Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/32619
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | DE CANNIERE, Helene | - |
dc.contributor.author | Corradi, Federico | - |
dc.contributor.author | SMEETS, Christophe | - |
dc.contributor.author | SCHOUTTETEN, Melanie | - |
dc.contributor.author | Varon, Carolina | - |
dc.contributor.author | Van Hoof, Chris | - |
dc.contributor.author | Van Huffel, Sabine | - |
dc.contributor.author | Groenendaal, Willemijn | - |
dc.contributor.author | VANDERVOORT, Pieter | - |
dc.date.accessioned | 2020-11-17T14:14:49Z | - |
dc.date.available | 2020-11-17T14:14:49Z | - |
dc.date.issued | 2020 | - |
dc.date.submitted | 2020-10-21T11:50:56Z | - |
dc.identifier.citation | SENSORS, 20 (12) (Art N° 3601) | - |
dc.identifier.uri | http://hdl.handle.net/1942/32619 | - |
dc.description.abstract | Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets. However, for clinical acceptance, easy interpretability of the AI models is crucial. The goal of the present study was to investigate whether a multi-parameter sensor could be used during a standardized activity test to interpret functional capacity in the longitudinal follow-up of CR patients. A total of 129 patients were followed for 3 months during CR using 6-min walking tests (6MWT) equipped with a wearable ECG and accelerometer device. Functional capacity was assessed based on 6MWT distance (6MWD). Linear and nonlinear interpretable models were explored to predict 6MWD. The t-distributed stochastic neighboring embedding (t-SNE) technique was exploited to embed and visualize high dimensional data. The performance of support vector machine (SVM) models, combining different features and using different kernel types, to predict functional capacity was evaluated. The SVM model, using chronotropic response and effort as input features, showed a mean absolute error of 42.8 m (+/- 36.8 m). The 3D-maps derived using the t-SNE technique visualized the relationship between sensor-derived biomarkers and functional capacity, which enables tracking of the evolution of patients throughout the CR program. The current study showed that wearable monitoring combined with interpretable ML can objectively track clinical progression in a CR population. These results pave the road towards ambulatory CR. | - |
dc.description.sponsorship | HDC is supported by a doctoral fellowship by the Research Foundation, Flanders (FWO), Belgium (grant number: 1S53616N). SVH acknowledges the financial support of imec. SVH's research received funding from the Flemish Government (AI Research Program).SVH and CV's research is supported by Agentschap Innoveren en Ondernemen (VLAIO) (150466), OSA+. The funding organizations did not make a contribution to the creation of this manuscript. | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.rights | 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | - |
dc.subject.other | wearable sensor | - |
dc.subject.other | machine learning | - |
dc.subject.other | physical fitness assessment | - |
dc.subject.other | cardiac rehabilitation | - |
dc.subject.other | patient progression monitoring | - |
dc.title | Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation | - |
dc.type | Journal Contribution | - |
dc.identifier.issue | 12 | - |
dc.identifier.volume | 20 | - |
local.format.pages | 15 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | De Canniere, H (corresponding author), Hasselt Univ, Fac Med & Life Sci, Mobile Hlth Unit, B-3500 Hasselt, Belgium.; De Canniere, H (corresponding author), Ziekenhuis Oost Limburg, Future Hlth Dept, B-3600 Genk, Belgium. | - |
dc.description.notes | helene.decanniere@uhasselt.be; Federico.corradi@imec.nl; | - |
dc.description.notes | Christophe.smeets@uhasselt.be; Melanie.schoutteten@uhasselt.be; | - |
dc.description.notes | carolina.varon@esat.kuleuven.be; chris.vanhoof@imec.be; | - |
dc.description.notes | sabine.vanhuffel@esat.kuleuven.be; Willemijn.groenendaal@imec.nl; | - |
dc.description.notes | pieter.vandervoort@zol.be | - |
dc.description.other | De Canniere, H (corresponding author), Hasselt Univ, Fac Med & Life Sci, Mobile Hlth Unit, B-3500 Hasselt, Belgium; Ziekenhuis Oost Limburg, Future Hlth Dept, B-3600 Genk, Belgium. helene.decanniere@uhasselt.be; Federico.corradi@imec.nl; Christophe.smeets@uhasselt.be; Melanie.schoutteten@uhasselt.be; carolina.varon@esat.kuleuven.be; chris.vanhoof@imec.be; sabine.vanhuffel@esat.kuleuven.be; Willemijn.groenendaal@imec.nl; pieter.vandervoort@zol.be | - |
local.publisher.place | ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.bibliographicCitation.artnr | 3601 | - |
dc.identifier.doi | 10.3390/s20123601 | - |
dc.identifier.pmid | 32604829 | - |
dc.identifier.isi | WOS:000553058600001 | - |
dc.contributor.orcid | Corradi, Federico/0000-0002-5868-8077; Varon, | - |
dc.contributor.orcid | Carolina/0000-0002-9581-0676; De Canniere, Helene/0000-0002-7681-3011; | - |
dc.contributor.orcid | Van Huffel, Sabine/0000-0001-5939-0996 | - |
local.provider.type | wosris | - |
local.uhasselt.uhpub | yes | - |
local.description.affiliation | [De Canniere, Helene; Smeets, Christophe J. P.; Schoutteten, Melanie; Vandervoort, Pieter] Hasselt Univ, Fac Med & Life Sci, Mobile Hlth Unit, B-3500 Hasselt, Belgium. | - |
local.description.affiliation | [De Canniere, Helene; Smeets, Christophe J. P.; Schoutteten, Melanie; Vandervoort, Pieter] Ziekenhuis Oost Limburg, Future Hlth Dept, B-3600 Genk, Belgium. | - |
local.description.affiliation | [Corradi, Federico; Smeets, Christophe J. P.; Groenendaal, Willemijn] Imec Netherlands, Holst Ctr, NL-5656 AE Eindhoven, Netherlands. | - |
local.description.affiliation | [Varon, Carolina; Van Hoof, Chris; Van Huffel, Sabine] Katholieke Univ Leuven, Dept Elect Engn ESAT, STADIUS Ctr Dynam Syst Signal Proc & Data Analyt, B-3001 Leuven, Belgium. | - |
local.description.affiliation | [Varon, Carolina] Delft Univ Technol, Dept Microelect Circuits & Syst CAS, NL-2600 AA Delft, Netherlands. | - |
local.description.affiliation | [Van Hoof, Chris] Imec Vzw Belgium, B-3001 Leuven, Belgium. | - |
local.description.affiliation | [Vandervoort, Pieter] Ziekenhuis Oost Limburg, Dept Cardiol, B-3600 Genk, Belgium. | - |
item.validation | ecoom 2021 | - |
item.contributor | DE CANNIERE, Helene | - |
item.contributor | Corradi, Federico | - |
item.contributor | SMEETS, Christophe | - |
item.contributor | SCHOUTTETEN, Melanie | - |
item.contributor | Varon, Carolina | - |
item.contributor | Van Hoof, Chris | - |
item.contributor | Van Huffel, Sabine | - |
item.contributor | Groenendaal, Willemijn | - |
item.contributor | VANDERVOORT, Pieter | - |
item.accessRights | Open Access | - |
item.fullcitation | DE CANNIERE, Helene; Corradi, Federico; SMEETS, Christophe; SCHOUTTETEN, Melanie; Varon, Carolina; Van Hoof, Chris; Van Huffel, Sabine; Groenendaal, Willemijn & VANDERVOORT, Pieter (2020) Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation. In: SENSORS, 20 (12) (Art N° 3601). | - |
item.fulltext | With Fulltext | - |
crisitem.journal.eissn | 1424-8220 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
sensors-20-03601 (1).pdf | Published version | 1.41 MB | Adobe PDF | View/Open |
WEB OF SCIENCETM
Citations
19
checked on Apr 30, 2024
Page view(s)
48
checked on Jul 20, 2022
Download(s)
10
checked on Jul 20, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.