Recent Advances on Wearable Electronics and Embedded Computing Systems for Biomedical Applications

Scilingo, Enzo Pasquale; Valenza, Gaetano

doi:10.3390/electronics6010012

Cited by 18 publications

(11 citation statements)

References 14 publications

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“…However, besides the important benefits that these technologies bring, such arm-worn systems are seriously affected by movement artefacts. In general, many wearable, long-term cardiac monitoring systems have not gained entry into the cardiovascular monitoring devices market due to clinical approval and certification issues and associated high costs [24]. Nevertheless, focus is being directed to the home environment and to the healthcare for the elderly, where patients generally benefit from increased comfort at home; a factor that may accelerate their recovery.…”

Section: Discussionmentioning

confidence: 99%

Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring

et al. 2017

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Abnormal heart rhythms (arrhythmias) are a major cause of cardiovascular disease and death in Europe. Sudden cardiac death accounts for 50% of cardiac mortality in developed countries; ventricular tachycardia or ventricular fibrillation is the most common underlying arrhythmia. In the ambulatory population, atrial fibrillation is the most common arrhythmia and is associated with an increased risk of stroke and heart failure, particularly in an aging population. Early detection of arrhythmias allows appropriate intervention, reducing disability and death. However, in the early stages of disease arrhythmias may be transient, lasting only a few seconds, and are thus difficult to detect. This work addresses the problem of extracting the far-field heart electrogram signal from noise components, as recorded in bipolar leads along the left arm, using a data driven ECG (electrocardiogram) denoising algorithm based on ensemble empirical mode decomposition (EEMD) methods to enable continuous non-invasive monitoring of heart rhythm for long periods of time using a wrist or arm wearable device with advanced biopotential sensors. Performance assessment against a control denoising method of signal averaging (SA) was implemented in a pilot study with 34 clinical cases. EEMD was found to be a reliable, low latency, data-driven denoising technique with respect to the control SA method, achieving signal-to-noise ratio (SNR) enhancement to a standard closer to the SA control method, particularly on the upper arm-ECG bipolar leads. Furthermore, the SNR performance of the EEMD was improved when assisted with an FFT (fast Fourier transform ) thresholding algorithm (EEMD-fft).

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Section: Discussionmentioning

confidence: 99%

Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring

et al. 2017

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“…The simplification of structure, materials, operations, and architectures of PEMFCs has been the object of intense research in the last decade. The strategies used by researchers to innovate the inflexible components of PEMFC were focused on the developments of soft materials and flexible configurations able to transform traditional into bendable devices for wearable applications, such us customer electronics [73], smart clothing [74] and healthcare [75,76]. Most of the literature working on the fabrication of flexible current collectors and electrodes is focused on the use of highly conductive and stretchable materials, such as metal nanostructures [68][69][70], conductive polymers [64], graphene sheets [67] and CNTs [66,67].…”

Section: Polymer Electrolyte and Zinc-air Fuel Cellmentioning

confidence: 99%

Soft Materials for Wearable/Flexible Electrochemical Energy Conversion, Storage, and Biosensor Devices

et al. 2020

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Next-generation wearable technology needs portable flexible energy storage, conversion, and biosensor devices that can be worn on soft and curved surfaces. The conformal integration of these devices requires the use of soft, flexible, light materials, and substrates with similar mechanical properties as well as high performances. In this review, we have collected and discussed the remarkable research contributions of recent years, focusing the attention on the development and arrangement of soft and flexible materials (electrodes, electrolytes, substrates) that allowed traditional power sources and sensors to become viable and compatible with wearable electronics, preserving or improving their conventional performances.

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“…The detection of these waves over a short period of time (<30 min) has been accomplished with great success and accuracy for acute screening and diagnostics [ 3 , 4 , 5 ]. Early detection of CVDs, on the other hand, necessitates long-term monitoring of ECG signals via ECG electrodes that are connected to wearable devices, mobile phones, smart clothes, and point-of-care (POC) devices in a smart home setting [ 6 ]. Devices used in remote monitoring typically rely on wireless communication and could require extensive power resources [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…A remote system with real-time monitoring for the elderly can provide a timely alarm when patients need urgent help [ 6 , 8 ]. This technology may relieve some of the pressure on hospitals to monitor patients on-site.…”

Section: Introductionmentioning

confidence: 99%

Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach

et al. 2018

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Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B/K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance (CR=6 and PRD=1.88) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.

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Recent Advances on Wearable Electronics and Embedded Computing Systems for Biomedical Applications

Abstract: The application of wearable electronics in the biomedical research and commercial ﬁelds has been gaining great interest over the last several decades.[...]

Cited by 18 publications

References 14 publications

Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring

Data-Driven ECG Denoising Techniques for Characterising Bipolar Lead Sets along the Left Arm in Wearable Long-Term Heart Rhythm Monitoring

Soft Materials for Wearable/Flexible Electrochemical Energy Conversion, Storage, and Biosensor Devices

Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach

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