Long-term Electrocardiogram Monitoring Research Articles

Efficacy of Various Dry Electrode-Based ECG Sensors: A Review.

Long-term electrocardiogram (ECG) monitoring is crucial for detecting and diagnosing cardiovascular diseases (CVDs). Monitoring cardiac health and activities using efficient, noninvasive, and cost-effective techniques such as ECG can be vital for the early detection of different CVDs. Wet electrode-based traditional ECG techniques come with unavoidable limitations of the altered quality of ECG signals caused by gel volatilization and unwanted noise followed by dermatitis. The limitation related to the wet electrodes for long-term ECG monitoring in static and dynamic postures reminds us of the urgency of a suitable substitute. Dry electrodes promise long-term ECG monitoring with the potential for significant noise reduction. This review discusses traditional and alternative techniques to record ECG in terms of meeting the efficient detection of CVDs by conducting a detailed analysis of different types of dry electrodes along with materials (substrate, support, matrix, and conductive part) used for fabrication, followed by the number of human subjects they have been used for validation. The degradation of these electrodes has also been discussed briefly. This review finds a need for more validation on a sufficient number of subjects and the issue of cost and noise hindering the commercialization of these dry electrodes.

Enhancement of single-lead dry-electrode ECG through wavelet denoising

Neonatal electrocardiogram (ECG) monitoring is an important diagnostic tool for identifying cardiac issues in infants at birth. Long-term remote neonatal dry-electrode ECG monitoring solutions can be an additional step for preventive healthcare measures. In these solutions, power and computationally efficient embedded signal processing techniques for denoising newborn ECGs can assist in increasing neonatal medical wearable time. Wavelet denoising is an appropriate denoising mechanism with low computational complexity that can be implemented on embedded microcontrollers for long-term remote ECG monitoring. Discrete wavelet transform (DWT) denoising for neonatal dry-electrode ECG using different wavelet families is investigated. The wavelet families and mother wavelets used include Daubechies (db1, db2, db3, db4, and db6), symlets (sym5), and coiflets (coif5). Different levels of added white Gaussian noise (AWGN) were added to 19 newborn ECG signals, and denoising was performed to select the appropriate wavelets for neonatal dry-electrode ECG. The selected wavelets then undergo real noise additions of baseline wander and electrode motion to determine their robustness and accuracy. Signal-to-noise ratio (SNR), mean squared error (MSE), and power spectral density (PSD) are used to examine denoising performance. db1, db2, and db3 wavelets are eliminated from analysis where the 30 dB AWGN led to negative SNR improvement for at least one newborn ECG, removing important ECG information. db4 and sym5 are eliminated from selection due to their different waveform morphology compared to the dry-electrode newborn ECG’s QRS complex. db6 and coif5 are selected due to their highest SNR improvement and lowest MSE of 6.26 × 10−6 and 1.65 × 10−7 compared to other wavelets, respectively. Their wavelet shapes are more like a newborn ECG’s QRS morphology, validating their selection. db6 and coif5 showed similar denoising performance, decreasing electrode motion and baseline wander noisy ECG signals by 10 dB and 14 dB, respectively. Further denoising of inherent dry-electrode noise is observed. DWT with coif5 or db6 wavelets is appropriate for denoising newborn dry-electrode ECGs for long-term neonatal dry-electrode ECG monitoring solutions under different noise types. Their similarity to newborn dry-electrode ECGs yields accurate and robust reconstructed denoised newborn dry-electrode ECG signals.

A combination of logical judging circuit and water-resistant ultrathin film PEDOT: PSS electrode for noninvasive ECG measurement

Heart disease-related deaths have increased in recent decades, with most patients dying of sudden cardiac arrest. In such instances, the effect of regular electrocardiogram (ECG) measurements is minimal. Therefore, long-term ECG monitoring has become increasingly important. In this paper, we report a non-adhesive high accuracy ECG monitoring system that can be used in various scenarios without interfering with daily activities. The ECG ultra-thin film electrode is made by water-resistant material based on poly(3,4-ethylenedioxythiophene) poly(4-styrenesulfonate) (PEDOT: PSS) electrode doped with ethylene glycol (EG) and xylitol, to improve the noise signal caused by sweat. The optimal ratio of the three ingredients of PEDOT: PSS/xylitol/EG was determined experimentally to accommodate the ECG monitoring. By using the proposed selectively closed multi-channel single-lead logic circuit, the noise of ECG signal received from the proposed film electrode can be successfully reduced during broad-area electrode measurements, thus to improve ECG measurement accuracy.

Gender and age characteristics and atrial fibrillation during long-term telemonitoring of the ECG

The aim – to evaluate the possibilities of long-term patch monitoring of the electrocardiogram (ECG) to determine gender and age characteristics of the frequency of detection of atrial fibrillation (AF). Materials and methods. A total of 1,123 patients participated in this 2-year (2021–2023) retrospective multicenter study. Long-term ECG monitoring was performed at the referral of internists and cardiologists of 30 health care institutions according to various clinical indications for this study. For long-term monitoring of the ECG, the innovative device «Mawi Heart Patch» was used – a disposable two-channel device with the possibility of continuous recording of the ECG for up to 7 days. The primary analysis of the records was carried out using Cardio.ai artificial intelligence algorithms, re-decryption and validation by certified doctors. Results. The average age of the study participants was (50.2 ± 16.65) years. There were 599 (53.3 %) women, 524 (46.6 %) men. The average time of ECG monitoring was (81.03 ± 18.5) hours, 74.35 % of the participants had continuous ECG monitoring for 3 days, 20.75 % for 4 days, and 2.58 % for 5 days. In total, AF was diagnosed in 7.93 % of patients, in particular, women and men were 6.51 % and 9.54 % respectively (p > 0.05). In elderly patients, AF was diagnosed statistically significantly more often than in middle-aged patients (18.5 and 3.6 %, respectively, p ˂ 0.001). Among all age groups, the highest frequency of detection of AF was in participants over 75 years old, compared to young (28.8 % vs 3.6 %, p ˂ 0.001) and middle-aged patients (28.8 vs 18.5 %, p ˂ 0.05). Conclusions. The study did not reveal a gender difference in the frequency of diagnosing AF. In patients over 50 years, a significant increase in the frequency of detection of AF was recorded, which tends to increase in the following age periods and reaches a maximum in long-livers (over 90 years). The use of long-term patch ECG monitoring in patients of older age groups expands diagnostic possibilities and makes it possible to prevent complications in time.

IRIDIA-AF, a large paroxysmal atrial fibrillation long-term electrocardiogram monitoring database

Atrial fibrillation (AF) is the most common sustained heart arrhythmia in adults. Holter monitoring, a long-term 2-lead electrocardiogram (ECG), is a key tool available to cardiologists for AF diagnosis. Machine learning (ML) and deep learning (DL) models have shown great capacity to automatically detect AF in ECG and their use as medical decision support tool is growing. Training these models rely on a few open and annotated databases. We present a new Holter monitoring database from patients with paroxysmal AF with 167 records from 152 patients, acquired from an outpatient cardiology clinic from 2006 to 2017 in Belgium. AF episodes were manually annotated and reviewed by an expert cardiologist and a specialist cardiac nurse. Records last from 19 hours up to 95 hours, divided into 24-hour files. In total, it represents 24 million seconds of annotated Holter monitoring, sampled at 200 Hz. This dataset aims at expanding the available options for researchers and offers a valuable resource for advancing ML and DL use in the field of cardiac arrhythmia diagnosis.

A Low-Power Wireless System for Predicting Early Signs of Sudden Cardiac Arrest Incorporating an Optimized CNN Model Implemented on NVIDIA Jetson.

The survival rate for sudden cardiac arrest (SCA) is low, and patients with long-term risks of SCA are not adequately alerted. Understanding SCA's characteristics will be key to developing preventive strategies. Many lives could be saved if SCA's early onset could be detected or predicted. Monitoring heart signals continuously is essential for diagnosing sporadic cardiac dysfunction. An electrocardiogram (ECG) can be used to continuously monitor heart function without having to go to the hospital. A zeolite-based dry electrode can provide safe on-skin ECG acquisition while the subject is out-of-hospital and facilitate long-term monitoring. To the ECG signal, a low-power 1 μW read-out circuit was designed and implemented in our prior work. However, having long-term ECG monitoring outside the hospital, i.e., high battery life, and low power consumption while transmission and reception of ECG signal are crucial. This paper proposes a prototype with a 10-bit resolution ADC and nRF24L01 transceivers placed 5 m apart. The system uses the 2.4 GHz worldwide ISM frequency band with GFSK modulation to wirelessly transmit digitized ECG bits at 250 kbps data rate to a physician's computer (or similar) for continuous monitoring of ECG signals; the power consumption is only 11.2 mW and 4.62 mW during transmission and reception, respectively, with a low bit error rate of ≤0.1%. Additionally, a subject-wise cross-validated, three-fold, optimized convolutional neural network (CNN) model using the Physionet-SCA dataset was implemented on NVIDIA Jetson to identify the irregular heartbeats yielding an accuracy of 89% with a run time of 5.31 s. Normal beat classification has an F1 score of 0.94 and a ROC score of 0.886. Thus, this paper integrates the ECG acquisition and processing unit with low-power wireless transmission and CNN model to detect irregular heartbeats.

Multi-task cascaded assessment of signal quality for long-term single-lead ECG monitoring

Background and objectivesIn long-term electrocardiogram (ECG) monitoring, the use of wearable devices for signal acquisition inevitably introduces noise, which could reduce the diagnostic capability. In order to improve the efficiency of signal analysis and the accuracy of clinical diagnosis, it is necessary to automatically evaluate the signal quality of ambulatory ECG. In this study, a novel multi-task cascaded method is proposed for signal quality assessment. MethodsBased on single-lead ECG, various signal quality indices were derived to assess three classes of signal qualities (clear ECG ideal for full wave analysis, mildly contaminated ECG available for ventricular activity-based analysis, severely contaminated ECG difficult to be analyzed) through a two-step classification corresponding to different tasks. The optimized subsets of quality indices were selected with a criterion of maximal-mean-minimal-variance. Different machine learning models were then trained and tested on public datasets. ResultsThe results show that random forest using the optimal feature subsets had the best performance, with the overall classification accuracy of 93.76%, 95.13% and 99.61% on the three unknown test sets, respectively. Meanwhile, for the identification of acceptable and unacceptable signals in the first task, the AUC on the test sets reached 0.9967, 0.9959 and 1.0000, respectively. ConclusionsThis paper presents a novel signal quality assessment framework, which could provide helpful feedback for ECG processing in specific application scenarios. It is expected to greatly promote accurate and efficient ECG-based diagnosis in long-term monitoring.

Value of baseline characteristics in the risk prediction of atrial fibrillation.

Atrial fibrillation (AF) is prone to heart failure and stroke. Early management can effectively reduce the stroke rate and mortality. Current clinical guidelines screen high-risk individuals based solely on age, while this study aims to explore the possibility of other AF risk predictors. A total of 18,738 elderly people (aged over 60 years old) in Chinese communities were enrolled in this study. The baseline characteristics were mainly based on the diagnosis results of electrocardiogram (ECG) machine during follow up, accompanied by some auxiliary physical examination basic data. After the analysis of both independent and combined baseline characteristics, AF risk predictors were obtained and prioritized according to the results. Independent characteristics were studied from three aspects: Chi-square test, Mann-Whitney U test and Cox univariate regression analysis. Combined characteristics were studied from two aspects: machine learning models and Cox multivariate regression analysis, and the former was combined with recursive feature elimination method and voting decision. The resulted optimal combination of risk predictors included age, atrial premature beats, atrial flutter, left ventricular hypertrophy, hypertension and heart disease. Patients diagnosed by short-time ECG machines with the occurrence of the above events had a higher probability of AF episodes, who are suggested to be included in the focus of long-term ECG monitoring or increased screening density. The incidence of risk predictors in different age ranges of AF patients suggests differences in age-specific patient management. This can help improve the detection rate of AF, standardize the management of patients, and slow down the progression of AF.

Removal of Insertable Cardiac Monitor for Cryptogenic Stroke: A Technical Note.

Insertable cardiac monitors (ICM) allow continuous long-term electrocardiogram monitoring and the detection of paroxysmal atrial fibrillation (PAF) in patients with cryptogenic stroke (CS). Several years have passed since ICM was indicated for CS, and many stroke neurologists will experience cases in which ICM removal is required. As a standard protocol, reincision of the wound at the time of implantation has been proposed by ICM brands. However, it may be difficult due to adhesions of subcutaneous tissue, migration of the device from its original position, and the capsule formed around the device. Our objective is to describe simple alternative techniques for successful ICM removal. From December 2016 to September 2021, 37 patients with CS underwent ICM removal at our institution. The device was removed through an incision directly above the proximal end of the device, perpendicular to the wound at the time of ICM implantation. The subcutaneous tissue was removed bluntly using forceps along the edges of the proximal end of the device. When a capsule was attached to the device, we cut the capsule with the blade to release the device. Once the device was visible, the proximal end of the device was grasped with forceps, and the device was pulled from the pocket with gentle traction. All patients undergoing ICM removal received a systematic check for wound dehiscence, wound infection, bleeding, and tissue ischemia at an outpatient examination of 1 week. The 37 patients who underwent removal of ICM were retrospectively reviewed in the medical record and analyzed for procedural success, intraoperative complications, and wound course at one week. All patients achieved procedural success. There were no intraoperative complications, wound dehiscence, bleeding, or skin ischemia at one week postoperatively. The reasons for removal were battery depletion in 65%, early removal before battery life after PAF detection in 32%, and exposure to the body surface in 3%. The devices removed were 62% Reveal LINQ (Medtronic, Minneapolis), 30% Confirm Rx (Abbott, Illinois), and 8% BioMonitor 2 (BIOTRONIK, Berlin), indicating that our method is effective regardless of model. We describe a simple technique for ICM removal for CS that is safe, reliable, and potentially effective in wound healing.

A PDMS-based microneedle array electrode for long-term ECG recording.

To acquire high-quality electrocardiogram (ECG) signals, traditional Ag/AgCl wet electrodes used together with conductive gel can effectively reduce electrode-skin interface impedance (EII) in a short term. However, their weaknesses of poor flexibility and instability can no longer meet the long-term monitoring requirements of intelligent wearable devices. Owing to the flexible dry electrode without conductive gel, it is a good choice to solve the critical problem on drying-out of conductive gel. Therefore, we develop a flexible microneedle array electrode (FMAE) based on polydimethylsiloxane (PDMS) substrate, which obtains reliable bioelectrical signals by way of penetrating into the stratum corneum (SC) of the skin. The fabrication process, including silicon mold, twice PDMS shape-transferring and encapsulation, has advantages of low cost, repeatable production and good biocompatibility. Afterwards, by comparing the performance with different electrodes, impedance test results indicate that the impedance of FMAE are smaller and more stable, and ECG tests in long term and at resting/jogging states also verify that FMAE can obtain durable, stable and reliable signals. In conclusion, FMAE is promising in long-term ECG monitoring.

A Near-Sensor ECG Delineation and Arrhythmia Classification System

Cardiovascular disease (CVD) has become the leading cause of death worldwide. As a widely used method for diagnosing CVD, currently electrocardiogram (ECG) monitoring tends to be implemented in wearable devices. This paper presents the prototype an ECG delineation and arrhythmia classification (EDAC) system suitable for wearable ECG biosensors. The proposed EDAC system is intended to be implemented after the electrodes and the analog front-end circuit, and its aim is signal processing at a low hardware overhead. The system consists of a Delta-modulator-based analog-to-feature converter (AFC), a corresponding ECG detection/delineation/feature extraction algorithm (DDF), an automatic gain controller (AGC) block, and a patient-dependent linear kernel support vector machine (SVM) classifier. The AFC converts the input analog signal into digital data of the slope and slope variation of the input signal, which is then used for detecting QRS complexes, localizing the fiducial points, and extracting the feature vectors for each heartbeat in the DDF block. At the same time, the AGC sends out a gain control signal based on the detected QRS complex to adjust the gain of the front-end amplifier. Finally, the SVM block performs arrhythmia classification. The EDAC system performance is evaluated using the MIT-BIH arrhythmia database. The system achieves 0.88% (0.93%), 99.1% (99.1%), 87.0% (92.8%), 99.6% (99.5%), and 89.3% (92.9%) in F1 score, accuracy, sensitivity, specificity, and positive predictive values of the supraventricular ectopic beats (ventricular ectopic beats) versus normal heartbeats classification while maintaining a low power dissipation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.66~\mu \text{W}$ </tex-math></inline-formula> at 1 kHz operating frequency in the front-end AFC block). The proposed system is attractive to future wearable long-term ECG monitoring biosensors.

Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring.

The paper analyses the autonomy of a wireless body sensor that continuously measures the potential difference between two proximal electrodes on the skin, primarily used for measuring an electrocardiogram (ECG) when worn on the torso. The sensor is powered by a small rechargeable battery and is designed for extremely low power use. However, the autonomy of the sensor, regarding its power consumption, depends significantly on the measurement quality selection, which directly influences the amount of data transferred. Therefore, we perform an in-depth analysis of the power consumption sources, particularly those connected with the Bluetooth Low Energy (BLE) communication protocol, in order to model and then tune the autonomy of the wireless low-power body sensor for long-term ECG monitoring. Based on the findings, we propose two analytical models for power consumption: one for power consumption estimation in idle mode and the other one for power estimation in active mode. The proposed models are validated with the measured power consumption of the ECG sensor at different ECG sensor settings, such as sampling rate and transmit power. The proposed models show a good fit to the measured power consumption at different ECG sensor sampling rates. This allows for power consumption analysis and sensor autonomy predictions for different sensor settings. Moreover, the results show that the transmit power has a negligible effect on the sensor autonomy in the case of streaming data with high sampling rates. The most energy can be saved by lowering the sampling rate with suitable connection interval and by packing as much data as possible in a single BLE packet.

Dry textile electrode for ambulatory monitoring after catheter ablation of atrial fibrillation: A pilot study of simultaneous comparison to the Holter electrocardiogram

Background: Holter electrocardiogram (ECG) is the gold standard for ambulatory monitoring of atrial fibrillation (AF) but it is insufficient because of its limited recording time. Although several consumer ECG devices provide longer recording time, they generally do not undergo the regulatory process for medical use. Furthermore, current medical-grade devices for longer ECG monitoring are not continuous or too invasive for AF monitoring.A wearable ECG with a medical-grade dry textile electrode is a promising technology to remedy this limitation. This pilot study aimed to simultaneously compare the wearable and Holter ECGs for ambulatory monitoring in a clinical setting. Methods: This prospective observational study enrolled 18 patients who underwent AF ablation. One day after AF ablation, ambulatory ECG was obtained for three hours simultaneously using both the wearable and Holter ECG devices. Automatic ECG interpretations between devices were compared with correlation and agreement analyses. Results: Simultaneous ECG monitoring demonstrated a comparable analysis time and total heart beats between the two devices. Almost complete correlation and agreement were also demonstrated in all clinically relevant testing aspects except in R-wave amplitude (r = 0.743, p < .001). AF was detected in three patients. AF duration was the same in both ECG devices in two patients with continuous AF. In the remaining patient with intermittent AF, AF duration was shortened by 0.6% with the wearable ECG as compared to that with the Holter ECG. Conclusions: Simultaneous ECG comparison revealed a high consistency between the wearable and Holter ECG devices. The results of this study warrant further clinical studies for long-term monitoring of ambulatory ECG after AF ablation.

Capabilities of long-term ECG monitoring in patients with myocardial infarction: the longer, the better?

Background. In modern cardiology, 24-hour electrocardiogram (ECG) monitoring has a high diagnostic value, but this method has a number of disadvantages in detecting episodes of unstable life-threatening arrhythmias. An increase in ECG monitoring duration allows expanding the possibilities of diagnosing life-threatening arrhythmias.Objective. To study the possibilities of long-term ECG monitoring (48–120 hours) in the detection of life-threatening arrhythmic events and parameters of myocardial electrical instability in patients with ST-segment elevation myocardial infarction (STEMI).Design and methods. The study included 71 STEMI patients. All patients from the 4th day of STEMI underwent multi-day ECG monitoring in 3 leads using a telemetric ECG recording complex with an average recording duration of 90.4 ± 30.2 hours. The analysis of episodes of myocardial ischemia, rhythm and conduction disturbances, turbulence and heart rate variability, late ventricular potentials and dispersion of the QT interval within 5 days was carried out.Results. Long-term monitoring allowed detecting high-grade ventricular extrasystoles. Analysis of episodes of myocardial ischemia in the postinfarction period revealed significant differences in the data of 120h-ECG monitoring in comparison with 24h-ECG. Multiday ECG monitoring made it possible to detect dysfunction of the autonomic regulation of cardiac activity in patients 2 times more often.Conclusion. A comprehensive assessment of the possibilities of multi-day ECG monitoring is a promising direction in predicting severe arrhythmias in patients in the postinfarction period.

ESC Working Group on e-Cardiology Position Paper: accuracy and reliability of electrocardiogram monitoring in the detection of atrial fibrillation in cryptogenic stroke patients: In collaboration with the Council on Stroke, the European Heart Rhythm Association, and the Digital Health Committee.

The role of subclinical atrial fibrillation as a cause of cryptogenic stroke is unambiguously established. Long-term electrocardiogram (ECG) monitoring remains the sole method for determining its presence following a negative initial workup. This position paper of the European Society of Cardiology Working Group on e-Cardiology first presents the definition, epidemiology, and clinical impact of cryptogenic ischaemic stroke, as well as its aetiopathogenic association with occult atrial fibrillation. Then, classification methods for ischaemic stroke will be discussed, along with their value in providing meaningful guidance for further diagnostic efforts, given disappointing findings of studies based on the embolic stroke of unknown significance construct. Patient selection criteria for long-term ECG monitoring, crucial for determining pre-test probability of subclinical atrial fibrillation, will also be discussed. Subsequently, the two major classes of long-term ECG monitoring tools (non-invasive and invasive) will be presented, with a discussion of each method's pitfalls and related algorithms to improve diagnostic yield and accuracy. Although novel mobile health (mHealth) devices, including smartphones and smartwatches, have dramatically increased atrial fibrillation detection post ischaemic stroke, the latest evidence appears to favour implantable cardiac monitors as the modality of choice; however, the answer to whether they should constitute the initial diagnostic choice for all cryptogenic stroke patients remains elusive. Finally, institutional and organizational issues, such as reimbursement, responsibility for patient management, data ownership, and handling will be briefly touched upon, despite the fact that guidance remains scarce and widespread clinical application and experience are the most likely sources for definite answers.

An Optical Implant for Biotelemetry: Design, In Vivo Verification, and Challenges

This article introduces a novel optical wireless framework that realizes linternet of Things (IoT) biotelemetry. For a clinical application, an implantable medical device (IMD) design is developed to capture in-body electrocardiogram (ECG) and transmit it through optical wireless communication to remote monitoring equipment. To achieve sub-milliwatt-level power consumption, a novel energy-saving strategy dedicated to the optical implants is implemented. Unlike existing RF-based IMDs, the optical IMD design allows seamless long-term ECG monitoring with limited battery capacity, which elicits considerable diagnostic accuracy enhancement. In vivo experiments with canine models verify the viability of the IMD prototype for successful real-time biotelemetry at the expense of only 384 µW along with its detection capability for various types of arrhythmic patterns. Important benefits of the optical IMD approach include the harmlessness of ultra-low-power optical emission to the living body as compared to the RF radiation of typical wireless IMDs prone to heat-generating dermal absorption, ending up with undesirable physiologic impacts. The optical IMD approach paves the way for an attractive IoT solution of biotelemetry applications.

Non-Contact Electrocardiograms Acquisition Method Based on Capacitive Coupling

Electrocardiogram (ECG) monitoring is an essential method for medical diagnosis [1], [2]. With the development of technology, better quality physiological signals can be easily recorded [3]. Long-term monitoring of electrocardiographic activity is helpful to monitor potential cardiac dysfunction. The Ag/Agcl electrode with conductive gel can record ECG signals with good signal quality and has been widely used in hospitals. However, the gel needs to be replaced regularly to achieve good signal quality because the quality of the signal deteriorates due to the gel drying over a long time. In addition, wet electrodes may not be suitable for long-term ECG monitoring because they may cause skin redness, swelling, and discomfort, especially for those patients with allergic reactions. Also, direct contact between electrodes and the human body is not applicable in many cases, such as skin injury patients, infants, etc. The goals of disease monitoring technology based on physiological signals are to reduce the amount invasive of equipment, increase the comfort of patients and improve the quality of life while pursuing high-quality signals.

Non-contact electrocardiogram measuring method based on capacitance coupling electrodes with ultra-high input impedance.

With the development of remote cardiac healthcare, wearable devices for electrocardiogram (ECG) monitoring are stringent requirements to cope with this rapid growth of demands. Due to the advantages of no-contact ECG measuring methods in safety, convenience, and comfortableness, it is more suitable for wearable long-term ECG monitoring than the conventional Ag/AgCl electrodes. The capacitance coupling printed circuit board (PCB) electrode with ultra-high input impedance proposed in this paper can realize non-contact ECG measurement through a multi-layer insulating medium. Then, an eight-channel ECG signal processing circuit is also designed and fabricated. In addition, the following important performance properties of the non-contact ECG measuring system, such as the input impedance, the phase-frequency characteristic, the amplitude-frequency characteristic, the coupling coefficient, and the input short-circuit input noise, were all experimentally measured. The synchronous comparison between the Ag/AgCl electrode and the PCB electrode was also conducted to verify the accuracy of the non-contact measuring method. Finally, the influence of the lead positions, coupling medium parameters, and the body motion states was also experimentally studied. The results demonstrate that the proposed non-contact ECG measuring method based on capacitance coupling PCB electrodes can effectively collect the main components of ECG signals and cardiac rhythm in various situations.

Dry textile electrode for ambulatory monitoring after catheter ablation of atrial fibrillation: A pilot study of simultaneous comparison to the Holter electrocardiogram

Background: Holter electrocardiogram (ECG) is the gold standard for ambulatory monitoring of atrial fibrillation (AF) but it is insufficient because of its limited recording time. Although several consumer ECG devices provide longer recording time, they generally do not undergo the regulatory process for medical use. Furthermore, current medical-grade devices for longer ECG monitoring are not continuous or too invasive for AF monitoring. A wearable ECG with a medical-grade dry textile electrode is a promising technology to remedy this limitation. This pilot study aimed to simultaneously compare the wearable and Holter ECGs for ambulatory monitoring in a clinical setting. Methods: This prospective observational study enrolled 18 patients who underwent AF ablation. One day after AF ablation, ambulatory ECG was obtained for three hours simultaneously using both the wearable and Holter ECG devices. Automatic ECG interpretations between devices were compared with correlation and agreement analyses. Results: Simultaneous ECG monitoring demonstrated a comparable analysis time and total heart beats between the two devices. Almost complete correlation and agreement were also demonstrated in all clinically relevant testing aspects except in R-wave amplitude (r = 0.743, p < .001). AF was detected in three patients. AF duration was the same in both ECG devices in two patients with continuous AF. In the remaining patient with intermittent AF, AF duration was shortened by 0.6% with the wearable ECG as compared to that with the Holter ECG. Conclusions: Simultaneous ECG comparison revealed a high consistency between the wearable and Holter ECG devices. The results of this study warrant further clinical studies for long-term monitoring of ambulatory ECG after AF ablation.

Dry textile electrode for ambulatory monitoring after catheter ablation of atrial fibrillation: A pilot study of simultaneous comparison to the Holter electrocardiogram.

Background: Holter electrocardiogram (ECG) is the gold standard for ambulatory monitoring of atrial fibrillation (AF) but it is insufficient because of its limited recording time. A wearable ECG with a medical-grade dry textile electrode is a promising technology to remedy this limitation. This pilot study aimed to simultaneously compare the wearable and Holter ECGs for ambulatory monitoring in a clinical setting. Methods: This prospective observational study enrolled 18 patients who underwent AF ablation. One day after AF ablation, ambulatory ECG was obtained for three hours simultaneously using both the wearable and Holter ECG devices. Automatic ECG interpretations between devices were compared with correlation and agreement analyses. Results: Simultaneous ECG monitoring demonstrated a comparable analysis time and total heart beats between the two devices. Almost complete correlation and agreement were also demonstrated in all clinically relevant testing aspects except in R-wave amplitude (r = 0.743, p < .001). AF was detected in three patients. AF duration was the same in both ECG devices in two patients with continuous AF. In the remaining patient with intermittent AF, AF duration was shortened by 0.6% with the wearable ECG as compared to that with the Holter ECG. Conclusions: Simultaneous ECG comparison revealed a high consistency between the wearable and Holter ECG devices. The results of this study warrant further clinical studies for long-term monitoring of ambulatory ECG after AF ablation.