Wearable Electrocardiogram Devices Research Articles

Recent Progress in the Development of Flexible Wearable Electrodes for Electrocardiogram Monitoring During Exercise

Electrocardiogram (ECG) monitoring has recently been an important indicator of cardiac health diagnosis. In the past, ECG could be measured under limited conditions in hospitals with 12‐lead electrode systems. Recently, portable and wearable devices have offered continuous, real‐time monitoring of ECG signals in real life. However, developing wearable ECG sensors that provide low‐motion artifacts and high‐quality signals during exercise conditions is still challenging. Herein, this review reports a systematic summary of the key characteristics, properties, and requirements of flexible wearable ECG devices for the early diagnosis of heart dysfunction in dynamic motions, including exercise. In addition, the recent progress in controlling sensor adhesion and novel materials for designing dry electrodes are discussed to improve ECG signal quality in exercise. Finally, various aspects of electrode developmental challenges and limitations are reviewed, and research directions for future studies are discussed.

Wearable electrocardiogram devices in patients with congenital long QT syndrome: The SMART-QT study

BackgroundIn patients with congenital long QT syndrome (LQTS), the risk of ventricular arrhythmia is correlated with the duration of the corrected QT interval and the changes in the ST-T wave pattern on the 12-lead surface electrocardiogram (12L-ECG). Remote monitoring of these variables could be useful. AimTo evaluate the abilities of two wearable electrocardiogram devices (Apple Watch and KardiaMobile 6L) to provide reliable electrocardiograms in terms of corrected QT interval and ST-T wave patterns in patients with LQTS. MethodsIn a prospective multicentre study (ClinicalTrials.gov identifier: NCT04728100), a 12L-ECG, a 6-lead KardiaMobile 6L electrocardiogram and two single-lead Apple Watch electrocardiograms were recorded in patients with LQTS. The corrected QT interval and ST-T wave patterns were evaluated manually. ResultsOverall, 98 patients with LQTS were included; 12.2% were children and 92.8% had a pathogenic variant in an LQTS gene. The main genotypes were LQTS type 1 (40.8%), LQTS type 2 (36.7%) and LQTS type 3 (7.1%); rarer genotypes were also represented. When comparing the ST-T wave patterns obtained with the 12L-ECG, the level of agreement was moderate with the Apple Watch (k=0.593) and substantial with the KardiaMobile 6L (k=0.651). Regarding the corrected QT interval, the correlation with 12L-ECG was strong for the Apple Watch (r=0.703 in lead II) and moderate for the KardiaMobile 6L (r=0.593). There was a slight overestimation of corrected QT interval with the Apple Watch and a subtle underestimation with the KardiaMobile 6L. ConclusionsIn patients with LQTS, the corrected QT interval and ST-T wave patterns obtained with the Apple Watch and the KardiaMobile 6L correlated with the 12L-ECG. Although wearable electrocardiogram devices cannot replace the 12L-ECG for the follow-up of these patients, they could be interesting additional monitoring tools.

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.

Dry electrode geometry optimization for wearable ECG devices

Wearable electronic devices, particularly for health monitoring, have seen rapid advancements in recent times. Among the various biophysical parameters that are of interest in a wearable device, an electrocardiogram (ECG) is critical as it enables detection of cardiovascular-related ailments and assessment of overall cardiac health. In a wearable ECG device, the choice of electrode design and material plays a key role in the performance of the sensor. In this work, we have explored various dry electrode-based sensor design geometries to realize a compact, lightweight, portable, gel-free wearable ECG patch that would aid in point-of-care (PoC) diagnostics. Furthermore, we have studied the influence of the region of the body at which the measurements were made under different body positions across varying external stimuli. We have studied the influence of surface area, perimeter and resistance offered by the electrodes on the ECG signal acquisition, its effects on device performance and found the hexagonal labyrinth configuration to be the most suitable candidate. A prototype of a wearable ECG patch was made by combining this electrode configuration and interfacing with wireless communication capabilities, and the results were compared with a commercially available portable ECG monitor. Such a device could find potential application in remote healthcare and ambulatory care settings, and as a PoC and a preventive medical device.

Signal Quality Analysis of Single-Arm Electrocardiography.

The number of people experiencing mental stress or emotional dysfunction has increased since the onset of the COVID-19 pandemic, as many individuals have had to adapt their daily lives. Numerous studies have demonstrated that mental health disorders can pose a risk for certain diseases, and they are also closely associated with the problem of mental workload. Now, wearable devices and mobile health applications are being utilized to monitor and assess individuals' mental health conditions on a daily basis using heart rate variability (HRV), typically measured by the R-to-R wave interval (RRI) of an electrocardiogram (ECG). However, portable or wearable ECG devices generally require two electrodes to perform bipolar limb leads, such as the Einthoven triangle. This study aims to develop a single-arm ECG measurement method, with lead I ECG serving as the gold standard. We conducted static and dynamic experiments to analyze the morphological performance and signal-to-noise ratio (SNR) of the single-arm ECG. Three morphological features were defined, RRI, the duration of the QRS complex wave, and the amplitude of the R wave. Thirty subjects participated in this study. The results indicated that RRI exhibited the highest cross-correlation (R = 0.9942) between the single-arm ECG and lead I ECG, while the duration of the QRS complex wave showed the weakest cross-correlation (R = 0.2201). The best SNR obtained was 26.1 ± 5.9 dB during the resting experiment, whereas the worst SNR was 12.5 ± 5.1 dB during the raising and lowering of the arm along the z-axis. This single-arm ECG measurement method offers easier operation compared to traditional ECG measurement techniques, making it applicable for HRV measurement and the detection of an irregular RRI.

PO-04-175 RECONSTRUCTING TWELVE-LEAD ELECTROCARDIOGRAM SIGNALS FROM A SINGLE-LEAD SIGNAL USING GENERATIVE ADVERSARIAL NETWORKS

Recently, many highly accurate artificial intelligence (AI)-based prediction models have been developed for 12-lead electrocardiogram (ECG) signals. Therefore, if one can faithfully reconstruct 12-lead signals from a single-lead signal, such prediction models can seamlessly be integrated into various wearable devices that measure only a single-lead ECG signal. We aim to verify the performance of AI model that constructs estimated 12-lead ECG signals from a single-lead ECG signal. To train and validate AI models, we used 36,610 12-lead standard ECG (10 seconds) signals recorded from Ewha Womans University Medical Center between 23 May 2017 and 2 September 2020. In this study, we examined whether it is possible to generate ECGs that reflect the characteristics of various cardiovascular diseases such as LBBB, RBBB, and AF. All input signals were normalized by using a bandpass filter and into [-1, 1]. To reconstruct 12-lead signals from a single-lead signal, we developed a novel conditional generative adversarial network, called EKGAN, which is based on Pix2Pix [1] and consists of two generators and one discriminator. Then, the performance of EKGAN was evaluated with respect to root mean square error (RMSE), mean absolute error (MAE) and percentage root mean squared difference (PRD). We used 36,610 and 9,154 ECGs to train and test AI models that generate other leads from a Lead I signal. Table 1 shows the performance of EKGAN and Pix2Pix for generating all lead signals except Lead I, where EKGAN outperformed Pix2Pix in terms of all measures. Figure 1 shows an example of original and generated 12-lead ECG signals by EKGAN and Pix2Pix using only the Lead I signal. Twelve-lead ECG signals can faithfully be reconstructed from a single-lead ECG signal by using our GAN-based reconstruction model, which opens up a wide range of medical applications of wearable ECG devices.

Identity affirmation is associated with adaptive cardiovascular flexibility in response to stress among sexual and gender minority people of color

BackgroundResearch with sexual and gender minority (e.g., lesbian, gay, bisexual, queer, asexual, trans, non-binary) people of Color (SGM-PoC) has largely focused on risk and negative health outcomes. The existing strengths-based mental health research suggests that identity affirmation may be associated with psychological resilience and good mental health among SGM-PoC, but little is known about predictors of physiological resilience and biomarkers of physical health in this population. Adaptive cardiovascular flexibility is an indicator of physiological resilience and physical health in that it enables the body to mobilize resources to adapt to challenges. This study explored the association between identity affirmation and physiological resilience, observed through cardiovascular flexibility in response to stress among SGM-PoC. MethodsParticipants were 95 SGM-PoC of varying ethnoracial backgrounds, sexual orientations, and gender identities, residing in Canada. Participants completed questionnaires on sociodemographic and psychosocial variables, including a measure of identity affirmation, the Queer People of Color Identity Affirmation Scale (QPIAS). In a laboratory setting, we induced stress using the Trier Social Stress Test and measured heart rate variability (HRV) over time using wearable electrocardiogram devices. ResultsResults from multilevel modeling analyses revealed that high QPIAS scores were associated with adaptive cardiovascular flexibility, as evidenced by reduced HRV during stress exposure, followed by HRV increase during recovery. As QPIAS scores increased, the shape of HRV trajectory increasingly reflected our predicted pattern of adaptive cardiovascular flexibility. Meanwhile, low QPIAS scores were not associated with this pattern and appeared to predict a more flatlined HRV activity during the experiment. ConclusionsFindings indicate that Queer People of Color identity affirmation may be related to physiological resilience, observed through a more adaptive cardiovascular profile when responding to stress. Identity affirmation may thus be a protective factor for SGM-PoC, pointing to the critical importance of affirming resources and strengths-based health research.

Augmenting ECG Data with Multiple Filters for a Better Emotion Recognition System.

A physiological-based emotion recognition system (ERS) with a unimodal approach such as an electrocardiogram (ECG) is not as popular compared to a multimodal approach. However, a single modality has the advantage of lower development and computational cost. Therefore, this study focuses on a unimodal ECG-based ERS. The ECG-based ERS has the potential to become the next mass-adopted consumer application due to the wide availability of wearable and mobile ECG devices in the market. Currently, ECG-inclusive affective datasets are limited, and many of the existing datasets have small sample sizes. Hence, ECG-based ERS studies are stunted by the lack of quality data. A novel multi-filtering augmentation technique is proposed here to increase the sample size of the ECG data. This technique augments the ECG signals by cleaning the data in different ways. Three small ECG datasets labelled according to emotion state are used in this study. The benefit of the proposed augmentation techniques is measured using the classification accuracy of five machine learning algorithms; k-nearest neighbours (KNN), support vector machine, decision tree, random forest and multilayer perceptron. The results show that with the proposed technique, there is a significant improvement in performance for all the datasets and classifiers. KNN classifier improved the most with the augmented data and the reported classification accuracies of over 90%.

Application of wearable 12-lead electrocardiogram devices in pre-hospital diagnosis of acute ST segment elevation myocardial infarction

To assess the value of wearable 12-lead electrocardiogram (ECG) devices in pre-hospital diagnosis of acute ST segment elevation myocardial infarction (STEMI). This analysis was conducted among 441 patients selected from the''National ECG Network'', who used wearable 12-lead ECG device with critical situation warning of ST change between January 2019, and August, 2021.The general characteristics, response time and complaints of the patients with STEMI were analyzed.The accuracy of pre-hospital diagnosis of STEMI was compared between clinician's interpretation of ECGs and AI diagnosis by the wearable ECG device. In 89 of the patients, a pre-hospital diagnosis of STEMI was made by physicians based on ECGs from the wearable devices, and 58 of them sought medical attention after online warning, with a referral rate of 65.17%.The average time for diagnostic assessment of the ECGs was 153.02 s, and the average time for confirmation of the diagnosis was 178.06 s.The sensitivity for pre-hospital diagnosis of STEMI by clinician's interpretation of the ECGs and by AI diagnosis was 100% and 88.37%, respectively, with a specificity of 95.40% and 79.31%, respectively.The pre-hospital diagnosis by clinicians and AI diagnosis of STEMI both showed a high consistency with the subsequent definite clinical diagnosis of STEMI. Wearable 12-lead ECG devices can accurately record ECG characteristics of STEMI patients outside the hospital and allow immediate data uploading for an early diagnosis.The diagnoses of STEMI made based on AI technology are highly consistent with those by clinicians, demonstrating excellent clinical performance of the wearable ECG devices.

A 2.66 µW Clinician-Like Cardiac Arrhythmia Watchdog Based on P-QRS-T for Wearable Applications.

A wearable electrocardiogram (ECG) device is an effective tool for managing cardiovascular diseases. This paper presents a low power clinician-like cardiac arrhythmia watchdog (CAW) for wearable ECG devices. The CAW is based on a novel P-QRS-T detection algorithm that makes use of clinical features to identify abnormalities. Implemented in 0.18 μm CMOS process, the CAW consumes 2.66 µW for 80 bpm heart rate at 1.2 V supply with an area of 0.578 mm2. Verified on QT database, the average sensitivity/positive predictivity for P-wave, QRS complex and T-wave are over 93.39%/88.55%, 99.69%/99.48%, and 97.13%/93.18% respectively, across over 190000 beats. It shows over 99.8% arrhythmia detection accuracy for 43 subjects evaluated on MIT-BIH database.

Remote and wearable ECG devices with diagnostic abilities in adults: A state-of-the-science scoping review.

The electrocardiogram (ECG) records the electrical activity in the heart in real time, providing an important opportunity to detecting various cardiac pathologies. The 12-lead ECG currently serves as the "standard" ECG acquisition technique for diagnostic purposes for many cardiac pathologies other than arrhythmias. However, the technical aspects of acquiring a 12-lead ECG are not easy. and its usage currently is restricted to trained medical personnel, which limits the scope of its usefulness. Remote and wearable ECG devices have attempted to bridge this gap by enabling patients to take their own ECG using a simplified method at the expense of a reduced number of leads, usually a single-lead ECG. In this review, we summarize the studies that investigated the use of remote ECG devices and their clinical utility in diagnosing cardiac pathologies. Eligible studies discussed Food and Drug Administration-cleared, commercially available devices that were validated in an adult population. We summarize technical logistics of signal quality and device reliability, dimensional and functional features, and diagnostic value. Our synthesis shows that reduced-set ECG wearables have huge potential for long-term monitoring, particularly if paired with real-time notification techniques. Such capabilities make them primarily useful for abnormal rhythm detection, and there is sufficient evidence that a remote ECG device can be superior to the traditional 12-lead ECG in diagnosing specific arrhythmias such as atrial fibrillation. However, this review identifies important challenges faced by this technology and highlights the limited availability of clinical research examining their usefulness.

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.

Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection

The analysis of the electrocardiogram (ECG) is critical for the diagnosis of arrhythmias. Recent advances in information and communications technology (ICT) have led to the development of wearable ECG devices and arrhythmia-detection algorithms. This study aimed to develop an ICT-based clinical trial service platform using a self-reporting technique for real-time arrhythmia detection. To establish a clinical-trial service platform, a mobile application (app), a demilitarized zone (DMZ), an internal network, and Amazon web services virtual private cloud (AWS-VPC) were developed. The ECG data acquired by a wearable device were transmitted to the mobile app, which collected the participants’ self-reported information. The mobile app transmitted raw ECG and self-reported data to the AWS-VPC and DMZ, respectively. In the AWS-VPC, the live-streaming and playback-reviewer services were operational to display the currently and previously acquired ECG data to clinicians through the web client. All the measured data were transmitted to the internal network, in which the arrhythmia-detection algorithm was executed and all the data were saved. The self-reporting technique and arrhythmia-detection algorithm are the key elements of this platform. In particular, subjective information of participants can be easily collected using a self-reporting technique. These features are particularly of critical importance for treating painless, sparsely occurring arrhythmias.

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.

Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors.

Heart rate is one of the most important diagnostic bases for cardiovascular disease. This paper introduces a deep autoencoding strategy into feature extraction of electrocardiogram (ECG) signals, and proposes a beat-to-beat heart rate estimation method based on convolution autoencoding and Gaussian mixture clustering. The high-level heartbeat features were first extracted in an unsupervised manner by training the convolutional autoencoder network, and then the adaptive Gaussian mixture clustering was applied to detect the heartbeat locations from the extracted features, and calculated the beat-to-beat heart rate. Compared with the existing heartbeat classification/detection methods, the proposed unsupervised feature learning and heartbeat clustering method does not rely on accurate labeling of each heartbeat location, which could save a lot of time and effort in human annotations. Experimental results demonstrate that the proposed method maintains better accuracy and generalization ability compared with the existing ECG heart rate estimation methods and could be a robust long-time heart rate monitoring solution for wearable ECG devices.

Return-to-Work Screening by Linear Discriminant Analysis of Heart Rate Variability Indices in Depressed Subjects.

Using a linear discriminant analysis of heart rate variability (HRV) indices, the present study sought to verify the usefulness of autonomic measurement in major depressive disorder (MDD) patients by assessing the feasibility of their return to work after sick leave. When reinstatement was scheduled, patients’ HRV was measured using a wearable electrocardiogram device. The outcome of the reinstatement was evaluated at one month after returning to work. HRV indices including high- and low-frequency components were calculated in three conditions within a session: initial rest, mental task, and rest after task. A linear discriminant function was made using the HRV indices of 30 MDD patients from our previous study to effectively discriminate the successful reinstatement from the unsuccessful reinstatement; this was then tested on 52 patients who participated in the present study. The discriminant function showed that the sensitivity and specificity in discriminating successful from unsuccessful returns were 95.8% and 35.7%, respectively. Sensitivity is high, indicating that normal HRV is required for a successful return, and that the discriminant analysis of HRV indices is useful for return-to-work screening in MDD patients. On the other hand, specificity is low, suggesting that other factors may also affect the outcome of reinstatement.

A Cloud-Connected Multi-Lead Electrocardiogram (ECG) Sensor Ring

The ability to acquire multi-lead electrocardiograms (ECGs) in a timely manner is critical to patient survival and better clinical outcomes after acute cardiac events, including myocardial infarctions (MI). However, current wearable ECG devices do not provide the traditional 12-lead information needed for clinical MI diagnosis. Here, we report, to our knowledge, the first finger-ring-shaped ECG sensor that can provide asynchronously derived 12-lead ECGs by sequentially placing the ring on eight defined locations on the body. We demonstrate that the sensor performance is comparable to that of a clinical 12-lead ECG machine in a human subject study. The ring ECG sensor has an input-referred electronics noise floor of less than 10uV (peak-to-peak) with a 480X gain and a bandwidth of 0.16 ~ 156 Hz. The battery lasts ~ 5 days under normal usage. The ring sensor can also wirelessly upload data to a cloud-based medical IoT informatics system via a smartphone gateway. Combined with advancements in cloud computing, telemedicine, and artificial intelligence, this on-demand IoT ECG sensor can potentially help high-risk cardiac patients reduce prehospital delays and seek timely life-saving interventions.

A SCALABLE HYBRID MODEL FOR ATRIAL FIBRILLATION DETECTION

In this work, a scalable hybrid model is proposed for the purpose of screening and continuous monitoring of atrial fibrillation (AF) using electrocardiogram (ECG) signals collected from wearable ECG devices. The time series of RR intervals (with units in seconds) extracted from the ECG signal is fed into a recurrent neural network (RNN), and the bandpass filtered and scaled signal itself is fed into a convolutional neural network (CNN). At the post-processing stage, these two predictions are merged. An additional logistic regression model using statistical features of “pseudo” PR interval sequence is applied to aid making the final prediction. The proposed model is trained and validated on several datasets from PhysioNet and achieves a precision of 98.28% and a specificity of 99.82% on a dataset collected from several PhysioNet databases. This hybrid model has already been deployed through a WeChat applet, providing services those using wearable ECG devices, thus helping the screening and continuous out-of-hospital monitoring of the disease of AF.

Development and Test of a Portable ECG Device with Dry Capacitive Electrodes and Driven Right Leg Circuit.

The use of wearable sensors for health monitoring is rapidly growing. Over the past decade, wearable technology has gained much attention from the tech industry for commercial reasons and the interest of researchers and clinicians for reasons related to its potential benefit on patients’ health. Wearable devices use advanced and specialized sensors able to monitor not only activity parameters, such as heart rate or step count, but also physiological parameters, such as heart electrical activity or blood pressure. Electrocardiogram (ECG) monitoring is becoming one of the most attractive health-related features of modern smartwatches, and, because cardiovascular disease (CVD) is one of the leading causes of death globally, the use of a smartwatch to monitor patients could greatly impact the disease outcomes on health care systems. Commercial wearable devices are able to record just single-lead ECG using a couple of metallic contact dry electrodes. This kind of measurement can be used only for arrhythmia diagnosis. For the diagnosis of other cardiac disorders, additional ECG leads are required. In this study, we characterized an electronic interface to be used with multiple contactless capacitive electrodes in order to develop a wearable ECG device able to perform several lead measurements. We verified the ability of the electronic interface to amplify differential biopotentials and to reject common-mode signals produced by electromagnetic interference (EMI). We developed a portable device based on the studied electronic interface that represents a prototype system for further developments. We evaluated the performances of the developed device. The signal-to-noise ratio of the output signal is favorable, and all the features needed for a clinical evaluation (P waves, QRS complexes and T waves) are clearly readable.