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VitalPy: A Vital Signal Analysis Package

Currently, the package supports PPG preprocessing and extraction of more than 400 features. The PPG pipeline was originally implemented for analysis of the AuroraBP database.

It provides:

  • PPG preprocessing: Singal quality metrics, baseline extraction, etc.
  • PPG feature extraction: time-domain, frequency-domain, statistical features ( >400 features)
  • Compatibility with PPG recorded from 128 Hz to 500 Hz: tested with local devices and large datasets.

Use

VitalPy is written in Python (3.9+). Navigate to the Python repository and install the required packages:

pip install -r requirements.txt

Import:

from src.ppg.PPGSignal import PPGSignal

Check the signal (make sure that the file waveform_df is in dataframe format and contains the columns 't' for time and 'ppg' for the signal values):

signal = PPGSignal(waveform_df, verbose=1)

signal.check_keypoints()

Get features:

signal = PPGSignal(waveform_df, verbose=0)

features = signal.extract_features()

Example plots for a AuroraBP signal

Used file: measurements_oscillometric/o001/o001.initial.Sitting_arm_down.tsv

Mean signal

The following figure shows the mean template computed from all templates within the signal given as input.

Preprocessing steps

All preprocessing steps are depicted. The final result should have filtered out all low quality waveforms.

PPG Keypoints

Exemplary PPG keypoint extraction.

License

VitalPy is available under the General Public License v3.0.

Citation

If you use this repository or any of its components and/or our paper as part of your research, please cite the publication as follows:

A. Cisnal, Y. Li, B. Fuchs, M. Ejtehadi, R. Riener and D. Paez-Granados, "Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring," in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2024.3411693.

@ARTICLE{10552318,
author={Cisnal, Ana and Li, Yanke and Fuchs, Bertram and Ejtehadi, Mehdi and Riener, Robert and Paez-Granados, Diego},
journal={IEEE Journal of Biomedical and Health Informatics}, title={Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring}, year={2024},
volume={},
number={},
pages={1-12},
keywords={Feature extraction;Estimation;Statistics;Sociology;Noise;Morphology;Monitoring;Cuffless blood pressure;photoplethysmography;pulse wave analysis},
doi={10.1109/JBHI.2024.3411693}}

About

A Vital Signal Analysis Package

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biosignalsfeature-extractionmachine-learningppgppg-featuresppg-signal

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Readme
GPL-3.0 license
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Custom properties

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24 stars

Watchers

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1 fork
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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VitalPy: A Vital Signal Analysis Package
Currently, the package supports PPG preprocessing and extraction of more than 400 features. The PPG pipeline was originally implemented for analysis of the AuroraBP database.
It provides:
  • PPG preprocessing: Singal quality metrics, baseline extraction, etc.
  • PPG feature extraction: time-domain, frequency-domain, statistical features ( >400 features)
  • Compatibility with PPG recorded from 128 Hz to 500 Hz: tested with local devices and large datasets.
Use
VitalPy is written in Python (3.9+). Navigate to the Python repository and install the required packages:
pip install -r requirements.txt
Import:
from src.ppg.PPGSignal import PPGSignal
Check the signal (make sure that the file waveform_df is in dataframe format and contains the columns 't' for time and 'ppg' for the signal values):
signal = PPGSignal(waveform_df, verbose=1)
signal.check_keypoints()
Get features:
signal = PPGSignal(waveform_df, verbose=0)
features = signal.extract_features()
Example plots for a AuroraBP signal
Used file: measurements_oscillometric/o001/o001.initial.Sitting_arm_down.tsv
Mean signal
The following figure shows the mean template computed from all templates within the signal given as input.
Preprocessing steps
All preprocessing steps are depicted. The final result should have filtered out all low quality waveforms.
PPG Keypoints
Exemplary PPG keypoint extraction.
License
VitalPy is available under the General Public License v3.0.
Citation
If you use this repository or any of its components and/or our paper as part of your research, please cite the publication as follows:
A. Cisnal, Y. Li, B. Fuchs, M. Ejtehadi, R. Riener and D. Paez-Granados, "Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring," in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2024.3411693.
@ARTICLE{10552318,
author={Cisnal, Ana and Li, Yanke and Fuchs, Bertram and Ejtehadi, Mehdi and Riener, Robert and Paez-Granados, Diego},
journal={IEEE Journal of Biomedical and Health Informatics}, title={Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring}, year={2024},
volume={},
number={},
pages={1-12},
keywords={Feature extraction;Estimation;Statistics;Sociology;Noise;Morphology;Monitoring;Cuffless blood pressure;photoplethysmography;pulse wave analysis},
doi={10.1109/JBHI.2024.3411693}}

About
A Vital Signal Analysis Package
Topics
biosignalsfeature-extractionmachine-learningppgppg-featuresppg-signal
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
24 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
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Used by
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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VitalPy: A Vital Signal Analysis Package
Currently, the package supports PPG preprocessing and extraction of more than 400 features. The PPG pipeline was originally implemented for analysis of the AuroraBP database.
It provides:
  • PPG preprocessing: Singal quality metrics, baseline extraction, etc.
  • PPG feature extraction: time-domain, frequency-domain, statistical features ( >400 features)
  • Compatibility with PPG recorded from 128 Hz to 500 Hz: tested with local devices and large datasets.
Use
VitalPy is written in Python (3.9+). Navigate to the Python repository and install the required packages:
pip install -r requirements.txt
Import:
from src.ppg.PPGSignal import PPGSignal
Check the signal (make sure that the file waveform_df is in dataframe format and contains the columns 't' for time and 'ppg' for the signal values):
signal = PPGSignal(waveform_df, verbose=1)
signal.check_keypoints()
Get features:
signal = PPGSignal(waveform_df, verbose=0)
features = signal.extract_features()
Example plots for a AuroraBP signal
Used file: measurements_oscillometric/o001/o001.initial.Sitting_arm_down.tsv
Mean signal
The following figure shows the mean template computed from all templates within the signal given as input.
Preprocessing steps
All preprocessing steps are depicted. The final result should have filtered out all low quality waveforms.
PPG Keypoints
Exemplary PPG keypoint extraction.
License
VitalPy is available under the General Public License v3.0.
Citation
If you use this repository or any of its components and/or our paper as part of your research, please cite the publication as follows:
A. Cisnal, Y. Li, B. Fuchs, M. Ejtehadi, R. Riener and D. Paez-Granados, "Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring," in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2024.3411693.
@ARTICLE{10552318,
author={Cisnal, Ana and Li, Yanke and Fuchs, Bertram and Ejtehadi, Mehdi and Riener, Robert and Paez-Granados, Diego},
journal={IEEE Journal of Biomedical and Health Informatics}, title={Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring}, year={2024},
volume={},
number={},
pages={1-12},
keywords={Feature extraction;Estimation;Statistics;Sociology;Noise;Morphology;Monitoring;Cuffless blood pressure;photoplethysmography;pulse wave analysis},
doi={10.1109/JBHI.2024.3411693}}

About
A Vital Signal Analysis Package
Topics
biosignalsfeature-extractionmachine-learningppgppg-featuresppg-signal
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
24 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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VitalPy: A Vital Signal Analysis Package
Currently, the package supports PPG preprocessing and extraction of more than 400 features. The PPG pipeline was originally implemented for analysis of the AuroraBP database.
It provides:
  • PPG preprocessing: Singal quality metrics, baseline extraction, etc.
  • PPG feature extraction: time-domain, frequency-domain, statistical features ( >400 features)
  • Compatibility with PPG recorded from 128 Hz to 500 Hz: tested with local devices and large datasets.
Use
VitalPy is written in Python (3.9+). Navigate to the Python repository and install the required packages:
pip install -r requirements.txt
Import:
from src.ppg.PPGSignal import PPGSignal
Check the signal (make sure that the file waveform_df is in dataframe format and contains the columns 't' for time and 'ppg' for the signal values):
signal = PPGSignal(waveform_df, verbose=1)
signal.check_keypoints()
Get features:
signal = PPGSignal(waveform_df, verbose=0)
features = signal.extract_features()
Example plots for a AuroraBP signal
Used file: measurements_oscillometric/o001/o001.initial.Sitting_arm_down.tsv
Mean signal
The following figure shows the mean template computed from all templates within the signal given as input.
Preprocessing steps
All preprocessing steps are depicted. The final result should have filtered out all low quality waveforms.
PPG Keypoints
Exemplary PPG keypoint extraction.
License
VitalPy is available under the General Public License v3.0.
Citation
If you use this repository or any of its components and/or our paper as part of your research, please cite the publication as follows:
A. Cisnal, Y. Li, B. Fuchs, M. Ejtehadi, R. Riener and D. Paez-Granados, "Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring," in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2024.3411693.
@ARTICLE{10552318,
author={Cisnal, Ana and Li, Yanke and Fuchs, Bertram and Ejtehadi, Mehdi and Riener, Robert and Paez-Granados, Diego},
journal={IEEE Journal of Biomedical and Health Informatics}, title={Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring}, year={2024},
volume={},
number={},
pages={1-12},
keywords={Feature extraction;Estimation;Statistics;Sociology;Noise;Morphology;Monitoring;Cuffless blood pressure;photoplethysmography;pulse wave analysis},
doi={10.1109/JBHI.2024.3411693}}

About
A Vital Signal Analysis Package
Topics
biosignalsfeature-extractionmachine-learningppgppg-featuresppg-signal
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
24 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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VitalPy: A Vital Signal Analysis Package
Currently, the package supports PPG preprocessing and extraction of more than 400 features. The PPG pipeline was originally implemented for analysis of the AuroraBP database.
It provides:
  • PPG preprocessing: Singal quality metrics, baseline extraction, etc.
  • PPG feature extraction: time-domain, frequency-domain, statistical features ( >400 features)
  • Compatibility with PPG recorded from 128 Hz to 500 Hz: tested with local devices and large datasets.
Use
VitalPy is written in Python (3.9+). Navigate to the Python repository and install the required packages:
pip install -r requirements.txt
Import:
from src.ppg.PPGSignal import PPGSignal
Check the signal (make sure that the file waveform_df is in dataframe format and contains the columns 't' for time and 'ppg' for the signal values):
signal = PPGSignal(waveform_df, verbose=1)
signal.check_keypoints()
Get features:
signal = PPGSignal(waveform_df, verbose=0)
features = signal.extract_features()
Example plots for a AuroraBP signal
Used file: measurements_oscillometric/o001/o001.initial.Sitting_arm_down.tsv
Mean signal
The following figure shows the mean template computed from all templates within the signal given as input.
Preprocessing steps
All preprocessing steps are depicted. The final result should have filtered out all low quality waveforms.
PPG Keypoints
Exemplary PPG keypoint extraction.
License
VitalPy is available under the General Public License v3.0.
Citation
If you use this repository or any of its components and/or our paper as part of your research, please cite the publication as follows:
A. Cisnal, Y. Li, B. Fuchs, M. Ejtehadi, R. Riener and D. Paez-Granados, "Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring," in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2024.3411693.
@ARTICLE{10552318,
author={Cisnal, Ana and Li, Yanke and Fuchs, Bertram and Ejtehadi, Mehdi and Riener, Robert and Paez-Granados, Diego},
journal={IEEE Journal of Biomedical and Health Informatics}, title={Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring}, year={2024},
volume={},
number={},
pages={1-12},
keywords={Feature extraction;Estimation;Statistics;Sociology;Noise;Morphology;Monitoring;Cuffless blood pressure;photoplethysmography;pulse wave analysis},
doi={10.1109/JBHI.2024.3411693}}

About
A Vital Signal Analysis Package
Topics
biosignalsfeature-extractionmachine-learningppgppg-featuresppg-signal
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
24 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
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Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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VitalPy: A Vital Signal Analysis Package
Currently, the package supports PPG preprocessing and extraction of more than 400 features. The PPG pipeline was originally implemented for analysis of the AuroraBP database.
It provides:
  • PPG preprocessing: Singal quality metrics, baseline extraction, etc.
  • PPG feature extraction: time-domain, frequency-domain, statistical features ( >400 features)
  • Compatibility with PPG recorded from 128 Hz to 500 Hz: tested with local devices and large datasets.
Use
VitalPy is written in Python (3.9+). Navigate to the Python repository and install the required packages:
pip install -r requirements.txt
Import:
from src.ppg.PPGSignal import PPGSignal
Check the signal (make sure that the file waveform_df is in dataframe format and contains the columns 't' for time and 'ppg' for the signal values):
signal = PPGSignal(waveform_df, verbose=1)
signal.check_keypoints()
Get features:
signal = PPGSignal(waveform_df, verbose=0)
features = signal.extract_features()
Example plots for a AuroraBP signal
Used file: measurements_oscillometric/o001/o001.initial.Sitting_arm_down.tsv
Mean signal
The following figure shows the mean template computed from all templates within the signal given as input.
Preprocessing steps
All preprocessing steps are depicted. The final result should have filtered out all low quality waveforms.
PPG Keypoints
Exemplary PPG keypoint extraction.
License
VitalPy is available under the General Public License v3.0.
Citation
If you use this repository or any of its components and/or our paper as part of your research, please cite the publication as follows:
A. Cisnal, Y. Li, B. Fuchs, M. Ejtehadi, R. Riener and D. Paez-Granados, "Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring," in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2024.3411693.
@ARTICLE{10552318,
author={Cisnal, Ana and Li, Yanke and Fuchs, Bertram and Ejtehadi, Mehdi and Riener, Robert and Paez-Granados, Diego},
journal={IEEE Journal of Biomedical and Health Informatics}, title={Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring}, year={2024},
volume={},
number={},
pages={1-12},
keywords={Feature extraction;Estimation;Statistics;Sociology;Noise;Morphology;Monitoring;Cuffless blood pressure;photoplethysmography;pulse wave analysis},
doi={10.1109/JBHI.2024.3411693}}

About
A Vital Signal Analysis Package
Topics
biosignalsfeature-extractionmachine-learningppgppg-featuresppg-signal
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
24 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
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VitalPy: A Vital Signal Analysis Package
Currently, the package supports PPG preprocessing and extraction of more than 400 features. The PPG pipeline was originally implemented for analysis of the AuroraBP database.
It provides:
  • PPG preprocessing: Singal quality metrics, baseline extraction, etc.
  • PPG feature extraction: time-domain, frequency-domain, statistical features ( >400 features)
  • Compatibility with PPG recorded from 128 Hz to 500 Hz: tested with local devices and large datasets.
Use
VitalPy is written in Python (3.9+). Navigate to the Python repository and install the required packages:
pip install -r requirements.txt
Import:
from src.ppg.PPGSignal import PPGSignal
Check the signal (make sure that the file waveform_df is in dataframe format and contains the columns 't' for time and 'ppg' for the signal values):
signal = PPGSignal(waveform_df, verbose=1)
signal.check_keypoints()
Get features:
signal = PPGSignal(waveform_df, verbose=0)
features = signal.extract_features()
Example plots for a AuroraBP signal
Used file: measurements_oscillometric/o001/o001.initial.Sitting_arm_down.tsv
Mean signal
The following figure shows the mean template computed from all templates within the signal given as input.
Preprocessing steps
All preprocessing steps are depicted. The final result should have filtered out all low quality waveforms.
PPG Keypoints
Exemplary PPG keypoint extraction.
License
VitalPy is available under the General Public License v3.0.
Citation
If you use this repository or any of its components and/or our paper as part of your research, please cite the publication as follows:
A. Cisnal, Y. Li, B. Fuchs, M. Ejtehadi, R. Riener and D. Paez-Granados, "Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring," in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2024.3411693.
@ARTICLE{10552318,
author={Cisnal, Ana and Li, Yanke and Fuchs, Bertram and Ejtehadi, Mehdi and Riener, Robert and Paez-Granados, Diego},
journal={IEEE Journal of Biomedical and Health Informatics}, title={Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring}, year={2024},
volume={},
number={},
pages={1-12},
keywords={Feature extraction;Estimation;Statistics;Sociology;Noise;Morphology;Monitoring;Cuffless blood pressure;photoplethysmography;pulse wave analysis},
doi={10.1109/JBHI.2024.3411693}}

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VitalPy: A Vital Signal Analysis Package
Currently, the package supports PPG preprocessing and extraction of more than 400 features. The PPG pipeline was originally implemented for analysis of the AuroraBP database.
It provides:
  • PPG preprocessing: Singal quality metrics, baseline extraction, etc.
  • PPG feature extraction: time-domain, frequency-domain, statistical features ( >400 features)
  • Compatibility with PPG recorded from 128 Hz to 500 Hz: tested with local devices and large datasets.
Use
VitalPy is written in Python (3.9+). Navigate to the Python repository and install the required packages:
pip install -r requirements.txt
Import:
from src.ppg.PPGSignal import PPGSignal
Check the signal (make sure that the file waveform_df is in dataframe format and contains the columns 't' for time and 'ppg' for the signal values):
signal = PPGSignal(waveform_df, verbose=1)
signal.check_keypoints()
Get features:
signal = PPGSignal(waveform_df, verbose=0)
features = signal.extract_features()
Example plots for a AuroraBP signal
Used file: measurements_oscillometric/o001/o001.initial.Sitting_arm_down.tsv
Mean signal
The following figure shows the mean template computed from all templates within the signal given as input.
Preprocessing steps
All preprocessing steps are depicted. The final result should have filtered out all low quality waveforms.
PPG Keypoints
Exemplary PPG keypoint extraction.
License
VitalPy is available under the General Public License v3.0.
Citation
If you use this repository or any of its components and/or our paper as part of your research, please cite the publication as follows:
A. Cisnal, Y. Li, B. Fuchs, M. Ejtehadi, R. Riener and D. Paez-Granados, "Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring," in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2024.3411693.
@ARTICLE{10552318,
author={Cisnal, Ana and Li, Yanke and Fuchs, Bertram and Ejtehadi, Mehdi and Riener, Robert and Paez-Granados, Diego},
journal={IEEE Journal of Biomedical and Health Informatics}, title={Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring}, year={2024},
volume={},
number={},
pages={1-12},
keywords={Feature extraction;Estimation;Statistics;Sociology;Noise;Morphology;Monitoring;Cuffless blood pressure;photoplethysmography;pulse wave analysis},
doi={10.1109/JBHI.2024.3411693}}

About
A Vital Signal Analysis Package
Topics
biosignalsfeature-extractionmachine-learningppgppg-featuresppg-signal
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
24 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages