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

109
110
109
109
...

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	tpi	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/(tpi-t3)	Diastolic peak falling slope
17	t1/tpi
18	t2/tpi
19	t3/tpi
20	∆T/tpi
21	ta1
22	tb1
23	te1
24	tf1
25	b2/a2
26	e2/a2
27	(b2+e2)/a2
28	ta2
29	tb2
30	ta1/tpi
31	tb1/tpi
32	te1/tpi
33	tf1/tpi
34	ta2/tpi
35	tb2/tpi
36	(ta1+ta2)/tpi
37	(tb1+tb2)/tpi
38	(te1+t2)/tpi
39	(tf1+t3)/tpi
40	fbase	Fundamental component frequency
41	|sbase|	Fundamental component magnitude
42	f2	2nd harmonic frequency
43	|s2|	2nd harmonic magnitude
44	f3	3rd harmonic frequency
45	|s3|	3rd 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