Ambulatory cardiac bio-signals: From mirage to clinical reality through a decade of progress

Periyaswamy, Thamizhisai; Balasubramanian, Mahendran

doi:10.1016/j.ijmedinf.2019.07.007

Cited by 9 publications

(5 citation statements)

References 116 publications

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“…In addition, dematerialization capability has been linked to PSSs due to the change in ownership perspective. Changing the use scheme to shared use (C23) refers to access-based products, such as renting or leasing for a period of time, but ownership still remains with the provider in charge of maintenance and repair, for example, ambulatory cardiac biosignals such as MoMe Kardia by InfoBionic [ 39 , 45 , 46 ]. Finally, the service platform (C24) refers to the smart services offered digitally and independently of the product [ 38 ].…”

Section: Resultsmentioning

confidence: 99%

“…Diagnostic services are becoming available and accessible through healthcare-based platforms, such as patient records, next-generation sequencing, biomarker data, and big data research [ 36 , 37 ]. In addition, diagnostic services are translated to the consumer through mobile apps and miniaturized digital electronics that are implantable or wearable, allowing real-time monitoring of daily life signals and aiding in prompt diagnosis and treatment [ [38] , [39] , [40] ]. Telemonitoring services can be applied to clinical trials, patient records, patient devices, and virtual consultations to track a patient's health to remotely interpret data and make decisions regarding follow-up and treatment [ 1 , 41 ].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Smart product-service systems in the healthcare industry: Intelligent connected products and stakeholder communication drive digital health service adoption

Negash¹,

Sarmiento²

2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Smart product-service systems in the healthcare industry: Intelligent connected products and stakeholder communication drive digital health service adoption

Negash¹,

Sarmiento²

2023

Heliyon

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“… 64 Recently, in addition to Holter monitoring, which has been traditionally used, the use of patch-type, watch-type, ring-type, and clothing-type monitoring systems have been approved by the FDA. 138 Accordingly, the availability of ambulatory biosignals is expected to increase significantly in the future.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis

et al. 2022

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Purpose Several artificial intelligence (AI) models for the detection and prediction of cardiovascular-related diseases, including arrhythmias, diabetes, and sleep apnea, have been reported. This systematic review and meta-analysis aimed to identify AI models developed for or applicable to wearable and mobile devices for diverse cardiovascular-related diseases. Materials and Methods The searched databases included Medline, Embase, and Cochrane Library. For AI models for atrial fibrillation (AF) detection, a meta-analysis of diagnostic accuracy was performed to summarize sensitivity and specificity. Results A total of 102 studies were included in the qualitative review. There were AI models for the detection of arrythmia (n=62), followed by sleep apnea (n=11), peripheral vascular diseases (n=6), diabetes mellitus (n=5), hyper/hypotension (n=5), valvular heart disease (n=4), heart failure (n=3), myocardial infarction and cardiac arrest (n=2), and others (n=4). For quantitative analysis of 26 studies reporting AI models for AF detection, meta-analyzed sensitivity was 94.80% and specificity was 96.96%. Deep neural networks showed superior performance [meta-analyzed area under receiver operating characteristics curve (AUROC) of 0.981] compared to conventional machine learning algorithms (meta-analyzed AUROC of 0.961). However, AI models tested with proprietary dataset (meta-analyzed AUROC of 0.972) or data acquired from wearable devices (meta-analyzed AUROC of 0.977) showed inferior performance than those with public dataset (meta-analyzed AUROC of 0.986) or data from in-hospital devices (meta-analyzed AUROC of 0.983). Conclusion This review found that AI models for diverse cardiovascular-related diseases are being developed, and that they are gradually developing into a form that is suitable for wearable and mobile devices.

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“…With the emergence of 24-h portable ECG or cardiac implantable electronic devices (CIEDs), such as a pacemaker, subclinical AF and device-detected AF can be detected, increasing the accessibility of the risk factors of stroke [33][34][35]. Moreover, with the dramatic increase in the accessibility to ambulatory ECG via wearable devices, other cardiac risk factors related to stroke can be monitored continuously and analyzed in real-time [36].…”

Section: Devices For Ecg Monitoringmentioning

confidence: 99%

Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction

Chen

2021

Sensors

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We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (EHRs) and machine learning (ML) algorithms to establish a stroke risk prediction system. Due to different characteristics, such as accessibility, time, and spatial resolution of various wearable-based technologies, strategies of applying different types of wearables to maximize the efficacy of stroke risk prediction are also reported. In addition, based on the various applications of multimodal electroencephalography–functional near-infrared spectroscopy (EEG–fNIRS) on stroke patients, the perspective of using this technique to improve the prediction performance is elaborated. Expected prediction has to be dynamically delivered with high-precision outcomes. There is a need for stroke risk stratification and management to reduce the resulting social and economic burden.

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Ambulatory cardiac bio-signals: From mirage to clinical reality through a decade of progress

Cited by 9 publications

References 116 publications

Smart product-service systems in the healthcare industry: Intelligent connected products and stakeholder communication drive digital health service adoption

Smart product-service systems in the healthcare industry: Intelligent connected products and stakeholder communication drive digital health service adoption

Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis

Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction

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