Detection of the Complete ECG Waveform with Woven Textile Electrodes

Fully Screen-Printed and Gentle-to-Skin Wet ECG Electrodes with Compact Wireless Readout for Cardiac Diagnosis and Remote Monitoring

Recent advances in electrocardiogram (ECG) diagnosis and monitoring have triggered a demand for smart and wearable ECG electrodes and readout systems. Here, we report the development of a fully screen-printed gentle-to-skin wet ECG electrode integrated with a scaled-down printed circuit board (PCB) packaged inside a 3D-printed antenna-on-package (AoP). All three components of the wet ECG electrode (i.e., silver nanowire-based conductive part, electrode gel, and adhesive gel) are screen-printed on a flexible plastic substrate and only require 265 times less metal for the conductive part and 176 times less ECG electrode gel than the standard commercial wet ECG electrodes. In addition, our electrically small AoP achieved a maximum read range of 142 m and offers a 4 times larger wireless communication range than the typical commercial chip antenna. The adult volunteers' study results indicated that our system recorded ECG data that correlated well with data from a commercial ECG system and electrodes. Furthermore, in the context of a 12-lead ECG diagnostic system, the fully printed wet ECG electrodes demonstrated a performance similar to that of commercially available wet ECG electrodes while being gentle on the skin. This was confirmed through a blind review method by two cardiology consultants and one family medicine consultant, validating the consistency of the diagnostic information obtained from both electrodes. In conclusion, these findings highlight the potential of fully screen-printed wet ECG electrodes for both monitoring and diagnostic purposes. These electrodes could serve as potential candidates for clinical practice, and the screen-printing method has the capability to facilitate industrial mass production.

Frontiers of Wearable Biosensors for Human Health Monitoring

Wearable biosensors offer noninvasive, real-time, and continuous monitoring of diverse human health data, making them invaluable for remote patient tracking, early diagnosis, and personalized medicine [...].

Modeling the Design Characteristics of Woven Textile Electrodes for long-Term ECG Monitoring

An electrocardiograph records the periodic voltage generated by the heart over time. There is growing demand to continuously monitor the ECG for proactive health care and human performance optimization. To meet this demand, new conductive textile electrodes are being developed which offer an attractive alternative to adhesive gel electrodes but they come with their own challenges. The key challenge with textile electrodes is that the relationship between the manufacturing parameters and the ECG measurement is not well understood, making design an iterative process without the ability to prospectively develop woven electrodes with optimized performance. Here we address this challenge by applying the traditional skin-electrode interface circuit model to woven electrodes by constructing a parameterized model of the ECG system. Then the unknown parameters of the system are solved for with an iterative MATLAB optimizer using measured data captured with the woven electrodes. The results of this novel analysis confirm that yarn conductivity and total conductive area reduce skin electrode impedance. The results also indicate that electrode skin pressure and moisture require further investigation. By closing this gap in development, textile electrodes can be better designed and manufactured to meet the demands of long-term ECG capture.

Design, Characterization, and Performance of Woven Fabric Electrodes for Electrocardiogram Signal Monitoring

Conductive gel needs to be applied between the skin and standard medical electrodes when monitoring electrocardiogram (ECG) signals, but this can cause skin irritation, particularly during long-term monitoring. Fabric electrodes are flexible, breathable, and capable of sensing ECG signals without conductive gel. The objective of this study was to design and fabricate a circular fabric electrode using weaving technology. To optimize the woven fabric electrode, electrodes of different diameter, fabric weave, and weft density were devised, and the AC impedance, open-circuit voltage, and static ECG signal were measured and comprehensively evaluated. Diameter of 4 cm, 12/5 sateen, and weft density of 46 picks/cm were concluded as the appropriate parameters of the fabric electrode. ECG signals in swinging, squatting, and rotating states were compared between the woven fabric electrode and the standard medical electrode. The results showed that the characteristic waveform of the woven fabric electrode with 86.7% improved data was more obvious than that of the standard medical electrode. This work provides reference data that will be helpful for commercializing the integration of fabric electrodes into smart textiles.

Wearable Fetal ECG Monitoring System from Abdominal Electrocardiography Recording

Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus’s health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be applied to fetal heart rate (FHR) monitoring at the home setting. The ECG monitoring system is based on data acquisition circuits, data transmission module, and signal analysis platform, which consists of low input-referred noise, high input impedance, and high resolution. The combination of the adaptive dual threshold (ADT) and the independent component analysis (ICA) algorithm is employed to extract the FECG from the AECG signals. To validate the performance of the proposed system, AECG is recorded and analyzed of pregnant women in three different postures (supine, seated, and standing). The result shows that the proposed system can record the AECG in different postures with good signal quality and high accuracy in fetal ECG and heart rate information. Sensitivity (Se), positive predictive accuracy (PPV), accuracy (ACC), and their harmonic mean (F1) are utilized as the metrics to evaluate the performance of the fetal QRS (fQRS) complexes extraction. The average Se, PPV, ACC, and F1 score are 99.62%, 97.90%, 97.40%, and 98.66% for the fQRS complexes extraction,, respectively. This paper shows the proposed system has a promising application in fetal health monitoring.

Development and Characterization of Embroidery-Based Textile Electrodes for Surface EMG Detection

The interest in wearable devices has expanded to measurement devices for building IoT-based mobile healthcare systems and sensing bio-signal data through clothing. Surface electromyography, called sEMG, is one of the most popular bio-signals that can be applied to health monitoring systems. In general, gel-based (Ag/AgCl) electrodes are mainly used, but there are problems, such as skin irritation due to long-time wearing, deterioration of adhesion to the skin due to moisture or sweat, and low applicability to clothes. Hence, research on dry electrodes as a replacement is increasing. Accordingly, in this study, a textile-based electrode was produced with a range of electrode shapes, and areas were embroidered with conductive yarn using an embroidery technique in the clothing manufacturing process. The electrode was applied to EMG smart clothing for fitness, and the EMG signal detection performance was analyzed. The electrode shape was manufactured using the circle and wave type. The wave-type electrode was more morphologically stable than the circle-type electrode by up to 30% strain, and the electrode shape was maintained as the embroidered area increased. Skin-electrode impedance analysis confirmed that the embroidered area with conductive yarn affected the skin contact area, and the impedance decreased with increasing area. For sEMG performance analysis, the rectus femoris was selected as a target muscle, and the sEMG parameters were analyzed. The wave-type sample showed higher EMG signal strength than the circle-type. In particular, the electrode with three lines showed better performance than the fill-type electrode. These performances operated without noise, even with a commercial device. Therefore, it is expected to be applicable to the manufacture of electromyography smart clothing based on embroidered electrodes in the future.

Smart Electronic Textiles for Wearable Sensing and Display

Increasing demand of using everyday clothing in wearable sensing and display has synergistically advanced the field of electronic textiles, or e-textiles. A variety of types of e-textiles have been formed into stretchy fabrics in a manner that can maintain their intrinsic properties of stretchability, breathability, and wearability to fit comfortably across different sizes and shapes of the human body. These unique features have been leveraged to ensure accuracy in capturing physical, chemical, and electrophysiological signals from the skin under ambulatory conditions, while also displaying the sensing data or other immediate information in daily life. Here, we review the emerging trends and recent advances in e-textiles in wearable sensing and display, with a focus on their materials, constructions, and implementations. We also describe perspectives on the remaining challenges of e-textiles to guide future research directions toward wider adoption in practice.

Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

Heart diseases such as myocardial ischemia (MI) are the main causes of human death. The prediction of MI and arrhythmia is an effective method for the early detection, diagnosis, and treatment of heart disease. For the rapid detection of arrhythmia and myocardial ischemia, the electrocardiogram (ECG) is widely used in clinical diagnosis, and its detection equipment and algorithm are constantly optimized. This paper introduces the current progress of portable ECG monitoring equipment, including the use of polymer material sensors and the use of deep learning algorithms. First, it introduces the latest portable ECG monitoring equipment and the polymer material sensor it uses and then focuses on reviewing the progress of detection algorithms. We mainly introduce the basic structure of existing deep learning methods and enumerate the internationally recognized ECG datasets. This paper outlines the deep learning algorithms used for ECG diagnosis, compares the prediction results of different classifiers, and summarizes two existing problems of ECG detection technology: imbalance of categories and high computational overhead. Finally, we put forward the development direction of using generative adversarial networks (GAN) to improve the quality of the ECG database and lightweight ECG diagnosis algorithm to adapt to portable ECG monitoring equipment.