPG

Installation

Requirements

Installing with Virtualenv

Quick Start

Usage

Data Processing

Raw data segmentation

Preprocessing

Feature extraction

Training set and test set spliting

Classification

Data Definition

PPG Signal Data

Sample Data

Segmented Signal Data

Sample Data

Preprocessed Data

Sample Data

Extracted Feature Data

Sample Data

Splited Feature Data

Sample Data

Sensors and Features

PPG-45 Feature Definition

API Reference

Module: ppg

Module: ppg.params

Module: ppg.signal

Peak Finding

PPG Signal Smoothing

PPG Single-Waveform Validation

PPG Single-Waveform Extraction

Module: ppg.feature

PPG Features

PPG-45

Stress-Induced Vascular Response Index (sVRI)

Module: ppg.learn

Split Data Set

Get Feature Set

Classifiers

Logistic Regression Classifier

Support Vector Classifier

Gaussian Naïve Bayes Classifier

Decision Tree Classifier

Random Forest Classifier

AdaBoost Classifier

Gradient Boosting Classifier

Voting Classifier

Module: ppg.utils

File Structure

License

ppg's People

Contributors

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Module: `ppg`

Module: `ppg.params`

Module: `ppg.signal`

Module: `ppg.feature`

Module: `ppg.learn`

Module: `ppg.utils`