Cargando…
Rett syndrome severity estimation with the BioStamp nPoint using interactions between heart rate variability and body movement
Rett syndrome, a rare genetic neurodevelopmental disorder in humans, does not have an effective cure. However, multiple therapies and medications exist to treat symptoms and improve patients’ quality of life. As research continues to discover and evaluate new medications for Rett syndrome patients,...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977017/ https://www.ncbi.nlm.nih.gov/pubmed/36857328 http://dx.doi.org/10.1371/journal.pone.0266351 |
_version_ | 1784899202811166720 |
---|---|
author | Suresha, Pradyumna Byappanahalli O’Leary, Heather Tarquinio, Daniel C. Von Hehn, Jana Clifford, Gari D. |
author_facet | Suresha, Pradyumna Byappanahalli O’Leary, Heather Tarquinio, Daniel C. Von Hehn, Jana Clifford, Gari D. |
author_sort | Suresha, Pradyumna Byappanahalli |
collection | PubMed |
description | Rett syndrome, a rare genetic neurodevelopmental disorder in humans, does not have an effective cure. However, multiple therapies and medications exist to treat symptoms and improve patients’ quality of life. As research continues to discover and evaluate new medications for Rett syndrome patients, there remains a lack of objective physiological and motor activity-based (physio-motor) biomarkers that enable the measurement of the effect of these medications on the change in patients’ Rett syndrome severity. In our work, using a commercially available wearable chest patch, we recorded simultaneous electrocardiogram and three-axis acceleration from 20 patients suffering from Rett syndrome along with the corresponding Clinical Global Impression—Severity score, which measures the overall disease severity on a 7-point Likert scale. We derived physio-motor features from these recordings that captured heart rate variability, activity metrics, and the interactions between heart rate and activity. Further, we developed machine learning (ML) models to classify high-severity Rett patients from low-severity Rett patients using the derived physio-motor features. For the best-trained model, we obtained a pooled area under the receiver operating curve equal to 0.92 via a leave-one-out-patient cross-validation approach. Finally, we computed the feature popularity scores for all the trained ML models and identified physio-motor biomarkers for Rett syndrome. |
format | Online Article Text |
id | pubmed-9977017 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99770172023-03-02 Rett syndrome severity estimation with the BioStamp nPoint using interactions between heart rate variability and body movement Suresha, Pradyumna Byappanahalli O’Leary, Heather Tarquinio, Daniel C. Von Hehn, Jana Clifford, Gari D. PLoS One Research Article Rett syndrome, a rare genetic neurodevelopmental disorder in humans, does not have an effective cure. However, multiple therapies and medications exist to treat symptoms and improve patients’ quality of life. As research continues to discover and evaluate new medications for Rett syndrome patients, there remains a lack of objective physiological and motor activity-based (physio-motor) biomarkers that enable the measurement of the effect of these medications on the change in patients’ Rett syndrome severity. In our work, using a commercially available wearable chest patch, we recorded simultaneous electrocardiogram and three-axis acceleration from 20 patients suffering from Rett syndrome along with the corresponding Clinical Global Impression—Severity score, which measures the overall disease severity on a 7-point Likert scale. We derived physio-motor features from these recordings that captured heart rate variability, activity metrics, and the interactions between heart rate and activity. Further, we developed machine learning (ML) models to classify high-severity Rett patients from low-severity Rett patients using the derived physio-motor features. For the best-trained model, we obtained a pooled area under the receiver operating curve equal to 0.92 via a leave-one-out-patient cross-validation approach. Finally, we computed the feature popularity scores for all the trained ML models and identified physio-motor biomarkers for Rett syndrome. Public Library of Science 2023-03-01 /pmc/articles/PMC9977017/ /pubmed/36857328 http://dx.doi.org/10.1371/journal.pone.0266351 Text en © 2023 Suresha et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Suresha, Pradyumna Byappanahalli O’Leary, Heather Tarquinio, Daniel C. Von Hehn, Jana Clifford, Gari D. Rett syndrome severity estimation with the BioStamp nPoint using interactions between heart rate variability and body movement |
title | Rett syndrome severity estimation with the BioStamp nPoint using interactions between heart rate variability and body movement |
title_full | Rett syndrome severity estimation with the BioStamp nPoint using interactions between heart rate variability and body movement |
title_fullStr | Rett syndrome severity estimation with the BioStamp nPoint using interactions between heart rate variability and body movement |
title_full_unstemmed | Rett syndrome severity estimation with the BioStamp nPoint using interactions between heart rate variability and body movement |
title_short | Rett syndrome severity estimation with the BioStamp nPoint using interactions between heart rate variability and body movement |
title_sort | rett syndrome severity estimation with the biostamp npoint using interactions between heart rate variability and body movement |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977017/ https://www.ncbi.nlm.nih.gov/pubmed/36857328 http://dx.doi.org/10.1371/journal.pone.0266351 |
work_keys_str_mv | AT sureshapradyumnabyappanahalli rettsyndromeseverityestimationwiththebiostampnpointusinginteractionsbetweenheartratevariabilityandbodymovement AT olearyheather rettsyndromeseverityestimationwiththebiostampnpointusinginteractionsbetweenheartratevariabilityandbodymovement AT tarquiniodanielc rettsyndromeseverityestimationwiththebiostampnpointusinginteractionsbetweenheartratevariabilityandbodymovement AT vonhehnjana rettsyndromeseverityestimationwiththebiostampnpointusinginteractionsbetweenheartratevariabilityandbodymovement AT cliffordgarid rettsyndromeseverityestimationwiththebiostampnpointusinginteractionsbetweenheartratevariabilityandbodymovement |