Electrocardiographic Device Research Articles

Radar-based contactless heart beat detection with a modified Pan–Tompkins algorithm

Background.Using radar for non-contact measuring human vital signs has garnered significant attention due to its undeniable benefits. However, achieving reasonably good accuracy in contactless measurement senarios is still a technical challenge.Materials and methods.The proposed method includes two stages. The first stage involves the process of datasegmentation and signal channel selection. In the next phase, the raw radar signal from the chosen channel is subjected to modified Pan-Tompkins.Results.The experimental findings from twelve individuals demonstrated a strong agreement between the contactless radar and contact electrocardiography (ECG) devices for heart rate measurement, with correlation coefficient of 98.74 percentage; and the 95% limits of agreement obtained by radar and those obtained by ECG were 2.4 beats per minute.Conclusion.The results showed high agreement between heart rate calculated by radar signals and heart rate by electrocardiograph. This research paves the way for future applications using non-contact sensors to support and potentially replace contact sensors in healthcare.

Assessing Africa’s position in the development of AI-enabled ECG devices

Background The integration of Artificial Intelligence (AI) in electrocardiographic (ECG) devices has become a pivotal area of research, particularly during the COVID-19 pandemic. These technologies are essential for enhancing cardiac diagnosis and monitoring. Methods This study assesses current trends, key contributors, and collaborative networks in the field of AI-enhanced ECG devices. We utilized a comprehensive analysis, using the Biblioshiny library from Bibliometrix for data exploration of data extracted from the Scopus database and VOSViewer for creating and visualizing maps. These tools were played an important role in conducting an in-depth analysis of the relationships and developments within the field. Results The analysis shows a significant increase in publications related to AI-enhanced ECG devices, with a marked surge during the COVID-19 pandemic. Despite the growing interest and technological advancements, the study exposes a notable disparity in the geographical distribution of research contributions, highlighting substantial under-representation of African researchers. This gap is attributed to infrastructural, financial constraints, and limited collaborative networks within the continent. Conclusion The rapid evolution and increasing importance of AI in ECG devices underscore the need for more inclusive research practices. There is a critical need to integrate and promote contributions from under-represented regions, particularly Africa, to ensure a globally diverse perspective in tackling health challenges. This study calls for enhanced participation and support for African researchers to bridge the existing research gap and foster global health equity.

Evaluation of Frontal QRS-T Angle in Children With ADHD and Healthy Controls.

Conflicting findings exist regarding the link between attention deficit hyperactivity disorder (ADHD) and cardiovascular diseases. This study aimed to evaluate the frontal QRS-T (fQRS-T) angle and its correlation with symptom severity in children diagnosed with ADHD. The study population consisted of 172 patients diagnosed with ADHD (120 drug naive and 52 drug positive) and 82 healthy controls. ADHD symptoms were assessed using the Atilla Turgay DSM-IV-Based Screening and Assessment Scale for Disruptive Conduct Disorders (T-DSM-IV-Scale). The fQRS-T angle and corrected QT (QTc) interval were obtained from the automated reports of 12-lead electrocardiography device for each patient. QTc interval and fQRS-T angle were significantly different among the groups. Post hoc analyses showed that QTc interval and fQRS-T angle of ADHD drug naive and ADHD drug positive patients were significantly higher than the healthy control groups. However, there was no significant difference between drug naive and drug positive patients regarding QTc interval and fQRS-T angle. Both QTc interval and fQRS-T angle showed positive correlations with the severity of ADHD symptoms (r = 0.263, p = .001 and r = 0.175, p = .023 respectively). We found that fQRS-T angle was significantly wider in children with ADHD. Therefore, we suggest that fQRS-T angle may help in cardiovascular risk assessment in children with ADHD.

Extended Ambulatory ECG Monitoring Enhances Identification of Higher-Risk Ventricular Tachyarrhythmias in Patients With Hypertrophic Cardiomyopathy

BackgroundIn hypertrophic cardiomyopathy (HCM), 48-hour ambulatory monitoring has been standard practice to detect nonsustained ventricular tachycardia (NSVT), a sudden death risk marker. Extended-wear ambulatory electrocardiographic (ECG) devices have more recently used for monitoring patients with HCM. ObjectiveWe aimed to evaluate NSVT burden identified with continuous ambulatory monitoring for up to 2 weeks compared with initial 48 hours. MethodsThere were 236 consecutive patients with HCM (49 ± 12 years) who underwent 14-day continuous ambulatory monitoring (Zio XT, iRhythm Technologies, San Francisco, CA); diagnostic yield of NSVT compared for initial 48 hours vs extended for 14 days. ResultsOf 236 patients, 114 (48%) had ≥ 1 runs of NSVT (median 2) over 14 days. Median length of NSVT was 7 beats (range: 3 to 67) at rates of 120 to 240 beats per minute (bpm) (median, 167 bpm). In 42 of the 114 patients (37%), initial NSVT occurred ≤ 48 hours and in 72 (63%) only during the extended monitoring period (3 to 14 days). Diagnostic yield for detecting NSVT over 14 days was 2.7-fold greater than ≤ 48 hours (P < .001). NSVT judged at higher risk (≥ 8 beats, > 200 bpm, ≥ 2 runs in consecutive 2-day period) was identified more frequently during extended monitoring, diagnostic yield 3-fold greater than ≤ 48 hours (P < .001). ConclusionIn HCM, NSVT episodes are frequent; however, in most patients, both NSVT and higher-risk NSVT were not detected during initial 48 hours and were confined solely to extended monitoring period. These data support additional clinical studies to evaluate the significance of NSVT on extended monitoring on sudden death risk in HCM.

UNet-BiLSTM: A Deep Learning Method for Reconstructing Electrocardiography from Photoplethysmography

Electrocardiography (ECG) is generally used in clinical practice for cardiovascular diagnosis and for monitoring cardiovascular status. It is considered to be the gold standard for diagnosing cardiovascular diseases and assessing cardiovascular status. However, it is not always easy to obtain. Unlike ECG devices, photoplethysmography (PPG) devices can be placed on body parts such as the earlobes, fingertips, and wrists, making them more comfortable and easier to obtain. Several methods for reconstructing ECG signals using PPG signals have been proposed, but some of these methods are subject-specific models. These models cannot be applied to multiple subjects and have limitations. This study proposes a neural network model based on UNet and bidirectional long short-term memory (BiLSTM) networks as a group model for reconstructing ECG from PPG. The model was verified using 125 records from the MIMIC III matched subset. The experimental results demonstrated that the proposed model was, on average, able to achieve a Pearson‘s correlation coefficient, root mean square error, percentage root mean square difference, and Fréchet distance of 0.861, 0.077, 5.302, and 0.278, respectively. This research can use the correlation between PPG and ECG to reconstruct a better ECG signal from PPG, which is crucial for diagnosing cardiovascular diseases.

Edge AI Model Deployed for Real-Time Detection of Atrial Fibrillation Risk during Sinus Rhythm.

Objectives: The study aimed to develop a deep learning-based edge AI model deployed on electrocardiograph (ECG) devices for the real-time detection of atrial fibrillation (AF) risk during sinus rhythm (SR) using standard 10 s, 12-lead electrocardiograms (ECGs). Methods: A novel approach was used to convert standard 12-lead ECGs into binary images for model input, and a lightweight convolutional neural network (CNN)-based model was trained using data collected by the Japan Agency for Medical and Research Development (AMED) between 2019 and 2022. Patients over 40 years old with digital, SR ECGs were retrospectively enrolled and divided into AF and non-AF groups. The data labeling was supervised by cardiologists. The dataset was randomly allocated into training, validation, and internal testing datasets. External testing was conducted on data collected from other hospitals. Results: The best-trained model achieved an AUC of 0.82 and 0.80, sensitivity of 79.5% and 72.3%, specificity of 77.8% and 77.7%, precision of 78.2% and 76.4%, and overall accuracy of 78.6% and 75.0% in the internal and external testing datasets, respectively. The deployed model and app package utilized 2.5 MB and 40 MB of the available ROM and RAM capacity on the edge ECG device, correspondingly. The processing time for AF risk detection was approximately 2 s. Conclusions: The model maintains comparable performance and improves its suitability for deployment on resource-constrained ECG devices, thereby expanding its potential impact to a wide range of healthcare settings. Its successful deployment enables real-time AF risk detection during SR, allowing for timely intervention to prevent AF-related serious consequences like stroke and premature death.

Real-world validation of smartphone-based photoplethysmography for rate and rhythm monitoring in atrial fibrillation.

Photoplethysmography- (PPG) based smartphone applications facilitate heart rate and rhythm monitoring in patients with paroxysmal and persistent atrial fibrillation (AF). Despite an endorsement from the European Heart Rhythm Association, validation studies in this setting are lacking. Therefore, we evaluated the accuracy of PPG-derived heart rate and rhythm classification in subjects with an established diagnosis of AF in unsupervised real-world conditions. Fifty consecutive patients were enrolled, 4 weeks before undergoing AF ablation. Patients used a handheld single-lead electrocardiography (ECG) device and a fingertip PPG smartphone application to record 3907 heart rhythm measurements twice daily during 8 weeks. The ECG was performed immediately before and after each PPG recording and was given a diagnosis by the majority of three blinded cardiologists. A consistent ECG diagnosis was exhibited along with PPG data of sufficient quality in 3407 measurements. A single measurement exhibited good quality more often with ECG (93.2%) compared to PPG (89.5%; P < 0.001). However, PPG signal quality improved to 96.6% with repeated measurements. Photoplethysmography-based detection of AF demonstrated excellent sensitivity [98.3%; confidence interval (CI): 96.7-99.9%], specificity (99.9%; CI: 99.8-100.0%), positive predictive value (99.6%; CI: 99.1-100.0%), and negative predictive value (99.6%; CI: 99.0-100.0%). Photoplethysmography underestimated the heart rate in AF with 6.6 b.p.m. (95% CI: 5.8 b.p.m. to 7.4 b.p.m.). Bland-Altman analysis revealed increased underestimation in high heart rates. The root mean square error was 11.8 b.p.m. Smartphone applications using PPG can be used to monitor patients with AF in unsupervised real-world conditions. The accuracy of AF detection algorithms in this setting is excellent, but PPG-derived heart rate may tend to underestimate higher heart rates.

Artificial intelligence-enhanced electrocardiography : Will it revolutionize diagnosis and management of our patients?

The use of artificial intelligence (AI) in healthcare has made significant progress in the last 10years. Many experts believe that utilization of AI technologies, especially deep learning, will bring about drastic changes in how physicians understand, diagnose, and treat diseases. One aspect of this development is AI-enhanced electrocardiography (ECG) analysis. It involves not only optimizing the traditional ECG analysis by the physician and improving the accuracy of automatic interpretation by the ECG device but also introducing entirely new diagnostic options enabled by AI. Examples include assessing left ventricular function, predicting atrial fibrillation, and diagnosing both cardiac and noncardiac conditions. Through AI, the ECG becomes acomprehensive tool for screening, diagnosis, and patient management, potentially revolutionizing clinical practices. This paper provides an overview of the current state of this development, discusses existing limitations, and explores the challenges that may arise for healthcare professionals in this context.

Evaluation and patient experience of wireless noninvasive fetal heart rate monitoring devices.

In clinical practice, fetal heart rate monitoring is performed intermittently using Doppler ultrasound, typically for 30 minutes. In case of a non-reassuring heart rate pattern, monitoring is usually prolonged. Noninvasive fetal electrocardiography may be more suitable for prolonged monitoring due to improved patient comfort and signal quality. This study evaluates the performance and patient experience of four noninvasive electrocardiography devices to assess candidate devices for prolonged noninvasive fetal heart rate monitoring. Non-critically sick women with a singleton pregnancy from 24 weeks of gestation were eligible for inclusion. Fetal heart rate monitoring was performed during standard care with a Doppler ultrasound device (Philips Avalon-FM30) alone or with this Doppler ultrasound device simultaneously with one of four noninvasive electrocardiography devices (Nemo Fetal Monitoring System, Philips Avalon-Beltless, Demcon Dipha-16 and Dräger Infinity-M300). Performance was evaluated by: success rate, positive percent agreement, bias, 95% limits of agreement, regression line, root mean square error and visual agreement using FIGO guidelines. Patient experience was captured using a self-made questionnaire. A total of 10 women were included per device. For fetal heart rate, Nemo performed best (success rate: 99.4%, positive percent agreement: 94.2%, root mean square error 5.1 BPM, bias: 0.5 BPM, 95% limits of agreement: -9.7 - 10.7 BPM, regression line: y = -0.1x + 11.1) and the cardiotocography tracings obtained simultaneously by Nemo and Avalon-FM30 received the same FIGO classification. Comparable results were found with the Avalon-Beltless from 36 weeks of gestation, whereas the Dipha-16 and Infinity-M300 performed significantly worse. The Avalon-Beltless, Nemo and Infinity-M300 closely matched the performance of the Avalon-FM30 for maternal heart rate, whereas the performance of the Dipha-16 deviated more. Patient experience scores were higher for the noninvasive electrocardiography devices. Both Nemo and Avalon-Beltless are suitable devices for (prolonged) noninvasive fetal heart rate monitoring, taking their intended use into account. But outside its intended use limit of 36 weeks' gestation, the Avalon-Beltless performs less well, comparable to the Dipha-16 and Infinity-M300, making them currently unsuitable for (prolonged) noninvasive fetal heart rate monitoring. Noninvasive electrocardiography devices appear to be preferred due to greater comfort and mobility.

Atrial Fibrillation Detection from Compressed ECG Measurements for Wireless Body Sensor Network

Recent years have witnessed an increasing prevalence of wearable devices in the public, where atrial fibrillation (AF) detection is a popular application in these devices. Generally, AF detection is performed on cloud whereas this paper describes an on-device AF detection method. Technically, compressed sensing (CS) is first used for electrocardiograph (ECG) acquisition. Then QRS detection is proposed to be performed directly on the compressed CS measurements, rather than on the reconstructed signals on the powerful cloud server. Based on the extracted QRS information, AF is determined by quantitatively analyzing the ( RR , dRR ) plot. Databases with ECG samples collected from both medical-level (MIT-BIH afdb) and wearable ECG devices (Physionet Challenge 2017) are introduced for performance validation. The experiment results well demonstrate that our on-device AF detection algorithm can approach the performance of those implemented on the raw signals. Our proposal is suitable for AF screening directly on the wearable devices, without the support of the data center for signal reconstruction and intelligent analysis.

HRV and EEG correlates of well-being using ultra-short, portable, and low-cost measurements.

Wearable electroencephalography (EEG) and electrocardiography (ECG) devices may offer a non-invasive, user-friendly, and cost-effective approach for assessing well-being (WB) in real-world settings. However, challenges remain in dealing with signal artifacts (such as environmental noise and movements) and identifying robust biomarkers. We evaluated the feasibility of using portable hardware to identify potential EEG and heart-rate variability (HRV) correlates of WB. We collected simultaneous ultrashort (2-min) EEG and ECG data from 60 individuals in real-world settings using a wrist ECG electrode connected to a 4-channel wearable EEG headset. These data were processed, assessed for signal quality, and analyzed using the open-source EEGLAB BrainBeats plugin to extract several theory-driven metrics as potential correlates of WB. Namely, the individual alpha frequency (IAF), frontal and posterior alpha asymmetry, and signal entropy for EEG. SDNN, the low/high frequency (LF/HF) ratio, the Poincaré SD1/SD2 ratio, and signal entropy for HRV. We assessed potential associations between these features and the main WB dimensions (hedonic, eudaimonic, global, physical, and social) implementing a pairwise correlation approach, robust Spearman's correlations, and corrections for multiple comparisons. Only eight files showed poor signal quality and were excluded from the analysis. Eudaimonic (psychological) WB was positively correlated with SDNN and the LF/HF ratio. EEG posterior alpha asymmetry was positively correlated with Physical WB (i.e., sleep and pain levels). No relationships were found with the other metrics, or between EEG and HRV metrics. These physiological metrics enable a quick, objective assessment of well-being in real-world settings using scalable, user-friendly tools.

An Energy-Efficient ECG Processor Based on HDWT and a Hybrid Classifier for Arrhythmia Detection

Cardiac arrhythmia (CA) is a severe cardiac disorder that results in a significant number of fatalities worldwide each year. Conventional electrocardiography (ECG) devices are often unable to detect arrhythmia symptoms during patients’ hospital visits due to their intermittent nature. This paper presents a wearable ECG processor for cardiac arrhythmia (CA) detection. The processor utilizes a Hilbert transform-based R-peak detection engine for R-peak detection, a Haar discrete wavelet transform (HDWT) unit for feature extraction, and a Hybrid ECG classifier that combines linear methods and Non-Linear Support Vector Machines (NLSVM) classifiers to distinguish between normal and abnormal heartbeats. The processor is fabricated by the CMOS 110 nm process with an area of 1.34 mm2 and validated with the MIT_BIH Database. The whole design consumes 4.08 μW with an average classification accuracy of 97.34%.

Precordial electrocardiographic recording and QT measurement from a novel wearable ring device

BackgroundThe availability of portable and wearable electrocardiography devices has increased secondary to technological development. Single-lead ECG recordings have been shown to reliably detect and characterize cardiac rhythms such as atrial fibrillation. Acquisition of precordial electrodes for full 12-lead ECG reconstruction from bipolar recordings is complicated by the absence of a body ground / Wilson central terminal electrode, however the extent of difference between standard precordial lead and those from a wearable bipolar ECG recorder has not been characterized. ObjectiveThis study characterizes precordial ECG lead set from sequential bipolar recordings from an ECG ring wearable device. MethodsIn 70 patients wearing an ECG device on their right finger, sequential precordial leads (CR1-CR6) were obtained along with chest electrodes (V1-V6). During acquisition of the modified precordial lead CR6 a full standardized 12 lead ECG capture was obtained.Using automated analysis software, signal quality was assessed, and the correlation values between the ring-derived ECG precordial leads and standard ECG leads were compared with QRS duration, QT width, and RR interval measurement. ResultsThere is high concordance in the morphology of precordial ECG leads obtained in a standard fashion and those recorded through an ECG ring. Morphologic alignment improved with increasing laterality of the precordial lead with chest to right arm ring recording (CR5, CR6) compared with anterior chest leads to right arm (CR1, CR2). Segmental measurements of QRS duration and QT segment were well aligned and of high correlation. ConclusionWearable ring based electrocardiographic technology is capable of high fidelity recording of precordial leads for non-simultaneous reconstruction of complete ECG sets. These correlate highly with surface-obtained QRS and QT duration measurement, which has significant implications for clinical applications. Uninterpretable tracings were primarily due to electrode noise from poor electrode contact.

A Scoping Review of Varying Mobile Electrocardiographic Devices.

The electrocardiogram (ECG) can now be measured using mobile devices. Mobile ECG devices, which are defined as devices capable of recording and transmitting non-standard ECGs, offer numerous advantages such as cost-effectiveness and being user-friendly. Mobile ECG can also extend recording lengths (e.g., 2days, 14days), which is necessary to capture important intermittent events (e.g., cardiac arrhythmias) and evaluate prognostic risk markers (e.g., prolonged corrected QT (QTc) interval). Some mobile ECG devices can even connect to broadband networks allowing patients to remotely transmit their ECG to a clinician. This article systematically examines different mobile ECG devices used in prior studies and provides a detailed assessment of five diverse yet commonly used mobile ECG devices: AliveCor KardiaMobile; AliveCor KardiaMobile 6L; iRhythm ZioPatch; Apple Smartwatch ECG; and CardioSecur System. These mobile ECG devices are diverse in the number of leads measured and the duration of monitoring. Similar to their diversity, there has been a wide range of clinical applications of mobile ECG devices. Despite significant progress, questions regarding data quality, and clinican and patient acceptance and compliance persist.

Diagnostic Accuracy of a Portable Electromyography and Electrocardiography Device to Measure Sleep Bruxism in a Sleep Apnea Population: A Comparative Study.

The gold standard for diagnosing sleep bruxism (SB) and obstructive sleep apnea (OSA) is polysomnography (PSG). However, a final hypermotor muscle activity often occurs after apnea episodes, which can confuse the diagnosis of SB when using portable electromyography (EMG) devices. This study aimed to compare the number of SB episodes obtained from PSG with manual analysis by a sleep expert, and from a manual and automatic analysis of an EMG and electrocardiography (EKG) device, in a population with suspected OSA. Twenty-two subjects underwent a polysomnographic study with simultaneous recording with the EMG-EKG device. SB episodes and SB index measured with both tools and analyzed manually and automatically were compared. Masticatory muscle activity was scored according to published criteria. Patients were segmented by severity of OSA (mild, moderate, severe) following the American Academy of Sleep Medicine (AASM) criteria. ANOVA and the Bland-Altman plot were used to quantify the agreement between both methods. The concordance was calculated through the intraclass correlation coefficient (ICC). On average, the total events of SB per night in the PSG study were (8.41 ± 0.85), lower than the one obtained with EMG-EKG manual (14.64 ± 0.76) and automatic (22.68 ± 16.02) analysis. The mean number of SB episodes decreases from the non-OSA group to the OSA group with both PSG (5.93 ± 8.64) and EMG-EKG analyses (automatic = 22.47 ± 18.07, manual = 13.93 ± 11.08). However, this decrease was minor in proportion compared to the automatic EMG-EKG analysis mode (from 23.14 to 22.47). The ICC based on the number of SB episodes in the segmented sample by severity degree of OSA along the three tools shows a moderate correlation in the non-OSA (0.61) and mild OSA (0.53) groups. However, it is poorly correlated in the moderate (0.24) and severe (0.23) OSA groups: the EMG-EKG automatic analysis measures 14.27 units more than PSG. The results of the manual EMG-EKG analysis improved this correlation but are not good enough. The results obtained in the PSG manual analysis and those obtained by the EMG-EKG device with automatic and manual analysis for the diagnosis of SB are acceptable but only in patients without OSA or with mild OSA. In patients with moderate or severe OSA, SB diagnosis with portable electromyography devices can be confused due to apneas, and further study is needed to investigate this.

Diagnostic accuracy of handheld single-lead electrocardiographic devices to detect atrial fibrillation: an examination using more than 30,000 rhythm traces in a randomised control trial

Abstract Background Early atrial fibrillation (AF) diagnosis offers an opportunity to initiate management to reduce stroke risk. Hand-held single-lead electrocardiograph (ECG) devices are widely available, yet there is little data on the implementation of AF screening using the devices in high-AF-risk communities and examining the accuracy of the devices over time. Such data may provide insights into the possibility of using artificial intelligence to scale up screening. Purpose To examine the accuracy of AliveCor ECG devices’ automated interpretation against clinicians’ diagnoses and explore the characteristics of the ECGs of older community-dwelling people in a real-world setting. Methods This is an analysis of ECGs received from people aged ≥75 years participating in an implementation trial of a community AF screening program over 12 months. Clinicians reviewed all ECGs transmitted to a central portal. Clinicians’ interpretations were taken as diagnostically accurate. Participants and their general practitioners (GPs) were notified of AF. The devices’ automatic ECG interpretations, including the participants’ pulse rates automatically recorded by the devices, were extracted from the central portal and analysed using R v3.6.0. Results 200 participants were enrolled. From May 2021 to Feb 2023, 30,040 ECGs were received; of these traces, clinicians confirmed AF in 479 traces and no AF in 29,561 traces. The devices’ automatic algorithm had a sensitivity of 91.4 % (i.e., detected AF in 438 of these 479 traces) and specificity of 96.6% (no AF: 28,546 / 29,561 traces). In total, the devices’ automated interpretation identified 1,453 ECGs with "Possible AF", of which clinicians confirmed 911 (62.7%) were in sinus rhythm and 438 (30.1%) had AF (Table 1). Breaking down the statistics for various rhythms and comparing the devices’ automatic algorithms with the clinicians’ diagnoses, discrepancies were found in 5296 ECG traces (Kappa = 0.33). Of these, 4277 (80.8%) were interpreted by the device automated algorithm as "Unreadable" (interferences), "Unclassified" (the device was unable to determine a result), and "Too short" (&amp;lt; 30 seconds of recording). Excluding the 3799 "Unclassified’ traces, the level of agreement between the ECG devices and clinicians improved (Kappa 0.63). The device automatically reported "Unclassified" and "Possible AF" rhythm traces had wide dispersion of the participants’ pulse rates (Figure 1). Conclusions The ECG devices had high sensitivity to detect AF but a low level of agreement in differentiating various rhythms. The characteristics of the longitudinal ECG rhythm traces, including the participants’ pulse rates over time coupled with the clinicians’ diagnoses of this large dataset, could be used to improve the devices’ automated algorithm and inform artificial intelligence strategies for large-scale community AF screening programs.Table 1Figure 1

Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices.

Heart diseases rank among the most fatal health concerns globally, with the majority being preventable through early diagnosis and effective treatment. Electrocardiogram (ECG) analysis is critical in detecting heart diseases, as it captures the heart's electrical activities. For continuous monitoring, wearable electrocardiographic devices must ensure user comfort over extended periods, typically 24 to 48 h. These devices demand specialized algorithms with low computational complexity to accommodate memory and power consumption constraints. One of the most crucial aspects of ECG signals is accurately detecting heartbeat intervals, specifically the R peaks. In this study, we introduce a novel algorithm designed for wearable devices, offering two primary attributes: robustness against noise and low computational complexity. Our algorithm entails fitting a least-squares parabola to the ECG signal and adaptively shaping it as it sweeps through the signal. Notably, our proposed algorithm eliminates the need for band-pass filters, which can inadvertently smooth the R peaks, making them more challenging to identify. We compared the algorithm's performance using two extensive databases: the meta-database QT database and the BIH-MIT database. Importantly, our method does not necessitate the precise localization of the ECG signal's isoelectric line, contributing to its low computational complexity. In the analysis of the QT database, our algorithm demonstrated a substantial advantage over the classical Pan-Tompkins algorithm and maintained competitiveness with state-of-the-art approaches. In the case of the BIH-MIT database, the performance results were more conservative; they continued to underscore the real-world utility of our algorithm in clinical contexts.

Effects of Audio-Visual Environmental Factors on Emotion Perception of Campus Walking Spaces in Northeastern China

In the context of urban sustainable development and the creation of pedestrian-friendly campus environments, optimizing campus walking spaces has emerged as a central focus in urban planning research. Presently, research in pedestrian environments predominantly adopts a macroscopic perspective, offering limited insights into pedestrians’ subjective experiences and emotional perceptions at a micro level. Therefore, this study conducted on-site experiments in 21 walking spaces across three campuses, utilizing image semantic analysis, multifunctional sound level meter, wearable electrocardiography devices, and the Profile of Mood States (POMS) to collect data separately on audio-visual environmental factors and pedestrians’ emotional states. This study’s findings revealed significant correlations (p &lt; 0.01) among factors such as the Green Visual Index, Spatial Enclosure Index, Sky Visibility Index, Spatial feasibility Index, and Equivalent Continuous A-weighted Sound Pressure Level with physiological and psychological alterations in pedestrians’ emotions. Additionally, the various proportions of audio-visual environmental factors also exerted significant influences on emotions (p &lt; 0.05). The relevant conclusions can provide a reference for optimizing the audio-visual environment of walking space and promoting the sustainable development of the campus. In future research, the effects of audio-visual environments on both emotional and physiological indicators, as well as subjective evaluations, can be explored further.

Performance of Single-Lead Handheld Electrocardiograms for Atrial Fibrillation Screening in Primary Care: The VITAL-AF Trial

BackgroundHandheld single-lead electrocardiographic (1L ECG) devices are increasingly used for atrial fibrillation (AF) screening, but their real-world performance is not well understood. ObjectivesThe purpose of this study was to quantify the diagnostic test characteristics of 1L ECG automated interpretations for prospective AF screening. MethodsWe calculated the diagnostic test characteristics of the AliveCor KardiaMobile 1L ECG (AliveCor, US) algorithm using unblinded cardiologist overread as the gold standard using single 30s tracings administered by medical assistants among individuals aged ≥65 years participating in the VITAL-AF trial (NCT03515057) of population-based AF screening embedded within routine primary care. ResultsA total of 14,230 individuals (mean age 74 ± 7 years, 60% women, 82% White) had 31,376 tracings reviewed by 13 cardiologists. A total of 24,906 (79.6%) tracings had an AliveCor interpretation of normal, 5,046 (16.1%) were unclassified, 797 (2.5%) were possible AF, and 573 (1.8%) were no analysis. Cardiologists read 808 (2.6%) tracings as AF. AliveCor possible AF had a PPV of 51.7% (95% CI: 47.8%-55.6%). AliveCor normal had an NPV of 99.8% (95% CI: 99.7%-99.8%). The AliveCor algorithm had an overall sensitivity of 51.0% (95% CI: 47.1%-54.9%) and a specificity of 98.7% (95% CI: 98.6%-98.9%). AliveCor tracings interpreted as unclassified (PPV 5.9%, 95% CI: 5.1%-6.7%) and no analysis (PPV 6.5%, 95% CI: 4.6%-8.9%) had low predictive values for AF and were increasingly prevalent at older ages (13.7% for age 65-69 years to 28.1% for age ≥85 years, P < 0.01). ConclusionsIn an older primary care population undergoing AF screening with handheld 1L ECGs, automated algorithm interpretations were sufficiently accurate to exclude the presence of AF but not to establish an AF diagnosis.

Mobile electrocardiographic devices and healthcare utilization in post-atrial fibrillation ablation patients.

The use of mobile electrocardiogram (mECG) devices is becoming more prevalent. mECG devices allow instant access to recording arrhythmias and enable automatic rhythm interpretation. Providers can remotely evaluate patients and this may reduce in-person healthcare utilization. We sought to evaluate the utility of mECG devices in reducing healthcare utilization among patients who underwent atrial fibrillation (AF) ablation. We identified a population of patients with paroxysmal or persistent AF presenting for their first AF ablation. Patients were divided into two groups: KardiaMobile (AliveCor, Mountain View, CA) mECG users and non-KardiaMobile users. Healthcare utilization was compared between the two groups for one year post-ablation. 184 patients were studied (76 KardiaMobile users, 108 non-KardiaMobile users). There was no difference in the number of office visits (p=0.59), cardiac-specific emergency department visits (p=0.26), cardiac-specific hospital admissions (p=0.13), ablations or cardioversions completed (p=0.24), telephone encounters (p=0.05), patient electronic health record messages (p=0.40), or cardiac imaging (transthoracic or transesophageal echocardiograms) tests ordered (p=0.36). Exposure to the device was associated with a reduction in ambulatory cardiac monitor use (p=0.04). There was no difference in sinus rhythm maintenance over 12months by Kaplan-Meier survival analysis (log rank test p=0.05) between groups. Mobile technology is available for heart rhythm monitoring and can give instant feedback to the user. mECG use is associated with a significant reduction in ambulatory cardiac monitor use in the post-ablation period. There was no difference in other AF-related healthcare utilization.