Wearable Electrocardiogram Monitoring Research Articles

Efficacy of Wearable Single-Lead ECG Monitoring during Exercise Stress Testing: A Comparative Study

Background and Objectives: Few comparative studies have evaluated wearable single-lead electrocardiogram (ECG) devices and standard multi-lead ECG devices during exercise testing. This study aimed to validate the accuracy of a wearable single-lead ECG monitor for recording heart rate (HR) metrics during graded exercise tests (GXTs). Methods: A cohort of 50 patients at a tertiary hospital underwent GXT while simultaneously being equipped with wearable single- and conventional multi-lead ECGs. The concordance between these modalities was quantified using the intraclass correlation coefficient and Bland–Altman plot analysis. Results: The minimum and average HR readings between the devices were generally consistent. Parameters such as ventricular ectopic beats and supraventricular ectopic beats showed strong agreement. However, the agreement for the Total QRS and Maximum RR was not sufficient. HR measurements across different stages of the exercise test showed sufficient agreement. Although not statistically significant, the standard multi-lead ECG devices exhibited higher noise levels compared to the wearable single-lead ECG devices. Conclusions: Wearable single-lead ECG devices can reliably monitor HR and detect abnormal beats across a spectrum of exercise intensities, offering a viable alternative to traditional multi-lead systems.

Cardiorespiratory disturbances in focal impaired awareness seizures: Insights from wearable ECG monitoring

PurposeSeizures are characterized by periictal autonomic changes. Wearable devices could help improve our understanding of these phenomena through long-term monitoring. In this study, we used wearable electrocardiogram (ECG) data to evaluate differences between temporal and extratemporal focal impaired awareness (FIA) seizures monitored in the hospital and at home. We assessed periictal heart rate, respiratory rate, heart rate variability (HRV), and respiratory sinus arrhythmia (RSA). MethodsWe extracted ECG signals across three time points — five minutes baseline and preictal, ten minutes postictal — and the seizure duration. After automatic Rpeak selection, we calculated the heart rate and estimated the respiratory rate using the ECG-derived respiration methodology. HRV was calculated in both time and frequency domains. To evaluate the influence of other modulators on the HRV after removing the respiratory influences, we recalculated the residual power in the high-frequency (HF) and low-frequency (LF) bands using orthogonal subspace projections. Finally, 5-minute and 30-second (ultra-short) ECG segments were used to calculate RSA using three different methods. Seizures from temporal and extratemporal origins were compared using mixed-effects models and estimated marginal means. ResultsThe mean preictal heart rate was 69.95 bpm (95 % CI 65.6 – 74.3), and it increased to 82 bpm, 95 % CI (77.51 – 86.47) and 84.11 bpm, 95 % CI (76.9 – 89.5) during the ictal and postictal periods. Preictal, ictal and postictal respiratory rates were 16.1 (95 % CI 15.2 – 17.1), 14.8 (95 % CI 13.4 – 16.2) and 15.1 (95 % CI 14 – 16.2), showing not statistically significant bradypnea. HRV analysis found a higher baseline power in the LF band, which was still significantly higher after removing the respiratory influences. Postictally, we found decreased power in the HF band and the respiratory influences in both frequency bands. The RSA analysis with the new methods confirmed the lower cardiorespiratory interaction during the postictal period. Additionally, using ultra-short ECG segments, we found that RSA decreases before the electroclinical seizure onset. No differences were observed in the studied parameters between temporal and extratemporal seizures. ConclusionsWe found significant increases in the ictal and postictal heart rates and lower respiratory rates. Isolating the respiratory influences on the HRV showed a postictal reduction of respiratory modulations on both LF and HF bands, suggesting a central role of respiratory influences in the periictal HRV, unlike the baseline measurements. We found a reduced cardiorespiratory interaction during the periictal period using other RSA methods, suggesting a blockade in vagal efferences before the electroclinical onset. These findings highlight the importance of respiratory influences in cardiac dynamics during seizures and emphasize the need to longitudinally assess HRV and RSA to gain insights into long-term autonomic dysregulation.

Evaluation of the Diagnostic Performance and Efficacy of Wearable Electrocardiogram Monitoring for Arrhythmia Detection after Cardiac Surgery.

Postoperative atrial fibrillation (A-fib) is a serious complication of cardiac surgery that is associated with increased mortality and morbidity. Traditional 24-hour Holter monitors have limitations, which have prompted the development of innovative wearable electrocardiogram (ECG) monitoring devices. This study assessed a patch-type wearable ECG device (MobiCARE-MC100) for monitoring A-fib in patients undergoing cardiac surgery and compared it with 24-hour Holter ECG monitoring. This was a single-center, prospective, investigator-initiated cohort study that included 39 patients who underwent cardiac surgery between July 2021 and June 2022. Patients underwent simultaneous monitoring with both conventional Holter and patchtype ECG devices for 24 hours. The Holter device was then removed, and patch-type monitoring continued for an additional 48 hours, to determine whether extended monitoring provided benefits in the detection of A-fib. This 72-hour ECG monitoring study included 39 patients, with an average age of 62.2 years, comprising 29 men (74.4%) and 10 women (25.6%). In the initial 24 hours, both monitoring techniques identified the same number of paroxysmal A-fib in 7 out of 39 patients. After 24 hours of monitoring, during the additional 48-hour assessment using the patch-type ECG device, an increase in A-fib burden (9%→38%) was observed in 1 patient. Most patients reported no significant discomfort while using the MobiCARE device. In patients who underwent cardiac surgery, the mobiCARE device demonstrated diagnostic accuracy comparable to that of the conventional Holter monitoring system.

Wearable dynamic electrocardiogram monitor-based screening for atrial fibrillation in the community-dwelling elderly population

BackgroundAtrial fibrillation (AF) is a major public health problem with high rates of morbidity, disability and mortality, especially in the elderly population. This study explored the diagnosis and treatment status of AF in adults aged ≥65 years in the community through wearable dynamic electrocardiogram (ECG) monitoring. MethodsWe conducted a cross-sectional study in 4 random communities within the Qingpu district of Shanghai, China. Between January 1, 2020 and June 30, 2022, the ECGs of 3852 adults aged 65 years or older were examined through wearable dynamic ECG monitoring. Data from 3839 participants were ultimately analyzed. Multivariate logistic regression was used to determine the independent predictors of AF. ResultsWearable dynamic ECG monitoring detected AF in 360 elderly people, 78 of whom were diagnosed with AF for the first time. Multivariate logistic regression analysis revealed that snoring, renal dysfunction, coronary heart disease and high CHA2DS2-VASc score were independent risk factors for AF. Among patients with unknown AF, 68 (87.20 %) met the criteria for anticoagulant therapy based on the CHA2DS2-VASc score. Only 4 (5.88 %) patients were taking anticoagulants. Of the patients with a clear history of AF, 249 (84.98 %) needed an anticoagulant strategy, but only 18 (7.23 %) took oral anticoagulants. ConclusionMany elderly people have silent AF, and wearable dynamic ECG monitoring can be used to screen for AF effectively.

Hierarchical deep learning for autonomous multi-label arrhythmia detection and classification on real-world wearable electrocardiogram data.

Arrhythmia detection and classification are challenging because of the imbalanced ratio of normal heartbeats to arrhythmia heartbeats and the complicated combinations of arrhythmia types. Arrhythmia classification on wearable electrocardiogram monitoring devices poses a further unique challenge: unlike clinically used electrocardiogram monitoring devices, the environments in which wearable devices are deployed are drastically different from the carefully controlled clinical environment, leading to significantly more noise, thus making arrhythmia classification more difficult. We propose a novel hierarchical model based on CNN+BiLSTM with Attention to arrhythmia detection, consisting of a binary classification module between normal and arrhythmia heartbeats and a multi-label classification module for classifying arrhythmia events across combinations of beat and rhythm arrhythmia types. We evaluate our method on our proprietary dataset and compare it with various baselines, including CNN+BiGRU with Attention, ConViT, EfficientNet, and ResNet, as well as previous state-of-the-art frameworks. Our model outperforms existing baselines on the proprietary dataset, resulting in an average accuracy, F1-score, and AUC score of 95%, 0.838, 0.906 for binary classification, and 88%, 0.736, 0.875 for multi-label classification. Our results validate the ability of our model to detect and classify real-world arrhythmia. Our framework could revolutionize arrhythmia diagnosis by reducing the burden on cardiologists, providing more personalized treatment, and achieving emergency intervention of patients by allowing real-time monitoring of arrhythmia occurrence.

Artificial intelligence in wearable electrocardiogram monitoring

Electrocardiogram (ECG) monitoring owns important clinical value in diagnosis, prevention and rehabilitation of cardiovascular disease (CVD). With the rapid development of Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI) and other advanced technologies, wearable ECG is playing an increasingly important role. With the aging process of the population, it is more and more urgent to upgrade the diagnostic mode of CVD. Using AI technology to assist the clinical analysis of long-term ECGs, and thus to improve the ability of early detection and prediction of CVD has become an important direction. Intelligent wearable ECG monitoring needs the collaboration between edge and cloud computing. Meanwhile, the clarity of medical scene is conducive for the precise implementation of wearable ECG monitoring. This paper first summarized the progress of AI-related ECG studies and the current technical orientation. Then three cases were depicted to illustrate how the AI in wearable ECG cooperate with the clinic. Finally, we demonstrated the two core issues-the reliability and worth of AI-related ECG technology and prospected the future opportunities and challenges.

Episodes of Atrial Fibrillation and Symptoms: A Temporal Analysis

Data on the relationship between symptoms and atrial fibrillation (AF) episodes are limited. The objective of this study was to determine the strength of temporal association between AF episodes and symptoms. This cross-sectional ambulatory assessment study was performed in a tertiary care center between June 2018 and December 2021. Patients with paroxysmal AF (1 episode of AF, burden not exceeding 95%) who used a mobile application and continuous wearable electrocardiogram monitor for 21 days were enrolled. The primary outcome was worse symptoms (symptoms above the mean score) over the study period. The association between worse symptoms and the presence of AF was evaluated for different time epochs. Multilevel mixed-effects models were used to quantify associations after accounting for confounders. Worse symptoms were more likely to be associated with the presence of AF episodes 15 minutes prior to the reporting of palpitations (OR, 2.8 [95% CI, 1.6-5.0]; P < .001), shortness of breath (OR, 2.2 [95% CI, 1.3-3.7]; P = .003), dizziness/lightheadedness (OR, 2.0 [95% CI, 1.0-3.7]; P = .04), and fatigue (OR, 1.7 [95% CI, 1.0-2.9]; P = .03). The correlation between the severity of symptoms and AF lessened as the time interval from AF events to symptoms increased. There is a significant relationship between onset of AF episodes and reporting of symptoms. This association diminishes over time and varies across different symptoms. If confirmed in larger studies, these findings may inform AF interventions that target symptoms just in time prior to a clinical visit.

Research on fatigue identification methods based on low-load wearable ECG monitoring devices

The identification of fatigue in personal workers in particular environments can be achieved through early warning techniques. In order to prevent excessive fatigue of medical workers staying in infected areas in the early phase of the coronavirus disease pandemic, a system of low-load wearable electrocardiogram (ECG) devices was used as intelligent acquisition terminals to perform a continuous measurement ECG collection. While machine learning (ML) algorithms and heart rate variability (HRV) offer the promise of fatigue detection for many, there is a demand for ever-increasing reliability in this area, especially in real-life activities. This study proposes a random forest-based classification ML model to identify the four categories of fatigue levels in frontline medical workers using HRV. Based on the wavelet transform in ECG signal processing, stationary wavelet transform was applied to eliminate the main perturbation of ECG in the motion state. Feature selection was performed using ReliefF weighting analysis in combination with redundancy analysis to optimize modeling accuracy. The experimental results of the overall fatigue identification achieved an accuracy of 97.9% with an AUC value of 0.99. With the four-category identification model, the accuracy is 85.6%. These results proved that fatigue analysis based on low-load wearable ECG monitoring at low exertion can accurately determine the level of fatigue of caregivers and provide further ideas for researchers working on fatigue identification in special environments.

A moldable PEDOT:PSS dry electrode with excellent epidermal compliance for wearable electrocardiogram monitoring

An aerogel dry electrode was prepared by an interesting foaming process. The resultant aerogel displayed impressive microscale shape moldability, enabling excellent skin compliance for high-quality ECG signal detection.

Utility of a novel wearable electrode embedded in an undershirt for electrocardiogram monitoring and detection of arrhythmias

BackgroundA 12-lead electrocardiogram (ECG) and Holter ECG have been established as gold standards for detection of arrhythmias. Recently, wearable ECG monitoring devices have been available. Our purpose of the present study was to investigate whether a novel wearable electrode embedded in an undershirt is useful for ECG monitoring and detection of arrhythmias.MethodsWe studied 31 consecutive hospitalized patients who underwent catheter ablation of tachyarrhythmias. Patients equipped a wearable electrode and a lead CM5 of Holter ECG simultaneously, and total heart beats, maximum heart rate (HR), mean HR, minimum HR, detections of arrhythmias, such as atrial fibrillation, non-sustained ventricular tachycardia, and premature ventricular contractions (Lown’s grade >II), were compared between the two methods using a Holter ECG analysis software.ResultsMedian recording time of ECG by wearable electrodes was 12.6 hours. Strong correlations between the two methods were observed in total heart beats (R = 0.999, P <0.001), maximum HR (R = 0.997, P <0.001), mean HR (R = 0.999, P <0.001), minimum HR (R = 0.989, P <0.001) and QRS duration (R = 0.900, P <0.001). Bland-Altman analysis showed excellent concordance between each parameter measured by two methods. In addition, the detection of atrial fibrillation (nine events), non-sustained ventricular tachycardia (two events), and premature ventricular contractions of Lown’s grade >II (five events) were concordant in two methods. In addition, there were no significant difference in parameters of time-domain and frequency-domain analyses of heart rate variability between the two methods.ConclusionsThe usefulness of a novel electrode embedded in an undershirt is equivalent to that of a Holter ECG in monitoring the ECG and detection of arrythmias.

FEASIBILITY AND USEFULNESS OF THE SINGLE-LEAD WEARABLE ECG MONITORING DURING THE ENDURANCE EXERCISE

This study aims to determine whether the single-lead wearable electrocardiogram monitoring during an endurance exercise; intensive cycling and running is feasible. It is also intended to find out the usefulness of the wearable single-lead Electrocardiography (ECG) monitoring to detect cardiac arrhythmia that may occur during exercise.

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.

Optically Unobtrusive Zeolite-Based Dry Electrodes for Wearable ECG Monitoring

Novel dry electrodes are increasingly evolving for monitoring electrocardiograms compared to the widely used <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ag/AgCl</i> (wet) electrodes as there is no skin preparation or wet gel needed. Many fabricated dry electrodes are however opaque, and the problem of skin irritation and rashes continue to pose a concern. In this paper, an optically unobtrusive dry electrode based on the iono-electric properties of zeolite is developed and proposed for continuous electrocardiogram (ECG) monitoring. An easily moldable composite of the sodium form of nano-porous zeolite and polydimethylsiloxane (PDMS) is reported for the first time. The electrical and bio-electrical characterizations are evaluated for 24 pairs of electrodes which were fabricated with two filler materials (Zeolite 4A and 13X), two filler concentrations (4 wt% and 12 wt%), three diameters (30 mm, 20 mm, and 10 mm), and differing thicknesses. High input impedance and high conductivity of the electrodes have proven the suitability for biosignal acquisition. ECG signals are acquired using each pair of electrodes. Results related to impedance, conductivity, and SNR measurements over a frequency range up to 10 kHz are presented. Among the 24 pairs, the results indicate that signal-to-noise ratio (SNR) for Zeolite 13X/PDMS- based filler with a concentration of 4 wt% and diameter of 30 mm has the highest SNR of 42.9 dB and conductivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.58 \mu \text{S}$ </tex-math></inline-formula> /mm. These electrodes are optically unobtrusive and easy to fabricate. Hence this process yields promising results for the next generation of wearable cardiovascular heart monitoring systems.

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.

A new method for manufacturing dry electrodes on textiles. Validation for wearable ECG monitoring

This paper presents a new dry ECG electrode printed on a textile substrate. The proposed manufacturing process permits cost-effective mass production. The ECG dry electrode is obtained through screen printing a conductive silver ink coated with a biocompatible carbon layer. Three different designs combining two shapes (circular and square) and two sizes were developed. The resulting measured impedances are similar to those obtained via a conventional electrode. The prototypes were attached to a bracelet and used with a commercial electrocardiogram (ECG) device to register ECG signals. The dry electrodes were validated via ECG monitoring and compared with a conventional wet electrode. The clinical interest intervals reported similar results and the QRS morphology presented slight differences. Noise evaluation showed no notable differences for all the analyzed parameters.

A CNN Model for Cardiac Arrhythmias Classification Based on Individual ECG Signals.

Wearable devices in the scenario of connected home healthcare integrated with artificial intelligence have been an effective alternative to the conventional medical devices. Despite various benefits of wearable electrocardiogram (ECG) device, several deficiencies remain unsolved such as noise problem caused by user mobility. Therefore, an insensitive and robust classification model for cardiac arrhythmias detection system needs to be devised. A one-dimensional seven-layer convolutional neural network (CNN) classification model with dedicated design of structure and parameters is developed to perform automatic feature extraction and classification based on large volume of original noisy signals. Record-based ten-fold cross validation scheme is devised for evaluation to ensure the independence of the training set and test set, and further improve the robustness of our method. The model can effectively detect cardiac arrhythmias, and can reduce the computational workload to a certain extent. Our experimental results outperform most recent literature on the cardiac arrhythmias classification with diagnostic accuracy of 0.9874, sensitivity of 0.9811, and specificity of 0.9905 for original signals; diagnostic accuracy of 0.9876, sensitivity of 0.9813, and specificity of 0.9907 for de-noised signals, respectively. The evaluation indicates that our proposed approach, which performs well on both original signals and de-noised signals, fits well with wearable ECG monitoring and applications.

Research on high-efficiency electrocardiogram automatic classification based on autoregressive moving average model fitting

The automatic detection of arrhythmia is of great significance for the early prevention and diagnosis of cardiovascular diseases. Traditional arrhythmia diagnosis is limited by expert knowledge and complex algorithms, and lacks multi-dimensional feature representation capabilities, which is not suitable for wearable electrocardiogram (ECG) monitoring equipment. This study proposed a feature extraction method based on autoregressive moving average (ARMA) model fitting. Different types of heartbeats were used as model inputs, and the characteristic of fast and smooth signal was used to select the appropriate order for the arrhythmia signal to perform coefficient fitting, and complete the ECG feature extraction. The feature vectors were input to the support vector machine (SVM) classifier and K-nearest neighbor classifier (KNN) for automatic ECG classification. MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database were used to verify in the experiment. The experimental results showed that the feature engineering composed of the fitting coefficients of the ARMA model combined with the SVM classifier obtained a recall rate of 98.2% and a precision rate of 98.4%, and the F 1 index was 98.3%. The algorithm has high performance, meets the needs of clinical diagnosis, and has low algorithm complexity. It can use low-power embedded processors for real-time calculations, and it's suitable for real-time warning of wearable ECG monitoring equipment.

An ECG Delineation and Arrhythmia Classification System Using Slope Variation Measurement by Ternary Second-Order Delta Modulators for Wearable ECG Sensors.

This paper presents a system for electrocardiogram (ECG) delineation and arrhythmia classification. The proposed system consists of a front-end integrated circuit, a delineation algorithm implemented on an FPGA board, and an arrhythmia classification algorithm. The front-end circuit applies a ternary second-order Delta modulator to measure the slope variation of the input analog ECG signal. The circuit converts the analog inputs into a pulse density modulated bitstream, whose pulse density is proportional to the slope variation of the input analog signal regardless of the instantaneous amplitude. The front-end chip can detect the minimum slope variation of 3.2mV/ms 2 within a 3ms timing error. The front-end integrated circuit was fabricated with a 180nm CMOS process occupying a 0.25mm 2 area with a 151 nW power consumption at the sampling rate of 1 kS/s. Based on the slope variation obtained from the front-end circuit, a delineation algorithm is designed to detect fiducial points in the ECG waveform. The delineation algorithm was tested on a Spartan-6 FPGA. The delineation system can detect the intervals, slopes, and morphology of the QRS/PT waves and form a feature set that contains 22 features. Based on these features, a rotate linear kernel support vector machine (SVM) is applied for patient-specific arrhythmia classification of the ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), and heartbeats originating in sinus node. The performance of the proposed system is comparable to the recently published methods while providing a promising solution for the low-complexity implementation of future wearable ECG monitoring systems.

Robust Heartbeat Classification for Wearable Single-Lead ECG via Extreme Gradient Boosting.

Wearable electrocardiogram (ECG) monitoring devices have enabled everyday ECG collection in our daily lives. However, the condition of ECG signal acquisition using wearable devices varies and wearable ECG signals could be interfered with by severe noises, resulting in great challenges of computer-aided automated ECG analysis, especially for single-lead ECG signals without spare channels as references. There remains room for improvement of the beat-level single-lead ECG diagnosis regarding accuracy and efficiency. In this paper, we propose new morphological features of heartbeats for an extreme gradient boosting-based beat-level ECG analysis method to carry out the five-class heartbeat classification according to the Association for the Advancement of Medical Instrumentation standard. The MIT-BIH Arrhythmia Database (MITDB) and a self-collected wearable single-lead ECG dataset are used for performance evaluation in the static and wearable ECG monitoring conditions, respectively. The results show that our method outperforms other state-of-the-art models with an accuracy of 99.14% on the MITDB and maintains robustness with an accuracy of 98.68% in the wearable single-lead ECG analysis.

Facile Fabrication of Robust and Reusable PDMS Supported Graphene Dry Electrodes for Wearable Electrocardiogram Monitoring

AbstractGraphene‐based epidermal dry electrodes have generated wide interests in electrocardiography (ECG) monitoring to screen cardiovascular diseases in time. Nevertheless, the poor stability, mechanical strength, and integration are significant obstacles hindering further product marketing of graphene electrodes in health care. Herein, robust, reusable, and patterned graphene‐based wearable dry electrodes are fabricated based on laser writing technology and supported by polydimethylsiloxane (PDMS) layers. After bending tests over 10 000 cycles and high‐power ultrasonic treatments (5 h), graphene/PDMS electrodes are demonstrated to have excellent stability and reusability for ECG recording. Taking the advantages of the designable and efficient CO2 laser production technique, a “staff‐shape” graphene electrode consisting of six chest‐lead electrodes and corresponding lead lines can be facilely customized and fabricated according to users’ somatotype to greatly simplify the acquisition procedure of 12‐lead ECG, realizing the acquisition of V1–V6 ECGs for all‐round cardiac monitoring. After thorough analysis of ECG patterns, the integrated chest electrode exhibits desirable sensitivity and reliability comparable to wet Ag/AgCl electrodes. The reusable graphene/PDMS electrodes are suitable for long‐term ECG monitoring and the integrated electrode presents a novel strategy to wearable 12‐ECG recording.