Recent development of piezoelectric biosensors for physiological signal detection and machine learning assisted cardiovascular disease diagnosis.


Journal

RSC advances
ISSN: 2046-2069
Titre abrégé: RSC Adv
Pays: England
ID NLM: 101581657

Informations de publication

Date de publication:
04 Oct 2023
Historique:
received: 31 08 2023
accepted: 21 09 2023
medline: 11 10 2023
pubmed: 11 10 2023
entrez: 11 10 2023
Statut: epublish

Résumé

As cardiovascular disease stands as a global primary cause of mortality, there has been an urgent need for continuous and real-time heart monitoring to effectively identify irregular heart rhythms and to offer timely patient alerts. However, conventional cardiac monitoring systems encounter challenges due to inflexible interfaces and discomfort during prolonged monitoring. In this review article, we address these issues by emphasizing the recent development of the flexible, wearable, and comfortable piezoelectric passive sensor assisted by machine learning technology for diagnosis. This innovative device not only harmonizes with the dynamic mechanical properties of human skin but also facilitates continuous and real-time collection of physiological signals. Addressing identified challenges and constraints, this review provides insights into recent advances in piezoelectric cardiac sensors, from devices to circuit systems. Furthermore, this review delves into the integration of machine learning technologies, showcasing their pivotal role in facilitating continuous and real-time assessment of cardiac status. The synergistic combination of flexible piezoelectric sensor design and machine learning holds substantial potential in automating the detection of cardiac irregularities with minimal human intervention. This transformative approach has the power to revolutionize patient care paradigms.

Identifiants

pubmed: 37818271
doi: 10.1039/d3ra05932d
pii: d3ra05932d
pmc: PMC10561672
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

29174-29194

Informations de copyright

This journal is © The Royal Society of Chemistry.

Déclaration de conflit d'intérêts

There are no conflicts to declare.

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Auteurs

Shunyao Huang (S)

University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University Minhang District Shanghai 200240 China yuljae.cho@sjtu.edu.cn.

Yujia Gao (Y)

University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University Minhang District Shanghai 200240 China yuljae.cho@sjtu.edu.cn.

Yian Hu (Y)

University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University Minhang District Shanghai 200240 China yuljae.cho@sjtu.edu.cn.

Fengyi Shen (F)

University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University Minhang District Shanghai 200240 China yuljae.cho@sjtu.edu.cn.

Zhangsiyuan Jin (Z)

University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University Minhang District Shanghai 200240 China yuljae.cho@sjtu.edu.cn.

Yuljae Cho (Y)

University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University Minhang District Shanghai 200240 China yuljae.cho@sjtu.edu.cn.

Classifications MeSH