A review on flexible wearables – Recent developments in non-invasive continuous health monitoring

Kazanskiy, Nikolay L.; Khonina, Svetlana N.; Butt, Muhammad A.

doi:10.1016/j.sna.2023.114993

Cited by 37 publications

(6 citation statements)

References 116 publications

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“…Various wireless technologies are commonly employed for this purpose, providing flexibility, mobility, and real-time data transfer, such as Bluetooth, Wi-Fi, cellular networks (3G, 4G, 5G), Zigbee, Internet of Things protocols, etc. [ 40 ]. The choice of wireless interface depends on factors such as the range required, power consumption, data transfer speed, and the specific use case for remote ECG monitoring.…”

Section: Methodsmentioning

confidence: 99%

Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG)

Krasteva,

Iliev,

Tabakov

2024

Sensors

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Research of novel biosignal modalities with application to remote patient monitoring is a subject of state-of-the-art developments. This study is focused on sonified ECG modality, which can be transmitted as an acoustic wave and received by GSM (Global System for Mobile Communications) microphones. Thus, the wireless connection between the patient module and the cloud server can be provided over an audio channel, such as a standard telephone call or audio message. Patients, especially the elderly or visually impaired, can benefit from ECG sonification because the wireless interface is readily available, facilitating the communication and transmission of secure ECG data from the patient monitoring device to the remote server. The aim of this study is to develop an AI-driven algorithm for 12-lead ECG sonification to support diagnostic reliability in the signal processing chain of the audio ECG stream. Our methods present the design of two algorithms: (1) a transformer (ECG-to-Audio) based on the frequency modulation (FM) of eight independent ECG leads in the very low frequency band (300–2700 Hz); and (2) a transformer (Audio-to-ECG) based on a four-layer 1D convolutional neural network (CNN) to decode the audio ECG stream (10 s @ 11 kHz) to the original eight-lead ECG (10 s @ 250 Hz). The CNN model is trained in unsupervised regression mode, searching for the minimum error between the transformed and original ECG signals. The results are reported using the PTB-XL 12-lead ECG database (21,837 recordings), split 50:50 for training and test. The quality of FM-modulated ECG audio is monitored by short-time Fourier transform, and examples are illustrated in this paper and supplementary audio files. The errors of the reconstructed ECG are estimated by a popular ECG diagnostic toolbox. They are substantially low in all ECG leads: amplitude error (quartile range RMSE = 3–7 μV, PRD = 2–5.2%), QRS detector (Se, PPV > 99.7%), P-QRS-T fiducial points’ time deviation (<2 ms). Low errors generalized across diverse patients and arrhythmias are a testament to the efficacy of the developments. They support 12-lead ECG sonification as a wireless interface to provide reliable data for diagnostic measurements by automated tools or medical experts.

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Section: Methodsmentioning

confidence: 99%

Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG)

Krasteva,

Iliev,

Tabakov

2024

Sensors

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“…Photoplethysmography (PPG) has been widely utilized to acquire blood and vessel related information due to its superior stability, which galvanizes the increasing interests of academia and industry in PPG-based PW sensors, − such as commercialized smart watches. It is reported that wearable smart watches exhibit a substantial market, with an estimated value of USD 978.86 million in 2022 and is anticipated to reach USD 4336.7 million by 2032 . A PPG sensor typically consists of a light source and a photodetector placed snugly against the skin and utilizes the changes in light intensity after transmission or reflection to acquire information related to vascular dynamics (Figure A) .…”

Section: Optical Methods For Pulse Wave Measurementsmentioning

confidence: 99%

Recent Advances in Materials, Devices and Algorithms Toward Wearable Continuous Blood Pressure Monitoring

Li,

Chu,

Chen

et al. 2024

ACS Nano

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“…-High-precision location-based services for personalized healthcare apps [31], fostering overall well-being and care management are some of advancements ICT may introduce in the healthcare context. -6G with the enhanced data rates and lower latency compared to 5G enable realtime remote patient monitoring, enabling swift intervention and proactive health management [32][33][34]. -Internet of Things (IoT) devices and the upcoming Internet of nano bio things play a pivotal role, gathering real-time health data for remote monitoring and quick intervention.…”

Section: Overview Of the Current And Future Ict For Healthcare Servicesmentioning

confidence: 99%

ICT in Nursing and Patient Healthcare Management: Scoping Review and Case Studies

Jayousi,

Barchielli,

Alaimo

et al. 2024

Sensors

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Over the past few decades, Information and Communication Technologies (ICT) have revolutionized the fields of nursing and patient healthcare management. This scoping review and the accompanying case studies shed light on the extensive scope and impact of ICT in these critical healthcare domains. The scoping review explores the wide array of ICT tools employed in nursing care and patient healthcare management. These tools encompass electronic health records systems, mobile applications, telemedicine solutions, remote monitoring systems, and more. This article underscores how these technologies have enhanced the efficiency, accuracy, and accessibility of clinical information, contributing to improved patient care. ICT revolution has revitalized nursing care and patient management, improving the quality of care and patient satisfaction. This review and the accompanying case studies emphasize the ongoing potential of ICT in the healthcare sector and call for further research to maximize its benefits.

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A review on flexible wearables – Recent developments in non-invasive continuous health monitoring

Cited by 37 publications

References 116 publications

Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG)

Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG)

Recent Advances in Materials, Devices and Algorithms Toward Wearable Continuous Blood Pressure Monitoring

ICT in Nursing and Patient Healthcare Management: Scoping Review and Case Studies

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