Electrocardiogram Acquisition Research Articles

A novel wearable device integrating ECG and PCG for cardiac health monitoring

The alarming prevalence and mortality rates associated with cardiovascular diseases have emphasized the urgency for innovative detection solutions. Traditional methods, often costly, bulky, and prone to subjectivity, fall short of meeting the need for daily monitoring. Digital and portable wearable monitoring devices have emerged as a promising research frontier. This study introduces a wearable system that integrates electrocardiogram (ECG) and phonocardiogram (PCG) detection. By ingeniously pairing a contact-type PZT heart sound sensing structure with ECG electrodes, the system achieves the acquisition of high-quality ECG and PCG signals. Notably, the signal-to-noise ratios (SNR) for ECG and PCG signals were measured at 44.13 dB and 30.04 dB, respectively, demonstrating the system’s remarkable stability across varying conditions. These collected signals were subsequently utilized to derive crucial feature values, including electromechanical delay (EMD), left ventricular ejection time (LVET), and pre-ejection period (PEP). Furthermore, we collected a dataset comprising 40 cases of ECG and PCG signals, enabling a comparative analysis of these three feature parameters between healthy individuals and coronary heart disease patients. This research endeavor presents a significant step forward in the realm of early, non-invasive, and intelligent monitoring of cardiovascular diseases, offering hope for earlier detection and more effective management of these life-threatening conditions.

Evaluating the diagnostic accuracy of reduced lead paediatric electrocardiograms.

Alternate electrocardiogram acquisition with fewer leads lacks systematic evaluation in children. This study aims to determine if electrocardiograms with fewer leads maintain diagnostic accuracy in paediatrics. This is a single-centre review of 200 randomly selected standard 12-lead electrocardiograms from our hospital database (2017-2020) for patients aged 2 weeks to 21 years. An overlay technique generated 8-lead (limb + V1/V6) and 6-lead (limb only) variations of the 12-lead tracings, resulting in a total of 600 electrocardiograms, which were then interpreted by two independent paediatric electrophysiologists. In total, 18% (35/200) of the baseline electrocardiograms were abnormal. Intervals were measured in lead II for all electrocardiograms. Comparing 12-lead to 6- and 8-lead electrocardiograms, there was almost perfect agreement for specific rhythm identification (97.5-100%, κ 0.85-1). The 8-lead showed substantial agreement with 12-lead electrocardiograms when identifying specific electrocardiogram patterns (97.5-100%, κ 0.66-1). A similar degree of agreement was not demonstrated with the 6-lead variant. Utilising the 12-lead electrocardiogram as the gold standard, sensitivity and specificity of the 8- and 6-lead electrocardiogram were > 89% for specific rhythm identification. Specificity for specific pattern recognition was > 99% while sensitivity was < 90% for certain variables for both 6- and 8-lead electrocardiogram, likely due to smaller sample size and fewer abnormal electrocardiograms. There was high percent reader agreement (92.5-100%). 8-lead electrocardiograms provide comparable diagnostic accuracy to 12-lead electrocardiograms for children. This information holds potential for future technological advancements in electrocardiogram acquisition tailored specifically for paediatrics. Additional studies are required to further refine conventional electrocardiogram acquisition.

Long-term time-to-ventricular arrhythmic event prediction using a deep learning based model on the electrocardiogram signal

Abstract Background Ventricular arrhythmias (VA) risk stratification tools in individuals without known cardiovascular disease are still insufficient. Previous studies have used convolutional neural networks (CNN) to classify VA by the end of an established follow-up period using the electrocardiogram (ECG), showing a moderate performance (area under the curve [AUC] of 0.610). We hypothesised that the appearance of the VA substrate on the ECG signal depends on the proximity of the ECG acquisition to the VA event. Thus, the performance could improve if the CNN was trained to predict time-to-VA event instead, and if the presence of censored data was considered. Purpose We developed and tested a CNN to predict time-to-VA event in middle-aged individuals without known cardiovascular disease, and we compared its performance with that from ECG indices, such as QRS duration, QT interval and T-wave morphology variations (TMV), as well as sex and age. Methods A total of 69,283 individuals without known cardiovascular disease from the UK Biobank were used as the training set. We applied 10-fold cross validation to train a CNN, where the input was a 15-second ECG at rest (lead I), and the output was ‘occurrence of VA’, ‘censored’, or ‘survival’, up to 1, 2, 3, 4, 6, 8, 10 and 12 years after the ECG acquisition. We used 15-second ECG (lead I) from an independent cohort of 17,320 individuals without cardiovascular disease also from UK Biobank as a test set, where we set a follow-up of 10 years. The above ECG indices were derived from the median heartbeat from the ECG using in-house algorithms. We derived optimal cut-off thresholds from the training test, and we evaluated the AUC, sensitivity, specificity and Cox regression hazard ratios (HR) in the test set. Results In the test set, 9,206 individuals (53%) reached the 10-year follow-up, during which 60 (0.7%) had a VA event. The AUC, sensitivity and specificity values of the CNN were 0.715, 0.881 and 0.400, respectively. These values were 0.592, 0.686 and 0.383 for the QRS duration, 0.569, 0.156 and 0.717 for the QT interval and 0.552, 0.712 and 0.350 for TMV. The combination of sex and age alone showed performance values of 0.760, 0.694 and 0.683, respectively. Survival analyses showed a HR of 3.883 (P = 3.0 x 10-7) for individuals with a CNN prediction greater than the threshold derived in the training set after adjusting for age and gender. Conclusions A CNN that is trained to predict time-to-VA event and accounts for censored data outperforms previous classification proposed models and the predictive value of individual ECG indices of risk. Although the predictive value of the CNN does not show a better long-term VA predictive value than sex and age alone, it provides a significant contribution independently from these two risk factors. Our findings support the use of the ECG to improve long-term VA risk stratification, which might be particularly useful in age- and sex-stratified analyses.

Evaluating the feasibility of upper arm ECG for cardiac monitoring

Abstract Background Ambulatory electrocardiogram (ECG) monitoring is essential for detecting paroxysmal cardiac events and tracking long-term physiological parameters. However, traditional Holter devices are cumbersome, restrict movement, and can cause skin irritation, resulting in sub-optimal patient compliance during extended monitoring periods. Wearable devices utilizing non-conventional form factors are emerging as promising non-intrusive alternatives for ECG monitoring. Purpose To determine the optimal location for single-lead ECG acquisition on the upper arm and assess the feasibility of using dry electrode armbands for cardiac monitoring when coupled with robust ECG processing software. Methods Stationary ECGs from 20 healthy adult participants were recorded across a two-phase data collection protocol and processed with clinically validated ECG software. In the first phase, the average signal amplitudes of wet-electrode ECGs obtained from 30 different configurations across the mid-brachial and proximal-brachial regions of the upper arm were assessed. In the second, the performance of two proprietary dry-electrode armbands, featuring either a single-point or three-point electrode strap, were evaluated on the optimal upper arm configuration using QRS detection and heart rate (HR) metrics. Comparative analysis was performed throughout using data simultaneously acquired on a gold standard Holter device in modified lead III (MLIII) configuration. Performance metrics were calculated relative to manually generated signal annotations. Results Wet-electrode-acquired ECGs from the proximal-brachial region exhibited significantly higher average signal amplitudes (0.12 ± 0.06 mV) compared to those from the mid-brachial region (0.05 ± 0.04 mV) of the upper arm (p &amp;lt; 0.05). The optimal configuration (A-F), positioned at the lateral and medial extents of the proximal-brachial region, demonstrated comparable average signal amplitudes (0.16 ± 0.07 mV, 0.16 ± 0.06 mV, 0.19 ± 0.08 mV) for single-point dry, three-point dry, and wet-electrode setups, respectively. Upon processing with ECG software, data from optimal dry-electrode configurations showed excellent average QRS sensitivity and specificity of 98.2% and 98.9% for the one-point strap, and 98.9% and 99.8% for the three-point strap, respectively. HR metrics from the optimized dry-electrode setups were comparable in accuracy to those derived from the reference Holter device, with the average HR error being similar across the three-point dry electrode strap (0.57% ± 0.28%), one-point dry electrode strap (1.16% ± 1.46%), and Holter device (0.74% ± 1.50%). Conclusion(s) Optimized upper arm single-lead ECG with dry electrodes can yield signals with sufficient amplitude and quality for automated ECG analysis. Coupled with robust ECG signal processing software, the cardiac metrics obtained from the optimized upper arm setups exhibited comparable performance to those derived from medical Holter data.

Zebra II as A Novel System to Record Electrophysiological Signals in Zebrafish.

Zebrafish and their mutant lines have been extensively used in biomedical investigations, cardiovascular studies, and drug screening. In the current study, the commercial version of the novel system, Zebra II, is presented. The protocol demonstrates electrocardiogram (ECG) acquisition and analysis from multiple zebrafish within controllable working environments. The device is composed of an external and independent perfusion system, a 4-point electrode, temperature sensors, and an embedded electronic system. In previous studies, the device prototype underwent validation against the established iWORX system through several tests, demonstrating similar data quality and ECG response to drug interventions. Following this, the study delved into examining the impact of anesthetic drugs and temperature fluctuations on zebrafish ECG, necessitating instant data evaluation. Thanks to the apparatus's capacity for consistent delivery of anesthetics and drugs, it was possible to extend ECG data collection up to 1 h, markedly longer than the 5 min duration supported by current systems. This paper introduces a pioneering, cloud-based, automated analysis utilizing data from four zebrafish, offering an efficient method for conducting combination experiments and significantly reducing time and effort. The system proved effective in capturing and analyzing ECG, especially in detecting drug-induced arrhythmias in wild-type zebrafish. Additionally, the capability to gather data across multiple channels facilitated the execution of randomized controlled trials with zebrafish models. The developed ECG system overcomes existing limitations, showing the potential to greatly expedite drug discovery and cardiovascular research involving zebrafish.

AI-enhanced reconstruction of the 12-lead electrocardiogram via 3-leads with accurate clinical assessment

The 12-lead electrocardiogram (ECG) is an integral component to the diagnosis of a multitude of cardiovascular conditions. It is performed using a complex set of skin surface electrodes, limiting its use outside traditional clinical settings. We developed an artificial intelligence algorithm, trained over 600,000 clinically acquired ECGs, to explore whether fewer leads as input are sufficient to reconstruct a 12-lead ECG. Two limb leads (I and II) and one precordial lead (V3) were required to generate a reconstructed 12-lead ECG highly correlated with the original ECG. An automatic algorithm for detection of ECG features consistent with acute myocardial infarction (MI) performed similarly for original and reconstructed ECGs (AUC = 0.95). When interpreted by cardiologists, reconstructed ECGs achieved an accuracy of 81.4 ± 5.0% in identifying ECG features of ST-segment elevation MI, comparable with the original 12-lead ECGs (accuracy 84.6 ± 4.6%). These results will impact development efforts to innovate ECG acquisition methods with simplified tools in non-specialized settings.

A Qualitative Analysis of Barriers to Evidence-Based Care in the Prehospital Management of Patients with Suspected Acute Coronary Syndrome

Objectives Prehospital electrocardiogram (ECG) and administration of aspirin are evidence-based strategies for patients with acute coronary syndrome (ACS). However, emergency medical services (EMS) compliance in patients with suspected ACS varies widely. We sought to understand the barriers to prehospital ECG acquisition and aspirin administration for patients with suspected ACS. Methods In this qualitative study, we interviewed EMS clinicians at three geographically diverse United States (U.S.)-based EMS agencies. We interviewed practicing clinicians and quality and operations leaders at these agencies. Based on the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, interviews were recorded, transcribed, and analyzed using a grounded qualitative approach with open coding. The Systems Engineering Initiative for Patient Safety (SEIPS) framework and a constant comparison technique were used to identify and refine themes. Results Twenty-five paramedics and 20 additional agency personnel participated. Median age was 41 (IQR: 34–51) years and 13 (29%) were female. Themes were organized using SEIPS and longitudinally through four phases of an EMS call. During the pre-arrival phase, staffing challenges, training quality, and dispatch may anchor EMS clinicians on a diagnosis. During the diagnosis and treatment phase, safety and communication barriers may take priority over care delivery. Additionally, EMS clinicians must allocate assets (e.g. whether to send an advanced life support unit) and financial resources; veteran EMS clinicians identified their experience whereas newer clinicians cited their recent education when making these decisions. Also, diagnostic uncertainty due to increasing patient complexity and atypical presentations contributed to diagnostic errors. During the response and transport phase, the scope of practice limits the use and interpretation of the ECG, with clinicians reporting that liberal use of ECG led to more rapid decision-making. Finally, in the after phase, personnel reported the “psychologically taxing” nature of the job contributing to biases, bad habits, and burnout. Performance feedback was desired for personal development, though currently perceived as infrequent and punitive. Conclusions Multiple, interrelated themes underscored the complexities of delivering evidence-based care to prehospital patients with ACS. Education in ECG interpretation, resource allocation, bias, and enhancing feedback may serve as strategies to address the identified barriers.

QTc-verlenging door antipsychoticagebruik binnen de kinder- en jeugdpsychiatrie: een systematische literatuurstudie

QTc prolongation associated with antipsychotic treatment in child and adolescent psychiatry: a systematic review Antipsychotics play a significant role in the treatment of psychiatric disorders. Monitoring of metabolic dysregulation and cardiac rhythm disturbances is essential. Given that the pharmacokinetic profile of children differs from that of adults, the literature cannot be extrapolated without consideration. Therefore, a systematic review on the risk of QTc prolongation in minors receiving antipsychotic treatment was conducted. The literature review was conducted within PubMed, Embase and Web of Science, focusing on randomized controlled trials (RCTs). Antipsychotics were selected based on the Belgian and Dutch formulary for the treatment of psychiatric disorders in minors. The study included 28 RCTs that examined the effect and safety of antipsychotic treatment in minors, including cardiac risks. The occurrence of clinically relevant QTc prolongation development is relatively rare. The comparison of the results was hindered by differences in the acquisition and correction of electrocardiograms (ECGs). It is advisable to identify risk factors through history-taking and blood sampling. Current research indicates that the risk of QTc prolongation and torsade de pointes in minors receiving antipsychotic treatment is relatively rare. However, vigilance is warranted in the presence of specific risk factors, in which case ECG monitoring is recommended.

Design and Analysis of a High-Gain, Low-Noise, and Low-Power Analog Front End for Electrocardiogram Acquisition in 45 nm Technology Using gm/ID Method

In this work, an analog front-end (AFE) circuit for an electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using an advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth band-pass filter (with fifth-order low-pass and second-order high-pass sections), and a second-order notch filter—all are based on two-stage, Miller-compensated operational transconductance amplifiers (OTA). The OTAs have been designed employing the gm/ID methodology. Both the pre-layout and post-layout simulation are carried out. The layout consumes an area of 0.00628 mm2 without the resistors and capacitors. Analysis of various simulation results are carried out for the proposed AFE. The circuit demonstrates a post-layout bandwidth of 239 Hz, with a variable gain between 44 and 58 dB, a notch depth of −56.4 dB at 50.1 Hz, a total harmonic distortion (THD) of −59.65 dB (less than 1%), an input-referred noise spectral density of &lt;34 μVrms/Hz at the pass-band, a dynamic range of 52.71 dB, and a total power consumption of 10.88 μW with a supply of ±0.6 V. Hence, the AFE exhibits the promise of high-quality signal acquisition capability required for portable ECG detection systems in modern healthcare.

Study Protocol for the Artificial Intelligence-Driven Evaluation of Structural Heart Diseases Using Wearable Electrocardiogram (ID-SHD).

Portable devices capable of electrocardiogram (ECG) acquisition have the potential to enhance structural heart disease (SHD) management by enabling early detection through artificial intelligence-ECG (AI-ECG) algorithms. However, the performance of these AI algorithms for identifying SHD in a real-world screening setting is unknown. To address this gap, we aim to evaluate the validity of our wearable-adapted AI algorithm, which has been previously developed and validated for detecting SHD from single-lead portable ECGs in patients undergoing routine echocardiograms in the Yale New Haven Hospital (YNHH). This is the protocol for a cross-sectional study in the echocardiographic laboratories of YNHH. The study will enroll 585 patients referred for outpatient transthoracic echocardiogram (TTE) as part of their routine clinical care. Patients expressing interest in participating in the study will undergo a screening interview, followed by enrollment upon meeting eligibility criteria and providing informed consent. During their routine visit, patients will undergo a 1-lead ECG with two devices - one with an Apple Watch and the second with another portable 1-lead ECG device. With participant consent, these 1-lead ECG data will be linked to participant demographic and clinical data recorded in the YNHH electronic health records (EHR). The study will assess the performance of the AI-ECG algorithm in identifying SHD, including left ventricular systolic dysfunction (LVSD), valvular disease and severe left ventricular hypertrophy (LVH), by comparing the algorithm's results with data obtained from TTE, which is the established gold standard for diagnosing SHD. All patient EHR data required for assessing eligibility and conducting the AI-ECG will be accessed through secure servers approved for protected health information. Data will be maintained on secure, encrypted servers for a minimum of five years after the publication of our findings in a peer-reviewed journal, and any unanticipated adverse events or risks will be reported by the principal investigator to the Yale Institutional Review Board, which has reviewed and approved this protocol (Protocol Number: 2000035532).

Mobile ECG devices in pediatric cardiology: efficacy and accuracy of smart watch and hand-held device in healthy children

Abstract Background and Aim Pediatric arrhythmias require accurate electrocardiogram (ECG) assessment for diagnosis and management. Mobile ECG devices, such as smart watch based and hand-held devices, offer potential advantages in terms of portability and accessibility. This study aimed to compare the efficancy and diagnostic accuracy of ECG tracings obtained using a smart watch and a hand-held device with standard 12-lead ECG tracings in healthy children. Methods ECG data were acquired from healthy pediatric subjects (n=30) aged 4 to 18 years using the smart watch, hand-held device, and standard 12-lead ECG. Single leads were acquired sequentially. The time required for ECG acquisition was recorded for each device. Subsequently, ECG leads were analyzed for voltage, duration, and morphology of waves and intervals, as well as ST segments. Qualitative assessment of leads included morphology comparison of waves and complexes to standard 12-lead ECG and using a physician based quality rating scale. Assessment was performed by two blinded pediatric electrophysiologists. Results We found significant differences in the time required to acquire ECG tracings between the smart watch, standard 12-lead ECG, and hand-held device, with the smart watch being notably slower (median 15 min, 4 min, 1 min, respectively). ECG leads obtained with the smart watch and hand-held device demonstrated comparability to standard 12-lead ECG tracings in terms of quantitative key parameters, including voltage (mV) and duration (ms) of waves and intervals. Qualitative assessment revealed good correlation of the hand-held device with standard 12-lead ECG, while the smart watch performed less well. Conclusions This study suggests that mobile ECG devices offer a diagnostic accuracy comparable to that of standard 12-lead ECGs in healthy children. While the smart watch may be slower in ECG acquisition, acquisition of all 12 standard ECG leads is feasible in this population. The potential of both included mobile devices for pediatric usage in specific clinical scenarios, such as remote monitoring or emergencies, is promising.Graphical Abstract

Prediction of Atrial Fibrillation with Single-Lead Mobile ECG during Normal Sinus Rhythm using Deep Learning

Abstract Background Single-lead ECG (electrocardiogram) acquisition from mobile devices provides more convenient way to identify arrhythmia. Prediction of atrial fibrillation (AF) by artificial intelligence facilitates AF screening. However, little is known about the prediction of AF with a single-lead ECG. Therefore, we proposed a method for predicting AF using single-lead mobile ECG during normal sinus rhythm (NSR). Methods Three deep learning models RNN, LSTM, and Resnet50 were used. Out of 13,509 ECGs from a total of 6,719 patients, we utilized a total of 10,287 NSR ECGs from 5,170 patients. Single-lead mobile ECGs were adjusted with noise filtering and segmented every 10 seconds. A random under-sampling was applied to reduce bias from data imbalance. Finally, 31,767 ECG segments composed of 15,157 segments labeled as potential AF and 16,610 segments labeled as healthy were entered for final analysis. Results The ResNet50 showed recall of 79.3%, precision of 65.8%, F1-score of 71.9%, accuracy of 70.5%, and AUC of 0.79 for the prediction of AF with NSR ECGs. These scores were higher than the other two models followed by RNN at 0.75 and LSTM at 0.74. For the external validation set, ResNet50 showed an F1-score of 64.1%, recall of 68.9%, precision of 60.0%, accuracy of 63.4%, and AUC of 0.68 for the prediction of AF with NSR ECGs. Conclusion A deep learning model using a single-lead mobile ECG during NSR predicted AF at risk in future. However, further study is needed to improve the performance of deep learning models for applying in the clinical setting.

ROLE OF ST–SEGMENT ELEVATION AT POST–ROSC ECG IN PATIENTS RESUSCITATED FROM OUT–OF–HOSPITAL CARDIAC ARREST

Abstract Background A 12–lead electrocardiogram (ECG), performed after return of spontaneous circulation (ROSC) in out–of–hospital cardiac arrest (OHCA), is essential for the identification of ST–segment elevation). The aim of the study is to investigate whether the presence of ST elevation may be a prognostic determinant in post–OHCA patients. Materials and Methods A total of 878 post–ROSC ECGs were collected from patients enrolled from 01/01/2015 to 12/31/2022 from the Lombardy Region Cardiac Arrest Registry (LombardiaCARe). Univariable and multivariable Cox regression models were applied to test the association between the presence of and morphology of supraST and mortality at 30 days after the event. Results Among the 878 post–ROSC ECGs, 406 (46.2%) had ST elevation [49 with tombstone morphology; 26 with lambda morphology; 45 with triangular morphology; and 8 with wave morphology]. Patients with ST elevation were more frequently males (73% vs 59%, p&amp;lt;0.001), younger [66 years old (57–75) vs 71 (61–80), p&amp;lt;0.01], and with a more frequently non–defibrillating rhythm of presentation (25%vs59%, p&amp;lt;0.01) and an event more frequently witnessed by 118 (30% vs 21%, p=0.008). Thirty–day survival was higher in patients with ST elevation (56% vs 41%, p&amp;lt;0.001). Correcting for variables associated with 30–day survival (age, sex, QRS amplitude, ECG acquisition time, first rhythm of presentation), the presence of ST elevation was not significantly associated with 30–day mortality [HR: 0.67 (95% CI: 0.27–1.64); p=0.05], while the number of segments was [1.1 (95%CI 0.85–0.15), p=0.004]. No morphology was independently associated with mortality when compared with standard morphology. Conclusions The presence of ST elevation on ECG post ROSC possesses, in addition to a known diagnostic role, a prognostic role in predicting 30–day survival especially if present in more than one segment.

Establishment of a digital electrocardiogram telemonitoring system and remote consultation service.

The use of telemonitoring electrocardiogram (ECG) improves the detection rate of various arrhythmias as it offers an extended recording time and does not impose activity restrictions. However, many community hospitals lack the resources to conduct this test due to a lack of cardiac function specialists and antiquated screening equipment. To establish a digital ECG telemonitoring system and remote consultation service between tertiary hospitals and community medical service stations to improve diagnosis and treatment effectiveness. We used the PI ECG telemonitoring data acquisition system, with a personal digital assistant (PDA) or computer serving as the platform for ECG acquisition, storage, display, printing, and transmission. We introduced this system to standardize the storage and transmission of ECG telemonitoring data, and ensure accurate transmission, reproducibility, and preservation of data. The implementation of the PI ECG telemonitoring data acquisition system enabled the sharing of remote ambulatory electrocardiography data between Beijing Shijitan Hospital and its affiliated community hospitals. This has resulted in reduced waiting times for patients to receive reports (from 0.3 to 1 hour, to 0.1 or less), shortened consultation times (from 2 hours to 0.1 hour), and improved patient satisfaction with consultations (> 20% of the patients reported they were highly satisfied). The system successfully combined the goals of intelligence, real-time synchronization, and efficiency, revolutionizing ECG telemonitoring diagnosis in the network era. Sharing digital telematics information between tertiary and community hospitals enables a tiered approach to diagnosis and treatment and allows for more efficient, accurate, and convenient remote diagnostics.

Influence of time-to-diagnosis on time-to-percutaneous coronary intervention for emergency department ST-elevation myocardial infarction patients: Time-to-electrocardiogram matters.

Earlier electrocardiogram (ECG) acquisition for ST-elevation myocardial infarction (STEMI) is associated with earlier percutaneous coronary intervention (PCI) and better patient outcomes. However, the exact relationship between timely ECG and timely PCI is unclear. We quantified the influence of door-to-ECG (D2E) time on ECG-to-PCI balloon (E2B) intervention in this three-year retrospective cohort study, including patients from 10 geographically diverse emergency departments (EDs) co-located with a PCI center. The study included 576 STEMI patients excluding those with a screening ECG before ED arrival or non-diagnostic initial ED ECG. We used a linear mixed-effects model to evaluate D2E's influence on E2B with piecewise linear terms for D2E times associated with time intervals designated as ED intake (0-10 min), triage (11-30min), and main ED (>30min). We adjusted for demographic and visit characteristics, past medical history, and included ED location as a random effect. The median E2B interval was longer (76vs 68 min, p<0.001) in patients with D2E>10 min than in those with timely D2E. The proportion of patients identified at the intake, triage, and main ED intervals was 65.8%, 24.9%, and 9.7%, respectively. The D2E and E2B association was statistically significant in the triage phase, where a 1-minute change in D2E was associated with a 1.24-minute change in E2B (95% confidence interval [CI]: 0.44-2.05, p=0.003). Reducing D2E is associated with a shorter E2B. Targeting D2E reduction in patients currently diagnosed during triage (11-30 min) may be the greatest opportunity to improve D2B and could enable 24.9% more ED STEMI patients to achieve timely D2E.

Patient-independent, MHD-robust R-peak detection for retrospective gating in cardiac MRI imaging

Objective. In cardiovascular magnetic resonance imaging, synchronization of image acquisition with heart motion (called gating) is performed by detecting R-peaks in electrocardiogram (ECG) signals. Effective gating is challenging with 3T and 7T scanners, due to severe distortion of ECG signals caused by magnetohydrodynamic effects associated with intense magnetic fields. This work proposes an efficient retrospective gating strategy that requires no prior training outside the scanner and investigates the optimal number of leads in the ECG acquisition set. Approach. The proposed method was developed on a data set of 12-lead ECG signals acquired within 3T and 7T scanners. Independent component analysis is employed to effectively separate components related with cardiac activity from those associated to noise. Subsequently, an automatic selection process identifies the components best suited for accurate R-peak detection, based on heart rate estimation metrics and frequency content quality indexes. Main results. The proposed method is robust to different B0 field strengths, as evidenced by R-peak detection errors of 2.4 ± 3.1 ms and 10.6 ± 15.4 ms for data acquired with 3T and 7T scanners, respectively. Its effectiveness was verified with various subject orientations, showcasing applicability in diverse clinical scenarios. The work reveals that ECG leads can be limited in number to three, or at most five for 7T field strengths, without significant degradation in R-peak detection accuracy. Significance. The approach requires no preliminary ECG acquisition for R-peak detector training, reducing overall examination time. The gating process is designed to be adaptable, completely blind and independent of patient characteristics, allowing wide and rapid deployment in clinical practice. The potential to employ a significantly limited set of leads enhances patient comfort.

Design and Development of ECG Monitoring Cloud Platform Based on the Internet of Things Electrocardiograph

The design and development of electrocardiogram（ECG） monitoring cloud platform based on the Internet of Things（IoT） electrocardiograph is introduced. The platform is mainly composed of ECG acquisition module, algorithm module, diagnostic model comparison module, warning module, positioning module and medical aid system. The ECG acquisition module collects ECG signals of patients and displays them in real time on the mobile terminals. Then they are uploaded to the ECG monitoring cloud platform through the IoT. The algorithm module and the diagnostic model comparison module mark, process, analyze and diagnose the ECG. Meanwhile, the ECG diagnosis and warning results are pushed to patients and 120 emergency centers through the IoT. Furthermore, the cloud platform will guide patients to self-rescue and the emergency platform will open the green channel to save patients in time.The platform realizes from the onset to diagnosis and treatment in one step, and saves lives against time.

Wearable 12-Lead ECG Acquisition Using a Novel Deep Learning Approach from Frank or EASI Leads with Clinical Validation.

The 12-lead electrocardiogram (ECG) is crucial in assessing patient decisions. However, portable ECG devices capable of acquiring a complete 12-lead ECG are scarce. For the first time, a deep learning-based method is proposed to reconstruct the 12-lead ECG from Frank leads (VX, VY, and VZ) or EASI leads (VES, VAS, and VAI). The innovative ECG reconstruction network called M2Eformer is composed of a 2D-ECGblock and a ProbDecoder module. The 2D-ECGblock module adaptively segments EASI leads into multi-periods based on frequency energy, transforming the 1D time series into a 2D tensor representing within-cycle and between-cycle variations. The ProbDecoder module aims to extract Probsparse self-attention and achieve one-step output for the target leads. Experimental results from comparing recorded and reconstructed 12-lead ECG using Frank leads indicate that M2Eformer outperforms traditional ECG reconstruction methods on a public database. In this study, a self-constructed database (10 healthy individuals + 15 patients) was utilized for the clinical diagnostic validation of ECG reconstructed from EASI leads. Subsequently, both the ECG reconstructed using EASI and the recorded 12-lead ECG were subjected to a double-blind diagnostic experiment conducted by three cardiologists. The overall diagnostic consensus among three cardiology experts, reaching a rate of 96%, indicates the significant utility of EASI-reconstructed 12-lead ECG in facilitating the diagnosis of cardiac conditions.

12-Lead ECG Reconstruction Based on Data From the First Limb Lead.

Electrocardiogram (ECG) data obtained from 12 leads are the most common and informative source for analyzing the cardiovascular system's (CVS) condition in medical practice. However, the large number of electrodes, specific placements on the body, and the need for specialized equipment make the ECG acquisition procedure complex and cumbersome. This raises the challenge of reducing the number of ECG leads by reconstructing missing leads based on available data. Most existing methods for reconstructing missing ECG leads rely on utilizing signals simultaneously from multiple known leads. This study proposes a method for reconstructing ECG data in 12 leads using signal data from the first lead, lead I. Such an approach can significantly simplify the ECG registration procedure. The study demonstrates the effectiveness of using unique models with a developed architecture of artificial neural networks (ANNs) to generate the reconstructed ECG signals. Fragments of ECG from lead I, with a duration of 128 samples and a sampling frequency of 100 Hz, are input to the models. ECG fragments can be extracted from the original signal at arbitrary time points. Each model generates an ECG signal of the same length at its output for the corresponding lead. Despite existing limitations, the proposed method surpasses known solutions regarding ECG generation quality when using a single lead. The study shows that introducing an additional feature of the direction of the electrical axis of the heart (EAH) as input to the ANN models enhances the generation quality. The quality of ECG generation by the proposed ANN models is found to be dependent on the presence of CVS diseases. The developed ECG reconstruction method holds significant potential for use in portable registration devices, screening procedures, and providing support for medical decision-making by healthcare specialists.

Multisensor Integrated Platform Based on MEMS Charge Variation Sensing Technology for Biopotential Acquisition.

We propose a new methodology for long-term biopotential recording based on an MEMS multisensor integrated platform featuring a commercial electrostatic charge-transfer sensor. This family of sensors was originally intended for presence tracking in the automotive industry, so the existing setup was engineered for the acquisition of electrocardiograms, electroencephalograms, electrooculograms, and electromyography, designing a dedicated front-end and writing proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in a domestic environment will be discussed in detail. The excellent results indicate that this technology can provide a low-power, unexplored solution to biopotential acquisition. The technological breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, it opens up additional possibilities for wearable sensors and strengthens the role of MEMS technology in medical wearables for the long-term synchronous acquisition of a wide range of signals.