High-Adhesive Flexible Electrodes and Their Manufacture: A Review

Stretchable and Conductive Cellulose/Conductive Polymer Composite Films for On-Skin Strain Sensors

Conductive composite materials have attracted considerable interest of researchers for application in stretchable sensors for wearable health monitoring. In this study, highly stretchable and conductive composite films based on carboxymethyl cellulose (CMC)-poly (3,4-ethylenedioxythiopehe):poly (styrenesulfonate) (PEDOT:PSS) (CMC-PEDOT:PSS) were fabricated. The composite films achieved excellent electrical and mechanical properties by optimizing the lab-synthesized PEDOT:PSS, dimethyl sulfoxide, and glycerol content in the CMC matrix. The optimized composite film exhibited a small increase of only 1.25-fold in relative resistance under 100% strain. The CMC-PEDOT:PSS composite film exhibited outstanding mechanical properties under cyclic tape attachment/detachment, bending, and stretching/releasing tests. The small changes in the relative resistance of the films under mechanical deformation indicated excellent electrical contacts between the conductive PEDOT:PSS in the CMC matrix, and strong bonding strength between CMC and PEDOT:PSS. We fabricated highly stretchable and conformable on-skin sensors based on conductive and stretchable CMC-PEDOT:PSS composite films, which can sensitively monitor subtle bio-signals and human motions such as respiratory humidity, drinking water, speaking, skin touching, skin wrinkling, and finger bending. Because of the outstanding electrical properties of the films, the on-skin sensors can operate with a low power consumption of only a few microwatts. Our approach paves the way for the realization of low-power-consumption stretchable electronics using highly stretchable CMC-PEDOT:PSS composite films.

Using Minimum Redundancy Maximum Relevance Algorithm to Select Minimal Sets of Heart Rate Variability Parameters for Atrial Fibrillation Detection

Heart rate is quite regular during sinus (normal) rhythm (SR) originating from the sinus node. In contrast, heart rate is usually irregular during atrial fibrillation (AF). Complete atrioventricular block with an escape rhythm, ventricular pacing, or ventricular tachycardia are the most common exceptions when heart rate may be regular in AF. Heart rate variability (HRV) is the variation in the duration of consecutive cardiac cycles (RR intervals). We investigated the utility of HRV parameters for automated detection of AF with machine learning (ML) classifiers. The minimum redundancy maximum relevance (MRMR) algorithm, one of the most effective algorithms for feature selection, helped select the HRV parameters (including five original), best suited for distinguishing AF from SR in a database of over 53,000 60 s separate electrocardiogram (ECG) segments cut from longer (up to 24 h) ECG recordings. HRV parameters entered the ML-based classifiers as features. Seven different, commonly used classifiers were trained with one to six HRV-based features with the highest scores resulting from the MRMR algorithm and tested using the 5-fold cross-validation and blindfold validation. The best ML classifier in the blindfold validation achieved an accuracy of 97.2% and diagnostic odds ratio of 1566. From all studied HRV features, the top three HRV parameters distinguishing AF from SR were: the percentage of successive RR intervals differing by at least 50 ms (pRR50), the ratio of standard deviations of points along and across the identity line of the Poincare plots, respectively (SD2/SD1), and coefficient of variation—standard deviation of RR intervals divided by their mean duration (CV). The proposed methodology and the presented results of the selection of HRV parameters have the potential to develop practical solutions and devices for automatic AF detection with minimal sets of simple HRV parameters. Using straightforward ML classifiers and the extremely small sets of simple HRV features, always with pRR50 included, the differentiation of AF from sinus rhythms in the 60 s ECGs is very effective.

Advances in Soft and Dry Electrodes for Wearable Health Monitoring Devices

Electrophysiology signals are crucial health status indicators as they are related to all human activities. Current demands for mobile healthcare have driven considerable interest in developing skin-mounted electrodes for health monitoring. Silver-Silver chloride-based (Ag-/AgCl) wet electrodes, commonly used in conventional clinical practice, provide excellent signal quality, but cannot monitor long-term signals due to gel evaporation and skin irritation. Therefore, the focus has shifted to developing dry electrodes that can operate without gels and extra adhesives. Compared to conventional wet electrodes, dry ones offer various advantages in terms of ease of use, long-term stability, and biocompatibility. This review outlines a systematic summary of the latest research on high-performance soft and dry electrodes. In addition, we summarize recent developments in soft materials, biocompatible materials, manufacturing methods, strategies to promote physical adhesion, methods for higher breathability, and their applications in wearable biomedical devices. Finally, we discuss the developmental challenges and advantages of various dry electrodes, while suggesting research directions for future studies.