Electrocardiogram Acquisition Research Articles

Comparison of quantifiable electrocardiographic changes associated with severe hyperkalemia

BackgroundHyperkalemia (HK) is a life-threatening condition that is frequently evaluated by electrocardiogram (ECG). ECG changes in severe HK (≥ 6.3 mEq/L) are not well-characterized. This study sought to compare and correlate ECG metrics in severe HK to baseline normokalemic ECGs and serum potassium. MethodsA retrospective analysis of 340 severe HK encounters with corresponding normokalemic ECGs was performed. ResultsVarious ECG metrics were analyzed. P wave amplitude in lead II, QRS duration, T wave slope, ratio of T wave amplitude: duration, and ratios of T wave: QRS amplitudes were significantly different between normokalemic and HK ECGs. P wave amplitude attenuation in lead II correlated better with serum potassium than in V1. T wave metrics that incorporated both T wave and QRS amplitudes correlated better than metrics utilizing T wave metrics alone. ConclusionMultiple statistically significant and quantifiable differences among ECG metrics were observed between normokalemic and HK ECGs and correlated with increasing degrees of serum potassium and along the continuum of serum potassium. When incorporated into a logistic regression model, the ability to distinguish HK versus normokalemia on ECG improved significantly. These findings could be integrated into an ECG acquisition system that can more accurately identify severe HK.

Telemedicine in the Era of a Pandemic: Usefulness of a Novel Three-Lead ECG.

The 12-lead electrocardiogram (ECG) is a first-line diagnostic tool for patients with cardiac symptoms. As observed during the COVID-19 pandemic, the ECG is essential to the initial patient evaluation. The novel KardioPal three-lead-based ECG reconstructive technology provides a potential alternative to a standard ECG, reducing the response time and cost of treatment and improving patient comfort. Our study aimed to evaluate the diagnostic accuracy of a reconstructed 12-lead ECG obtained by the KardioPal technology, comparing it with the standard 12-lead ECG, and to assess the feasibility and time required to obtain a reconstructed ECG in a real-life scenario. A prospective, nonrandomized, single-center, adjudicator-blinded trial was conducted on 102 patients during the COVID-19 pandemic at the Dedinje Cardiovascular Institute in Belgrade. The KardioPal system demonstrated a high feasibility rate (99%), with high specificity (96.3%), sensitivity (95.8%), and diagnostic accuracy (96.1%) for obtaining clinically relevant matching of reconstructed 12-lead compared to the standard 12-lead ECG recording. This novel technology provided a significant reduction in ECG acquisition time and the need for personnel and space for obtaining ECG recordings, thereby reducing the risk of viral transmission and the burden on an already overwhelmed healthcare system such as the one experienced during the COVID-19 pandemic.

Application of the Alivecor KardiaMobile smartphone modulated ECG device for use in horses.

Electrocardiography is the method used to monitor the electrical impulses in the heart. These diagnostics are increasingly making use of smartphone-based technologies. The objective of this research was to determine whether the Alivecor KardiaMobile (ECGAKM) smartphone-modulated electrocardiographic device, a novel ECG device, can be used to obtain reliable electrocardiogram (ECG) readings in horses. The device was initially tested in 36 Nooitgedacht pony mares to determine the best site of application, method of skin preparation, and ECGAKM device orientation for reliable ECG tracings. Once the most reliable site for ECG acquisition was determined, the device was then applied, in this manner, to 31 Nooitgedacht pony mares and compared with a standard telemetric ECG system (ECGTV). The ECGAKM device was best applied in the fourth intercostal space on the left hemithorax in a vertical orientation and with the skin dampened with 70% ethanol. Mean values determined for RR and QT intervals between the ECGAKMS and ECGTV were not significantly different, however, mean values for the duration of the QRS complexes were significantly different for the two devices. There is acceptable agreement between the ECGTV and ECGAKM devices with regards to the measurement of the PQ; RR and QT intervals but not the QRS duration. The automatically calculated heart rate is not an accurate measure of true heart rate. The Alivecor KardiaMobile (ECGAKM) device can be considered as a simplified screening ECG device in situations where the more standardised system is either unavailable or impractical, but has some limitations.

A Multi-channel System Architecture Design for Myocardial Infarction Prediction

Cardiac and cerebral infarctions represent the most perilous pathologies within the spectrum of cardiovascular and cerebrovascular diseases, with grave consequences following their onset. However, prompt diagnosis and treatment can substantially enhance the chances of successful rescue and avert severe outcomes. Therefore, the development of a non-invasive, low-cost, and easy-to-use cardiac and cerebral infarction prediction system for home and personal use bears considerable social and economic significance.The electrocardiogram (ECG), acupoint electrical signals, and pulse, as physiological signals, contain abundant information on human health status. Integrating the information from these three signals can offer a more comprehensive and accurate risk prediction of cardiovascular and cerebrovascular diseases in patients. This approach represents a promising avenue for advancing the field of healthcare and disease management.Firstly, we analyzed the myocardial infarction and cerebral infarction prediction system requirements. The research determines four pulse collection points, two ECG, and ten acupoint electricity collection points. Then, we completed the cardiovascular and cerebrovascular disease risk warning system’s overall architecture design and front-end scheme design of ECG, acupoint electricit and pulse wave acquisition. The system successfully collects data from the test subjects by connecting to the computer.

Removal of Motion Artifacts in Capacitive Electrocardiogram Acquisition: A Review.

Capacitive electrocardiogram (cECG) systems are increasingly used for the monitoring of cardiac activity. They can operate within the presence of a small layer of air, hair or cloth and do not require a qualified technician. They can be integrated into wearables, clothing or objects of daily life, such as beds or chairs. While they offer many advantages over conventional electrocardiogram systems (ECG) that rely on wet electrodes, they are more prone to be affected by motion artifacts (MAs). These effects, which are due to the relative movement of the electrode in relation to the skin, are several orders of magnitude higher than ECG signal amplitudes, they occur in frequencies that might overlap with the ECG signal, and they may saturate the electronics in the most severe cases. In this paper, we provide a detailed description of MA mechanisms that translate into capacitance variations due to electrode-skin geometric changes or into triboelectric effects due to electrostatic charge redistribution. A state-of-the-art overview of the different approaches based on materials and construction, analog circuits and digital signal processing is provided as well as the trade-offs to be made using these techniques, to mitigate MAs efficiently.

Technical characterization of the single-lead electrocardiogram signal from four different smartwatches and its clinical implications

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Department of Cardiology, University Hospital Basel, University of Basel, Switzerland Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland Background Smartwatches with the capability of single-lead electrocardiograms (ECGs) acquisition have been established as valuable tool for the detection of atrial fibrillation (AF). For the currently available smartwatches, however, the technical specification of the acquired ECG signal is unclear. Purpose The aim of the current study was to characterize quantitatively the raw signal of the single-lead ECG of four currently available smartwatches (Apple watch series 6, Withings ScanWatch, Samsung Galaxy Watch 4, Fitbit Charge 5) and assess their clinical applicability based on their technical features. Method A bench test was performed applying a synthetic, well-characterized signal to the electrodes of the watches (Figure: (A) Apple watch series 6, (B) Withings ScanWatch, (C) Samsung Galaxy Watch 4, (D) Fitbit Charge 5). The frequency response to this input signal was acquired and Bode plots were calculated (Figure, second column: Bode diagrams show the frequency response (upper) and the phase angles (lower)). To compare the ECG characteristics of the four watches, the synthetical response to a pre-recorded surface ECG dataset was calculated based on the frequency characteristics determined above (Figure, third column: The synthetical raw signal is shown in grey, the response of the watch in blue). Results The frequency responses of all four watches shows a bandpass filter, with an additional 50/60 Hz narrow-band filter (Figure). While the recommended high-pass cut-off at 0.5 Hz was fulfilled by all devices (1), a low-pass cut-off was observed for all devices below 70Hz (instead of 150Hz) resulting in a significantly damped signal. In detail, a damping of -6dB, corresponding to a signal amplitude reduction of 50%, was observed above 70 Hz and below 0.4 Hz for all watches. A QRS complex amplitude reduction was observed for the Apple, Withings and Samsung watch. A pronounced noise filtering is visible for the Samsung and Fitbit Smartwatch. A difference for the clinically relevant characteristic fiducial points, such as the onset of the P-wave or QRS complex, cannot be observed in these examples. However, when using the tangent method to detect the end of the T-wave to calculate the QTc value, a difference between the measures can be observed for the Withings Watch. Conclusion The technical characteristics in terms of filtering characteristics of the single-lead ECG from four commercially available smartwatches differ and must be taken into consideration when attempting to use smartwatches beyond the detection of AF.

Wireless CardioS framework for continuous ECG acquisition

A first-level textile-based electrocardiogram (ECG) monitoring system referred to as “CardioS” (cardiac sensor) for continuous health monitoring applications is proposed in this study to address the demand for resource-constrained environments. and the signal quality assessment of a wireless CardioS was studied. The CardioS consists of a Lead-I ECG signal recorded wirelessly using silver-plated nylon woven (Ag-NyW) dry textile electrodes to compare the results of wired wearable Ag-NyW textile electrode-based ECG acquisition system and CardioS. The effect of prolonged usage of Ag-NyW dry electrodes on electrode impedance was tested in the current work. In addition, electrode half-cell potential was measured to validate the range of Ag-NyW dry electrodes for ECG signal acquisition. Further, the quality of signals recorded by the proposed wireless CardioS framework was evaluated and compared with clinical disposable (Ag–AgCl Gel) electrodes. The signal quality was assessed in terms of mean magnitude coherence spectra, signal cross-correlation, signal-to-noise-band ratio (S band/N band), crest factor, low and high band powers and power spectral density. The experimental results showed that the impedance was increased by 2.5–54.6% after six weeks of continuous usage. This increased impedance was less than 1 MΩ/cm2, as reported in the literature. The half-cell potential of the Ag-NyW textile electrode obtained was 80 mV, sufficient to acquire the ECG signal from the human body. All the fidelity parameters measured by Ag-NyW textile electrodes were correlated with standard disposable electrodes. The cardiologists validated all the measurements and confirmed that the proposed framework exhibited good performance for ECG signal acquisition from the five healthy subjects. As a result of its low-cost architecture, the proposed CardioS framework can be used in resource-constrained environments for ECG monitoring.

Validation of the detection of ischemia using 12 lead smartphone based electrocardiography - a non-randomized, single blinded, cross-sectional, multicenter study

Background: Reliable and early detection of myocardial ischemia using computer-aided analysis of electrocardiograms (ECG) provides an important reference for early diagnosis of CVD. We developed a 12-lead smartphone-based electrocardiogram (ECG) acquisition and monitoring system (called “Spandan”), and an application to assess underlying ischemia from analysis of electrocardiographic (ECG) signals only. Objectives of this study were to validate the Spandan 12 lead ECG interpretation for accuracy in the detection of Ischemia in comparison to cardiologists’ diagnosis and to evaluate the accuracy of ischemia in comparison to the interpretation of standard 12 lead ECG. Methods: In this multi-center study all patients (n=597) visiting the ECG room at the department of cardiology were enrolled in the study by taking their written consent and explaining the purpose of the study. Results: Mean age was 52.85 years. The male gender (n=344, 57.62%) shows the maximum frequency than female gender. 12 lead Spandan smartphone ECG recorded fewer false positive cases (8 versus 230) and identified greater true negative cases (310 versus 115). Spandan smartphone ECG recorded better specificity (97.4% versus 33.3%) and positive predictive value (87.4% versus 51.4%) as compared to gold standard ECG. The accuracy of interpretation of Ischemia by cardiologists diagnosis through 12 lead Spandan smartphone ECG was better (100%) as compared gold standard (95.3%). Conclusions: Our study highlights the potential of Spandan smartphone ECG in the detection of myocardial ischemia. This may improve patient satisfaction and reduce healthcare costs.

ECG-based cardiodynamicsgram can reflect anomalous functional information in coronary artery disease.

The cardiodynamicsgram (CDG), a novel noninvasive method, extracts dynamic ST-T segment information from an electrocardiogram (ECG) through deterministic learning. The CDG can reflect anomalous functional information in coronary artery disease (CAD). We retrospectively enrolled 456 patients with suspected CAD who underwent coronary computed tomography angiography (CCTA) from January 2020 to 2022, followed immediately by standard 12-lead ECG acquisition. Positivity for CAD were defined as CCTA ≥ 50% or CT-derived fractional flow reserve (CT-FFR) ≤ 0.8. A CDG value <0 was considered negative; otherwise, it was considered positive. We also evaluated the diagnostic performance of the CDG in the ECG-diagnosis-negative subgroup and in patients who had undergone invasive coronary angiography (ICA) after CCTA. Of 362 patients, 168 (46.41%) were positive for CAD, and 178 (49.17%) were men. The median age was 59 (52-66) years. The accuracy of the CDG in the diagnosis of CAD was 79.56%, with a sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) of 75.60%, 82.99%, and 0.836 (95% CI: 0.794-0.878), respectively. Similarly, in the ECG-diagnosis-negative subgroup (n = 223), the accuracy of the CDG was 80.27%, with an AUC of 0.842 (95% CI: 0.790-0.895). Among the 11 patients with CAD confirmed by ICA, 10 were diagnosed positive by the CDG. Furthermore, the CDG values and CT-FFR were correlated (r = -.395; p < .001). The ECG-based CDG has relatively high specificity and accuracy for the diagnosis of CAD and reflects functional cardiac information to some extent. It has the potential to be used as a screening tool for suspected CAD patients before CCTA.

Morphological Autoencoders for Beat-by-Beat Atrial Fibrillation Detection Using Single-Lead ECG

Engineered feature extraction can compromise the ability of Atrial Fibrillation (AFib) detection algorithms to deliver near real-time results. Autoencoders (AEs) can be used as an automatic feature extraction tool, tailoring the resulting features to a specific classification task. By coupling an encoder to a classifier, it is possible to reduce the dimension of the Electrocardiogram (ECG) heartbeat waveforms and classify them. In this work we show that morphological features extracted using a Sparse AE are sufficient to distinguish AFib from Normal Sinus Rhythm (NSR) beats. In addition to the morphological features, rhythm information was included in the model using a proposed short-term feature called Local Change of Successive Differences (LCSD). Using single-lead ECG recordings from two referenced public databases, and with features from the AE, the model was able to achieve an F1-score of 88.8%. These results show that morphological features appear to be a distinct and sufficient factor for detecting AFib in ECG recordings, especially when designed for patient-specific applications. This is an advantage over state-of-the-art algorithms that need longer acquisition times to extract engineered rhythm features, which also requires careful preprocessing steps. To the best of our knowledge, this is the first work that presents a near real-time morphological approach for AFib detection under naturalistic ECG acquisition with a mobile device.

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

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

Predicting physical fatigue in athletes in rope skipping training using ECG signals

Physical fatigue is a crucial factor that leads to a decrease in performance, especially in athletes. This study proposes effective physiological indicators and methods to predict physical fatigue. We designed a ”training-cognitive task” experiment using specialized electrocardiogram (ECG) acquisition devices (Shimmer3) to collect ECG data during the training process. The participants were required to complete a 2-back cognitive task before and after one-hour of rope skipping training. After the experiment, we analyzed the heart rate variability (HRV) signals of the subjects using the time domain, frequency domain, and nonlinear dynamics. Pearson’s correlation coefficient analysis and paired t-tests were applied to the measured indicators. Subsequently, selected ECG indicators with statistical significance, the particle swarm optimization-support vector regression (PSO-SVR), and extreme learning machine (ELM) were used to build regression models that predicted subjective and objective physical fatigue. Moreover, we have compared the performance of Gaussian process regression (GPR), random forest (RF), k-nearest neighbors (KNN), and adaptive boosting (AdaBoost) based machine learning model for prediction. The results indicated that PSO-SVR had a better performance in predicting subjective physical fatigue, with mean absolute percentage error (MAPE) of 11.53%, and ELM achieved competitive outcomes for objective physical fatigue prediction with MAPE of 19.77%. This study provides a reliable experimental basis for identifying effective physical fatigue.

A 43.3-μW Biopotential Amplifier With Tolerance to Common-Mode Interference of 18 Vpp and T-CMRR of 105 dB in 180-nm CMOS

In this article, we present an electrocardiogram (ECG) amplifier that has a large total CMRR (TCMRR) regardless of contact impedance mismatch and a large tolerance to common-mode interference (CMI). To achieve these features, an adaptive TCMRR enhancing loop is implemented in parallel with a charge-pump-based common-mode suppressing loop (CMSL). It adjusts the CMI current through each contact impedance so that common-mode (CM) to differential-mode (DM) conversion is minimized. We also propose a fast settling technique for the adaptive loop so that contact impedance variation can be tracked fast enough to allow robust ECG acquisition. A prototype chip fabricated in 180-nm CMOS achieves TCMRR larger than 105 dB even when there is contact impedance mismatch of up to 30%. It also achieves tolerance to CMI of 18 VPP at 60 Hz and input-referred noise of 1.90 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mu }\rm V_{rms}$ </tex-math></inline-formula> while consuming 43.3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> .

Characterization and Validation of Flexible Dry Electrodes for Wearable Integration.

When long-term biosignal monitoring is required via surface electrodes, the use of conventional silver/silver chloride (Ag/AgCl) gelled electrodes may not be the best solution, as the gel in the electrodes tends to dry out over time. In this work, the electrical behaviour and performance of dry electrodes for biopotential monitoring was assessed. Three materials were investigated and compared against the gold-standard Ag/AgCl gelled electrodes. To characterize their electrical behaviour, the impedance response over the frequency was evaluated, as well as its signal to noise ratio. The electrodes' performance was evaluated by integrating them in a proven electrocardiogram (ECG) acquisition setup where an ECG signal was acquired simultaneously with a set of dry electrodes and a set of standard Ag/AgCl gelled electrodes as reference. The obtained results were morphologically compared using the Normalised Root Mean Squared Error (nRMSE) and the Cosine Similarity (CS). The findings of this work suggest that the use of dry electrodes for biopotential monitoring is a suitable replacement for the conventional Ag/AgCl gelled electrodes. The signal obtained with dry electrodes is comparable to the one obtained with the gold standard, with the advantage that these do not require the use of gel and can be easily integrated into fabric to facilitate their use in long-term monitoring scenarios.

Influence of the Door-to-ECG Time on the Prognosis of Patients with Acute Coronary Syndrome.

The rapid acquisition of an electrocardiogram (ECG) plays a crucial role in the diagnosis and management decisions in patients with acute coronary syndrome (ACS). We determined the time-to-ECG acquisition, identified factors associated with timely acquisition, and evaluated the influence of time-to-ECG on in-hospital mortality. We measured the door-to-ECG time for 903 of 2140 patients in the emergency department of Far Eastern Memorial Hospital with a diagnosis of ACS from January 1, 2016 to December 31, 2018, via a retrospective chart review. The primary outcome was in-hospital mortality. Outcome analysis of mortality was conducted using multivariable logistic regression. The secondary outcome was to determine which factors influenced whether or not a patient received an ECG within 10 min. The analysis was conducted using multiple logistic regression. The median time-to-ECG was 5 min (interquartile range: 4-11 min) in all patients. In multivariable logistic regression analysis, we found that older age and more severe heart-broken index were significantly related to timely ECG acquisition. In-hospital mortality was higher in those in whom ECG was performed after more than 10 min. However, in the multivariable logistic regression analysis, it did not have a significant positive correlation with ECG acquisition time. Timely ECG acquisition owing to the triage protocol at our institution, the heart-broken index, led to early PCI and thus better outcomes for the ACS patients in this study. The implementation of a protocol-driven timely evaluation of patients with ACS and prompt PCI are important.

A comparison of a precordial belt ECG and a gel-adhesive electrode in terms of ECG acquisition time and usability in emergency department patients

An electrocardiogram (ECG) is a fast, valuable, and non-invasive test that has a wide range of uses in the diagnosis, examination, screening, and prognosis of many cardiac and non-cardiac morbidities. The study was designed as a prospective, single-blind randomized controlled, single center. The CONSORT (Consolidated Standards of Reporting Trials) statement is used. The patients were divided into two groups: Group Gel Adhesive Electrode (GAE) and Group precordial belt ECG (PBE), according to the ECG recording technique. The results between the two groups were compared. A total of 250 patients were included in the study. 127 patients were included in the Group GAE and 123 patients were included in the Group PBE. The mean ECG recording time of patients in the Group GAE was 86.83 ± 22.53 seconds, while it was 59.56 ± 15.08 seconds in the Group PBE. When the ECG recording time is evaluated, the mean of the patients in the Group PBE is shorter, and there is a statistically significant difference between the two groups (p &lt; 0.001). When evaluated in terms of the need for repeat ECG acquisitions of the ECG (Group PBE: 9.75%, Group GAE: 14.96%), there is no statistically significant difference between the groups (p = 0.251). Causes of a repeat ECG were chest hair (Group GAE: 3.15%, Group PBE: 6.50%; p = 0.215), perspiration (Group GAE: 8.66%, Group PBE: 3.15%; p = 0.108), shaking (Group GAE: 15%, Group PBE: 0%; p = 0.122). Compared to the GAE, the precordial ECG Belt may be more advantageous in terms of examination time.

An efficient ECG denoising method by fusing ECA-Net and CycleGAN.

For wearable electrocardiogram (ECG) acquisition, it was easy to infer motion artifices and other noises. In this paper, a novel end-to-end ECG denoising method was proposed, which was implemented by fusing the Efficient Channel Attention (ECA-Net) and the cycle consistent generative adversarial network (CycleGAN) method. The proposed denoising model was optimized by using the ECA-Net method to highlight the key features and introducing a new loss function to further extract the global and local ECG features. The original ECG signal came from the MIT-BIH Arrhythmia Database. Additionally, the noise signals used in this method consist of a combination of Gaussian white noise and noises sourced from the MIT-BIH Noise Stress Test Database, including EM (Electrode Motion Artifact), BW (Baseline Wander) and MA (Muscle Artifact), as well as mixed noises composed of EM+BW, EM+MA, BW+MA and EM+BW+MA. Moreover, corrupted ECG signals were generated by adding different levels of single and mixed noises to clean ECG signals. The experimental results show that the proposed method has better denoising performance and generalization ability with higher signal-to-noise ratio improvement (SNRimp), as well as lower root-mean-square error (RMSE) and percentage-root-mean-square difference (PRD).

Paramedic perspectives on sex and gender equity in prehospital electrocardiogram acquisition

Acute coronary syndrome (ACS) makes up a substantial healthcare burden. Previous research has demonstrated that women fare worse than men across a range of hospital-based ACS processes and outcomes. In the prehospital setting, the 12-lead electrocardiogram (ECG) can identify critical ACS cases and speed access to definitive treatment. However, studies on rates of ECG acquisition among patients at risk of ACS in the prehospital setting have shown that women receive the test less frequently than men. The purpose of this study was to investigate the reasons for observed gender differences in rates of ECG acquisition in the prehospital setting. This is a cross-sectional study of paramedics in an urban emergency medical service agency. An experienced qualitative researcher conducted 26 semi-structured interviews. Using a subjectivist inductive approach, the study team analyzed interview transcripts following the six-step process described by Braun and Clarke (2006). Qualitative rigor was maintained through consistent review, discussion, and revision at each stage. Four themes were identified among diverse views around the 12-lead acquisition. (1) Safety (feelings of physical comfort and psychological safety related to administering a 12-lead). (2) Knowledge (subject-matter knowledge involving the protocols and procedures as well as self-knowledge of participants’ own practice). (3) Training and learning (perceptions of foundational training and on-the-job learning, and how those have influenced their perceptions of how to care for patients experiencing ACS). (4) Professionalism (perceptions of behavior and attitudes and how they influence patient care). These four interrelated themes were integrated to represent a model of prehospital cardiac care. This study addressed a gap in the literature by documenting prehospital providers’ attitudes and beliefs around cardiac care. The resulting model of prehospital cardiac care offers both a template for evaluating the equity of current treatment and a road map for improvement.

A 672-nW, 670-nVrms ECG Acquisition AFE With Noise-Tolerant Heartbeat Detector

This paper presents an electrocardiogram acquisition analog front-end (AFE) with a noise tolerant heartbeat (HB) detector. Source degradation and transconductance bootstrap techniques are incorporated into the AFE to reduce the 1/f noise of the amplifier. Furthermore, the chopper modulation, DC-servo loop (DSL) and pre-charge technology are combined to reduce interference from the environment. A mixed-signal implementation of HB detector with the symmetric-comparison loop is proposed to reduce the power consumption and area, which also suppresses motion artifact interference by adaptive thresholds. Implemented in 0.18 μm CMOS process, the circuit only occupies an area of 0.122 mm2 and consumes 0.62 μW at a 1.2-V supply, of which AFE and HB detector consume 507 nW and 110 nW, respectively. Simulation results show that the gain and the CMRR of AFE range from 30-45 dB and 65-105 dB, respectively. The input-referred noise is 670 nVrms with a mid-band gain of 42 dB and a bandwidth ranging from 0.5 Hz to 1 kHz.

Multi-category classification of left ventricle ejection fraction using a convolutional neural network

Abstract Background Screening for left ventricular (LV) systolic dysfunction (defined as ejection fraction ≤35%) based on data from a standard 12-lead electrocardiogram (ECG) has become well established when standard digital ECGs are available–8 independent leads sampled at least 250 hertz for 10 seconds. As the algorithm has been incorporated into various clinical scenarios and ancillary research projects, a limitation of the binary classification at 35% has become apparent. Purpose The objective of this study was to develop and validate a deep learning-based algorithm that would classify LVEF into three categories based on only the digital ECG input. Methods After IRB approval, native digital resting ECGs acquired between 1/1/2010 and 12/31/2021 on patients seen in Mayo Clinic in Jacksonville were extracted from the institutional electronic ECG database management system (MUSE, GE Healthcare). These ECGs were matched with transthoracic echocardiograms obtained up to four days prior or 30 days after the ECGs acquisition. A convolutional neural network consisting of 8 layers of convolutions, batch normalization and pooling was trained using Keras and Tensorflow with hyper-parameter optimization for L1 and L2 regularization, learning rate adjustments, and class weights to predict three classes of LVEF: ≤35%, 36–51%, and ≥52% based on clinical relevance. The primary measure of overall performance was the detection of LVEF ≤35%; however, the triad of model predictions was also considered in translating the model output to human interpretable findings. Results A total of 30,153 patients (60,169 ECG pairings; mean age 63 years; 48% male) were randomly split at the patient level into training (24,172 patients), validation (2,973 patients) and testing (3,008 patients). The trained model provided robust discrimination in the withheld testing data – AUROC of 0.941 (95% CI: 0.931 to 0.950). Using the optimal model threshold based on Youden's index from the validation data (0.186), sensitivity and specificity were estimated to be 87.9% (95% CI: 83.8% to 91.2%) and 86.3% (95% CI: 85.4% to 87.2%) in the testing data. In instances where discordant predictions were observed, the posterior distribution of model probabilities provide additional insights into the possible underlying value of LVEF (Figure 1). Conclusions The utilization of a multi-category deep learning classification model for the detection of reduced ejection fraction adds new dimensions to the use of AI technologies on digital ECGs. This work shows high discrimination can still be obtained when using three classes of LVEF. Funding Acknowledgement Type of funding sources: None.