The unique parameters of graphene (GN)—notably its considerable electron mobility, high surface area, and electrical conductivity—are bringing extensive attention into the wearable technologies. This work presents a novel graphene-based electrode for acquisition electrocardiogram (ECG). proposed was fabricated by coating GN on top metallic layer Ag/AgCl using chemical vapour deposition (CVD) technique. To investigate performance GN-based electrode, two types electrodes were with different...

Graphene (GN), a single layer two-dimensional structure nanomaterial, exhibits exceptional physical, electrical and chemical properties that lead to many applications from electronics biomedicine. The unique parameters of GN, notably its considerable electron mobility, thermal conductivity, high surface area are bringing heightened attention into biomedical applications. This study assesses the recent advances in GN-based biosensors derivatives different areas focus on glucose sensing, DNA...

Continuous and reliable measurements of core body temperature (CBT) are vital for studies on human thermoregulation. Because tympanic membrane directly reflects the carotid artery, it is an accurate non-invasive method to record CBT. However, commercial thermometers lack portability continuous measurements. In this study, graphene inks were utilized increase accuracy from ear by coating platelets lens infrared thermopile sensor. The proposed ear-based device was designed investigating canal...

Abstract Single-molecule research techniques such as patch-clamp electrophysiology deliver unique biological insight by capturing the movement of individual proteins in real time, unobscured whole-cell ensemble averaging. The critical first step analysis is event detection, so called “idealisation”, where noisy raw data are turned into discrete records protein movement. To date there have been practical limitations idealisation; high quality idealisation typically laborious and becomes...

This work proposes the design and evaluation of a wearable mobile ear-based electrocardiogram (ECG) monitoring system using highly electrically conductive material - graphene enabled electrodes. Prolonged physiological is important in diagnosis, chronic diseases, improving training regimes. Current technologies for ECGs alone are acceptable to clinicians or professionals, but alien users. Uncomfortable unfamiliar not efficient obtaining ambulatory data they designed for. Smartphones...

In this paper, we present a wireless Multiple Smart Sensor System (MSSS) in conjunction with smartphone to enable an unobtrusive monitoring of electrocardiogram (ear-lead ECG) integrated multiple sensor system which includes core body temperature and blood oxygen saturation (SpO2) for ambulatory patients. The proposed behind-the-ear device makes the desirable measure ECG data: technically less complex, physically attached non-hair regions, hence more suitable long term use, user friendly as...

Development of automated analysis tools for “single ion channel” recording is hampered by the lack available training data. For machine learning based tools, very large sets are necessary with sample-by-sample point labelled data (e.g., 1 sample every 100microsecond). In an experimental context, such human supervision, and whilst this feasible simple analysis, it infeasible to generate enormous datasets that would be a big approach using hand crafting. work we aimed develop methods simulated...

In this paper, we present a multi-parameter wearable sensor system in conjunction with smartphone to enable real-time unobtrusive monitoring of core body temperature, electrocardiogram (ear-lead ECG), and blood oxygen saturation (SpO2) on ambulatory patients. Clinical research illustrating that continuing accurate measurements temperature (CBT) are crucial investigate human thermoregulation environment during activity. On the other hand, ECG remains mainstay test for primary diagnosis...

Abstract Single-molecule research such as patch-clamp recording delivers unique biological insight by capturing the movement of individual proteins in real time, unobscured whole-cell ensemble averaging. The critical first step analysis is event detection, so called “idealisation”, where noisy raw data are turned into discrete records protein movement. To date there have been practical limitations idealisation; high quality idealisation typically laborious and becomes infeasible subjective...

Development of automated analysis tools for “single ion channel” recording is hampered by the lack available training data. For machine learning based tools, very large sets are necessary with sample-by-sample point labelled data (e.g., 1 sample every 100microsecond). In an experimental context, such human supervision, and whilst this feasible simple analysis, it infeasible to generate enormous datasets that would be a big approach using hand crafting. work we aimed develop methods simulated...

Barrett's oesophagus (BE) is one of the early indicators esophageal cancer. Patients with BE are monitored and undergo ablation therapies to minimise risk, thereby making it eminent identify area precisely. Automated segmentation can help clinical endoscopists assess treat more accurately. Endoscopy imaging include multiple modalities in addition conventional white light (WL) modality. Supervised models require large amount manual annotations incorporating all data variability training data....

Gastrointestinal (GI) cancer precursors require frequent monitoring for risk stratification of patients. Automated segmentation methods can help to assess areas more accurately, and assist in therapeutic procedures or even removal. In clinical practice, addition the conventional white-light imaging (WLI), complimentary modalities such as narrow-band (NBI) fluorescence are used. While, today most approaches supervised only concentrated on a single modality dataset, this work exploits use...