Electrocardiogram Measurements Research Articles

The effect of enriched versus depleted housing on eucalyptus smoke-induced cardiovascular dysfunction in mice

Objectives: Living conditions play a major role in health and well-being, particularly for the cardiovascular and pulmonary systems. Depleted housing contributes to impairment and development of disease, but how it impacts body resiliency during exposure to environmental stressors is unknown. This study examined the effect of depleted (DH) versus enriched housing (EH) on cardiopulmonary function and subsequent responses to wildfire smoke. Materials and Methods: Two cohorts of healthy female mice, one of them surgically implanted with radiotelemeters for the measurement of electrocardiogram, body temperature (Tco) and activity, were housed in either DH or EH for 7 weeks. Telemetered mice were exposed for 1 h to filtered air (FA) and then flaming eucalyptus wildfire smoke (WS) while untelemetered mice, which were used for ventilatory assessment and tissue collection, were exposed to either FA or WS. Animals were continuously monitored for 5–7 days after exposure. Results: EH prevented a decrease in Tco after radiotelemetry surgery. EH mice also had significantly higher activity levels and lower heart rate during and after FA and WS. Moreover, EH caused a decreased number of cardiac arrhythmias during WS. WS caused ventilatory depression in DH mice but not EH mice. Housing enrichment also upregulated the expression of cardioprotective genes in the heart. Conclusions: The results of this study indicate that housing conditions impact overall health and cardiopulmonary function. More importantly, depleted housing appears to worsen the response to air pollution. Thus, non-chemical factors should be considered when assessing the susceptibility of populations, especially when it comes to extreme environmental events.

The association between perceived social functioning and heart rate variability is mediated by subclinical depressive symptomatology and moderated by gender.

Chronic loneliness and low perceived social support have been recognized as risk factors for both mental and cardiovascular disorders. It has been proposed that their link to psychophysiological problems may involve changes in parasympathetic activity. However, the exact underlying psychopathological mechanisms and the moderating effects of gender are still not thoroughly examined. Thus, the present study investigated associations between perceived social functioning and resting vagal tone in the context of potential cognitive and subclinical mediators and gender differences. Three hundred twenty-five young adults (aged 18-35, 180 women) underwent an electrocardiogram measurement of 6-minute resting heart rate variability (HRV). They also completed questionnaires assessing loneliness, perceived social support, social cognitive biases, depressive and social anxiety symptoms, and general mental health. In men, HRV was significantly and negatively associated with poorer perceived social functioning, depressive symptoms, and self-reported social cognitive biases, while in women, there was a quadratic link between HRV and depressive symptoms and HRV and general mental health. Moderated mediation analysis revealed that depressive symptoms fully mediated the relationship between perceived social functioning and HRV in men. The results suggest that decreased resting vagal tone in lonely individuals is linked to depressive symptomatology rather than to specific social cognitive biases and that this association is significant only in men.

Real-world validation of smartphone-based photoplethysmography for heart rate monitoring in atrial fibrillation

Abstract Introduction Photoplethysmography (PPG) based smartphone applications facilitate heart rate monitoring that may enable physicians to guide rate control in patients with atrial fibrillation (AF). Despite extensive validation of PPG-derived heart rate, validation studies in the presence of AF are lacking, particularly in unsupervised free-living conditions. Purpose To evaluate the accuracy of PPG-derived heart rate using a smartphone application in patients with an established diagnosis of AF, both during sinus rhythm and AF, in a real-world setting. Methods Consecutive patients undergoing AF ablation were enrolled four weeks prior to the procedure. They were instructed to assess their heart rate twice daily during eight weeks with a one-minute finger-over-the-camera smartphone-PPG recording. The reference heart rate was obtained from two 30-seconds handheld single-lead electrocardiogram (ECG) recordings performed immediately before and after the PPG recording. Measurements were valid if the heart rate did not change by more than 10% between the first and second ECG and maintained sinus rhythm or AF consistently. Results In total, fifty patients (age 63 ±11 years, 68% male) performed 3407 high-quality PPG measurements accompanied with two high-quality ECGs measurements both in sinus rhythm or AF. After excluding 223 measurements due to inconsistent heart rate between the first and second ECG, 3184 measurements remained. The mean heart rate (71.5 ± 17.3 beats per minute (bpm)) was significantly higher during AF (94.7 ± 19.4 bpm) compared to sinus rhythm (65.7 ± 10.7 bpm) (p-value < 0.001). The heart rate assessment was accurate within a 10% margin in 93.8% of PPG measurements. Inaccurate heart rate assessments were more frequent in AF (161/637; 25.3%) compared to sinus rhythm (35/2547; 1.4%) (p-value < 0.001). PPG underestimated the heart rate in sinus rhythm with 0.4 bpm (95% CI: -0.3 to -0.5 bpm) and in AF with 6.6 bpm (95% CI: -5.8 to -7.4 bpm). The deviation of PPG-derived heart rate from the ECG-derived true heart rate was significantly greater during AF (root mean square error (RMSE) = 11.8 bpm) compared to sinus rhythm (RMSE = 2.2 bpm) (p-value < 0.001). The heart rate correlation plots and corresponding Bland-Altman plots demonstrate a skewed underestimation of the heart rate in AF with PPG, particularly for high heart rates. Conclusions Heart rate can accurately be derived from PPG recordings using a smartphone application in real-world conditions. However, PPG measurements may underestimate high heart rates in the presence of AF.Heart Rate in Sinus RhythmHeart Rate in Atrial Fibrillation

#886 Prevalence and prognostic relevance of electrocardiographic abnormalities among patients with ANCA-associated vasculitis

Abstract Background and Aims Cardiovascular disease (CVD) is one of the leading causes of death in patients with ANCA-associated vasculitis (AAV). However, in current clinical guidelines, limited evidence is available to support the recommendation of AAV-specific screening for cardiovascular comorbidities. This study reported the prevalence of electrocardiogram (ECG) abnormalities and investigated associations between ECG abnormalities and fatal CVD in a validated cohort of patients with AAV compared to matched controls. Method Based on a retrospective matched cohort design, patients with granulomatosis with polyangiitis [ICD-10: DM31.3] or microscopic polyangiitis [ICD-10: DM3.17] and accessible ECGs were identified in nationwide Danish health care registers from 2000 to 2021. Patients with AAV were matched 1:3 on age, sex, and calendar year of ECG measurement with controls without AAV. Index date was defined as the date of ECG measurement. ECGs were hierarchically categorized as either no, minor, or major ECG abnormalities according to contemporary literature. The associations between ECG abnormalities and fatal CVD were assessed in multiple Cox regression models adjusted for age, sex, and comorbidities. Counterfactual G-estimation of hazard ratios (HRs) standardized to age and sex was performed to estimate the 5-year absolute risk (AR) of fatal CVD according to ECG-abnormalities. Results A total of 1431 patients with AAV were matched to 4293 controls. The median age of patients with AAV and matched controls was 69 (IQR 58-77) years, and 52.3% were males. Median study follow-up time was 4.8 (IQR, 2.7-7.8) years. AAV was associated with increased prevalence of left ventricular hypertrophy (17.5% vs. 12.5%, p < 0.001), ST-T deviations (10.1% vs. 7.1%, p < 0.001), atrial fibrillation (9.6% vs. 7.5%, p = 0.017) and corrected QT prolongation (5.9% vs. 3.6%, p < 0.001) compared to controls. When examining associations of fatal CVD across no, minor or major ECG abnormalities, only the patients with AAV and major ECG abnormalities had a higher risk of fatal CVD [HR 1.99 (95% CI, 1.49-2.65)], compared to matched controls. This corresponded to a significantly higher standardized 5-year AR of fatal CVD compared to controls: 19.14% (95% CI, 16-22%) vs. 9.41% (95% CI, 8-11%). Conclusion AAV was associated with a higher prevalence of major ECG abnormalities such as left ventricular hypertrophy, atrial fibrillation, and ST-T deviations compared to matched controls. Moreover, the patients with AAV demonstrating major ECG abnormalities had a particularly elevated risk of fatal CVD. This indicates that AAV patients with major ECG abnormalities are a distinct high-risk group within AAV patients prone to cardiovascular-related mortality.

Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level

Artificial intelligence-enabled electrocardiogram (ECG) algorithms are gaining prominence for the early detection of cardiovascular (CV) conditions, including those not traditionally associated with conventional ECG measures or expert interpretation. This study develops and validates such models for simultaneous prediction of 15 different common CV diagnoses at the population level. We conducted a retrospective study that included 1,605,268 ECGs of 244,077 adult patients presenting to 84 emergency departments or hospitals, who underwent at least one 12-lead ECG from February 2007 to April 2020 in Alberta, Canada, and considered 15 CV diagnoses, as identified by International Classification of Diseases, 10th revision (ICD-10) codes: atrial fibrillation (AF), supraventricular tachycardia (SVT), ventricular tachycardia (VT), cardiac arrest (CA), atrioventricular block (AVB), unstable angina (UA), ST-elevation myocardial infarction (STEMI), non-STEMI (NSTEMI), pulmonary embolism (PE), hypertrophic cardiomyopathy (HCM), aortic stenosis (AS), mitral valve prolapse (MVP), mitral valve stenosis (MS), pulmonary hypertension (PHTN), and heart failure (HF). We employed ResNet-based deep learning (DL) using ECG tracings and extreme gradient boosting (XGB) using ECG measurements. When evaluated on the first ECGs per episode of 97,631 holdout patients, the DL models had an area under the receiver operating characteristic curve (AUROC) of <80% for 3 CV conditions (PTE, SVT, UA), 80–90% for 8 CV conditions (CA, NSTEMI, VT, MVP, PHTN, AS, AF, HF) and an AUROC > 90% for 4 diagnoses (AVB, HCM, MS, STEMI). DL models outperformed XGB models with about 5% higher AUROC on average. Overall, ECG-based prediction models demonstrated good-to-excellent prediction performance in diagnosing common CV conditions.

Safety and prescribing recommendations for verapamil in newly diagnosed pediatric type 1 diabetes (T1D): The CLVer experience

ObjectivesTo report the safety and side effects associated with taking verapamil for beta-cell preservation in children with newly-diagnosed T1D. Research Design and MethodsEighty-eight participants aged 8.5 to 17.9 years weighing ≥ 30 kg were randomly assigned to verapamil (N = 47) or placebo (N = 41) within 31 days of T1D diagnosis and followed for 12 months from diagnosis, main CLVer study. Drug dosing was weight-based with incremental increases to full dosage. Side effect monitoring included serial measurements of pulse, blood pressure, liver enzymes, and electrocardiograms (ECGs). At study end, participants were enrolled in an observational extension study (CLVerEx), which is ongoing. No study drug is provided during the extension, but participants may use verapamil if prescribed by their diabetes care team. ResultsOverall rates of adverse events were low and comparable between verapamil and placebo groups. There was no difference in the frequency of liver function abnormalities. Three CLVer participants reduced or discontinued medication due to asymptomatic ECG changes. One CLVerEx participant (18 years old), treated with placebo during CLVer, who had not had a monitoring ECG, experienced complete AV block with a severe hypotensive episode 6 weeks after reaching his maximum verapamil dose following an inadvertent double dose on the day of the event. ConclusionsThe use of verapamil in youth newly-diagnosed with T1D appears generally safe and well tolerated with appropriate monitoring. We strongly recommend monitoring for potential side effects including an ECG at screening and an additional ECG once full dosage is reached.ClinicalTrials.gov number: NCT04233034.

Bone Mechanical Loading Reduces Heart Rate and Increases Heart Rate Variability via the Autonomic Nervous System in Anesthetized Mice

Mechanical loading of bone creates interstitial fluid flow and shear stress within the bone lacunocanalicular system, leading to alterations of bone size and mass. Bone plays an important role as an endocrine organ, releasing endocrine signaling molecules that impact systemic physiology. Further, the central and peripheral nervous systems regulate bone mass in response to strain through alterations in autonomic tone. This suggests the potential for interplay between bone and other organs via endocrine signaling and autonomic innervation. In this study, our group investigated this potential interplay by focusing on bone and heart. We hypothesized that mechanical bone loading of mice in vivo would result in altered cardiac function. To test this hypothesis, we performed acute in vivo mechanical loading on right leg tibias of anesthetized TOPGAL and CD-1 mice. Tibias underwent compressive cyclic loading (a sine wave) that modulates between -0.3N and -9.0N for 300 cycles at 2 Hz. Cardiac function was monitored with lead II electrocardiogram (ECG) data, heart rate (HR), and heart rate variability (HRV). Immediate, transient reduction in resting HR (0.93 ± 0.013 fold change from baseline, n=6-7, p&lt;0.01 compared to control) was achieved during tibial loading in 6-month-old male and female mice, with concurrent increase in HRV (1.24 ± 0.11 fold change from baseline, n=6-7, p&lt;0.01). Both HR and HRV returned to baseline levels upon completion of the loading process. ECG measurements, QRS and corrected QT (QTc), were not found to be altered (p&gt;0.05) during loading and 30 minutes following loading. In further studies, 1N-2N did not produce a decrease in HR, while 3N (0.92 ± 0.077 fold change from baseline, n=12, p&lt;0.05) and 9N (0.93 ± 0.061 fold change from baseline, n=13, p&lt;0.05) decreased HR. There was decreased magnitude and responsiveness with aging to 11 months old at 3N and 9N (p&gt;0.05), suggesting the response may weaken with age. The immediate, transient nature of the changes to resting HR and HRV suggest a neural mechanism for this response. To test this mechanism, we inhibited local neuronal afferent activity by injecting lidocaine (2.5mg/kg) near the tibia prior to loading. In doing so, the decrease in HR during loading was significantly diminished (vehicle 0.90 ± 0.060 vs. lidocaine 0.97 ± 0.042 fold change from baseline, n=7-8, p&lt;0.05). To test the efferent arm of the response, mice were injected with the parasympathetic, muscarinic acetylcholine receptor antagonist atropine (2mg/kg), or the sympathetic, β1/β2 receptor antagonist propranolol (10mg/kg). Propranolol significantly inhibited the HR decrease during loading (vehicle 0.88 ± 0.095 vs. propranolol 0.98 ± 0.026 fold change from baseline, n=7-8, p&lt;0.05), while atropine did not (n=8-11, p&gt;0.05). These findings suggest that reductions in sympathetic tone on the heart during bone loading led to the decrease in HR. In conclusion, this study uncovered a reflexive neural connection between bone and heart that is affected by age and mediated by afferent neurons in the tibia and alterations in efferent sympathetic tone to the heart. These findings provide new knowledge that builds on the current science of bone remodeling and its regulation. This study was funded by HRSA Medical Student Education Grant T99HP39202 and NIH P01AG039355. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Respiratory rate monitoring in ICU patients and healthy volunteers using electrical impedance tomography: a validation study

Objective. The respiratory rate (RR) is considered one of the most informative vital signals. A well-validated standard for RR measurement in mechanically ventilated patient is capnography; a noninvasive technique for expiratory CO2 measurements. Reliable RR measurements in spontaneously breathing patients remains a challenge as continuous mainstream capnography measurements are not available. This study aimed to assess the accuracy of RR measurement using electrical impedance tomography (EIT) in healthy volunteers and intensive care unit (ICU) patients on mechanical ventilation and spontaneously breathing post-extubation. Comparator methods included RR derived from both capnography and bioimpedance electrocardiogram (ECG) measurements. Approach. Twenty healthy volunteers wore an EIT belt and ECG electrodes while breathing through a capnometer within a 10–40 breaths per minute (BPM) range. Nineteen ICU patients underwent similar measurements during pressure support ventilation and spontaneously breathing after extubation from mechanical ventilation. Stable periods with regular breathing and no artefacts were selected, and agreement between measurement methods was assessed using Bland–Altman analysis for repeated measurements. Main result. Bland–Altman analysis revealed a bias less than 0.2 BPM, with tight limits of agreement (LOA) ±1.5 BPM in healthy volunteers and ventilated ICU patients when comparing EIT to capnography. Spontaneously breathing ICU patients had wider LOA (±2.5 BPM) when comparing EIT to ECG bioimpedance, but gold standard comparison was unavailable. RR measurements were stable for 91% of the time for capnography, 68% for EIT, and 64% of the ECG bioimpedance signals. After extubation, the percentage of stable periods decreased to 48% for EIT signals and to 55% for ECG bioimpedance. Significance. In periods of stable breathing, EIT demonstrated excellent RR measurement accuracy in healthy volunteers and ICU patients. However, stability of both EIT and ECG bioimpedance RR measurements declined in spontaneously breathing patients to approximately 50% of the time.

Monitoring Adolescent and Young Adult Patients With Cancer via a Smart T-Shirt: Prospective, Single-Cohort, Mixed Methods Feasibility Study (OncoSmartShirt Study).

Wearables that measure vital parameters can be potential tools for monitoring patients at home during cancer treatment. One type of wearable is a smart T-shirt with embedded sensors. Initially, smart T-shirts were designed to aid athletes in their performance analyses. Recently however, researchers have been investigating the use of smart T-shirts as supportive tools in health care. In general, the knowledge on the use of wearables for symptom monitoring during cancer treatment is limited, and consensus and awareness about compliance or adherence are lacking. The aim of this study was to evaluate adherence to and experiences with using a smart T-shirt for the home monitoring of biometric sensor data among adolescent and young adult patients undergoing cancer treatment during a 2-week period. This study was a prospective, single-cohort, mixed methods feasibility study. The inclusion criteria were patients aged 18 to 39 years and those who were receiving treatment at Copenhagen University Hospital - Rigshospitalet, Denmark. Consenting patients were asked to wear the Chronolife smart T-shirt for a period of 2 weeks. The smart T-shirt had multiple sensors and electrodes, which engendered the following six measurements: electrocardiogram (ECG) measurements, thoracic respiration, abdominal respiration, thoracic impedance, physical activity (steps), and skin temperature. The primary end point was adherence, which was defined as a wear time of >8 hours per day. The patient experience was investigated via individual, semistructured telephone interviews and a paper questionnaire. A total of 10 patients were included. The number of days with wear times of >8 hours during the study period (14 d) varied from 0 to 6 (mean 2 d). Further, 3 patients had a mean wear time of >8 hours during each of their days with data registration. The number of days with any data registration ranged from 0 to 10 (mean 6.4 d). The thematic analysis of interviews pointed to the following three main themes: (1) the smart T-shirt is cool but does not fit patients with cancer, (2) the technology limits the use of the smart T-shirt, and (3) the monitoring of data increases the feeling of safety. Results from the questionnaire showed that the patients generally had confidence in the device. Although the primary end point was not reached, the patients' experiences with using the smart T-shirt resulted in the knowledge that patients acknowledged the need for new technologies that improve supportive cancer care. The patients were positive when asked to wear the smart T-shirt. However, technical and practical challenges in using the device resulted in low adherence. Although wearables might have potential for home monitoring, the present technology is immature for clinical use.

Comparison of electrocardiogram (ECG) waveforms and centralized ECG measurements between a wearable mobile ECG device and a standard 12-lead ECG

Comparison of electrocardiogram (ECG) waveforms and centralized ECG measurements between a wearable mobile ECG device and a standard 12-lead ECG

Evaluating the Effects of Acute and Chronic Doxorubicin Administration on Cardiac Function Through Electrocardiographic Measurements

Background: The medications used to treat cancer can lead to cardiac problems, which restricts their use. Furthermore, the method these medications are taken seems to have an impact on varied out-comes. Therefore, this study aimed to examine whether administering doxorubicin (DOX) agent acutely and chronically has distinct impacts on the electrical activity of the heart. Materials and Methods: Twenty-six male Wistar-Albino rats, weighing between 200-250 grams, were split into three groups: control group; no treatment was applied to animals (n=8), DOX acute group: a single dosage of 15.05 mg/kg of DOX was given at the end of the 3 weeks (n=8), DOX chronic group; which received an intraperitoneal (i.p.) 2.15 mg/kg DOX for 3 weeks, 7 doses in total (n=10). At the end of the experimental period, electrocardiogram (ECG) measurements were taken for all animals and evaluated. Results: ECG data showed that heart rate (HR), P wave amplitude, and P duration did not differ between the acute and control groups but did statistically significantly declined in the chronic group. In both DOX groups, PR interval remained unchanged compared to the control. Also, RR interval increased significantly in the chronic group while it remained unchanged in the acute DOX dose group. The QRS duration was found to have considerably increased in both DOX groups. Furthermore, it was found that both DOX groups had a considerable increase in the QT interval, although the chronic group's increase was more noticeable. Conclusions: In conclusion, it is thought that the ways in which these medications are administered may result in significant variations in heart function. Acute DOX treatment appears to be less harmful than chronic exposure, as evidenced by its lack of adverse effects, particularly on P wave amplitude (a measure of atrial contraction) and P wave duration (the length of the contraction). However, more research is required to validate these findings. Key Words: Electrocardiogram (ECG), Doxorubicin (DOX), Cardiotoxicity

Electrocardiographic manifestations in female team handball players: analyzing ECG changes in athletes.

Long-term intense training leads to structural, functional, and electrical remodeling of the heart. How different sports affect the heart has not been fully investigated, particularly for female athletes. The aim of the present study was to investigate the morphology of 12-lead resting electrocardiogram (ECG) in elite female handball players compared to non-athlete female subjects. Potential changes will be explored to see if they could be explained by differences in cardiac dimensions and exercise hours. A cross-sectional study of 33 elite female team handball players compared to 33 sex and age-matched, non-athletic controls (age range 18-26 years) was performed. All participants underwent a resting 12-lead ECG and an echocardiographic examination. ECG variables for left ventricular hypertrophy and durations were evaluated and adjusted for cardiac dimensions and exercise hours using ANCOVA analysis. A linear regression analysis was used to describe relation between echocardiographic and ECG measures and exercise hours. The female handball players had larger cardiac dimensions and significantly lower heart rate and QTc duration (Bazett's formula) as well as increased QRS and QT durations compared to controls. The 12-lead sum of voltage and the 12-lead sum of voltage ∗ QRS were significantly higher among handball players. Changes in ECG variables reflecting the left ventricle could in part be explained by left ventricular size and exercise hours. Correlation with exercise hours were moderately strong in most of the echocardiographic measures reflecting left ventricular (LV), left ventricular mass (LVM), left atrium (LA) and right atrium (RA) size. Poor to fair correlations were seen in the majority of ECG measures. Female team handball players had altered ECGs, longer QRS and QT durations, higher 12-lead sum of voltage and 12-lead sum of voltage ∗ QRS as well as shorter QTc (Bazett's formula) duration compared to non-athletic controls. These findings could only partly be explained by differences in left ventricular size. Despite larger atrial size in the athletes, no differences in P-wave amplitude and duration were found on ECG. This suggest that both structural, and to some degree electrical remodeling, occur in the female team handball players' heart and highlight that a normal ECG does not rule out structural adaptations. The present study adds knowledge to the field of sports cardiology regarding how the heart in female team handball players adapts to this type of sport.

Simple risk scoring using sinus rhythm electrocardiograms predicts the incidence of atrial fibrillation in the general population

Atrial fibrillation (AF) is an arrhythmic disease. Prediction of AF development in healthy individuals is important before serious complications occur. We aimed to develop a risk prediction score for future AF using participants’ data, including electrocardiogram (ECG) measurements and information such as age and sex. We included 88,907 Japanese participants, aged 30–69 years, who were randomly assigned to derivation and validation cohorts in a ratio of 1:1. We performed multivariate logistic regression analysis and obtained the standardised beta coefficient of relevant factors and assigned scores to them. We created a score based on prognostic factors for AF to predict its occurrence after five years and applied it to validation cohorts to assess its reproducibility. The risk score ranged from 0 to 17, consisting of age, sex, PR prolongation, QT corrected for heart rate prolongation, left ventricular hypertrophy, premature atrial contraction, and left axis deviation. The area under the curve was 0.75 for the derivation cohort and 0.73 for the validation cohort. The incidence of new-onset AF reached over 2% at 10 points of the risk score in both cohorts. Thus, in this study, we showed the possibility of predicting new-onset AF using ECG findings and simple information.

Simple models vs. deep learning in detecting low ejection fraction from the electrocardiogram

Abstract Aims Deep learning methods have recently gained success in detecting left ventricular systolic dysfunction (LVSD) from electrocardiogram (ECG) waveforms. Despite their high level of accuracy, they are difficult to interpret and deploy broadly in the clinical setting. In this study, we set out to determine whether simpler models based on standard ECG measurements could detect LVSD with similar accuracy to that of deep learning models. Methods and results Using an observational data set of 40 994 matched 12-lead ECGs and transthoracic echocardiograms, we trained a range of models with increasing complexity to detect LVSD based on ECG waveforms and derived measurements. The training data were acquired from the Stanford University Medical Center. External validation data were acquired from the Columbia Medical Center and the UK Biobank. The Stanford data set consisted of 40 994 matched ECGs and echocardiograms, of which 9.72% had LVSD. A random forest model using 555 discrete, automated measurements achieved an area under the receiver operator characteristic curve (AUC) of 0.92 (0.91–0.93), similar to a deep learning waveform model with an AUC of 0.94 (0.93–0.94). A logistic regression model based on five measurements achieved high performance [AUC of 0.86 (0.85–0.87)], close to a deep learning model and better than N-terminal prohormone brain natriuretic peptide (NT-proBNP). Finally, we found that simpler models were more portable across sites, with experiments at two independent, external sites. Conclusion Our study demonstrates the value of simple electrocardiographic models that perform nearly as well as deep learning models, while being much easier to implement and interpret.

Open access EEG dataset of repeated measurements from a single subject for microstate analysis

Electroencephalography (EEG) microstate analysis is a neuroimaging analytical method that has received considerable attention in recent years and is widely used for analysing EEG signals. EEG is easily influenced by internal and external factors, which can affect the repeatability and stability of EEG microstate analysis. However, there have been few reports and publicly available datasets on the repeatability of EEG microstate analysis. In the current study, a 39-year-old healthy male underwent a total of 60 simultaneous electroencephalography and electrocardiogram measurements over a period of three months. After the EEG recording was completed, magnetic resonance imaging (MRI) was also conducted. To date, this EEG dataset has the highest number of repeated measurements for one individual. The dataset can be used to assess the stability and repeatability of EEG microstates and other analytical methods, to decode resting EEG states among subjects with open eyes, and to explore the stability and repeatability of cortical spatiotemporal dynamics through source analysis with individual MRI.

Electrocardiogram signal processing algorithm on microcontroller using wavelet transform method

The electrocardiogram (ECG) is an important parameter for analyzing the cardiac system. It serves as the primary diagnostic tool for patients with suspected heart disease, guiding appropriate cardiac investigations according to the disease or condition suspected. However, ECG measurements may generate noise, leading to false diagnoses. The wavelet transform is an effective and widely-used technique for eliminating noise. Typically, analysis and generation algorithms are developed on computer and using software built in. This paper presents a noise elimination algorithm based on the wavelet transform method, designed to operate on resource-limited Node microcontroller unit (MCU). An efficiency study was conducted to determine the optimum mother wavelet implementation of the algorithm, and the results showed that, when considering synthetic ECG signals, db4 was the most suitable for eliminating interference by achieving the highest signal to noise ratio (SNR) and correlation coefficient. In addition, this algorithm prototype can analyze ECG signals using the wavelet transform method processed in a microcontroller and is accurate compared to reliable programs. It has the potential to be further developed into a low-cost portable ECG signal measurement tool for use in remote medicine, healthcare facilities in resource-limited areas, education and training, as well as home monitoring for chronic patients.

Electrocardiographic and Autonomic Nervous System Changes after Changes in the Posture of Children and Adolescents with Duchenne Muscular Dystrophy.

Duchenne Muscular Dystrophy (DMD) is a rare inherited neuromuscular disease. At first, cardiac involvement may be asymptomatic. Therefore, assessing patients using non-invasive methods can help detect any changes. Analyze the electrocardiogram (ECG) test and heart rate variability (HRV) of the DMD group and compare the information with that of the age-matched control group. A prospective study with 27 male patients with DMD (11.9 years old), who underwent clinical evaluation, ECG, echocardiogram, and Holter monitoring. ECG (200% increase) was assessed by two independent observers. HRV was measured over time (24 h) and in the frequency domain, in the supine and sitting positions. The healthy group consisted of nine patients (11.0 years old). A value of p < 0.05 was considered statistically significant. The mean ejection fraction (EF) was 60% (34 to 71%). The Kappa coefficient for ECG measurements ranged from 0.64 to 1.00. An increase in the R/S ratio in V1 was observed in 25.9% of the subjects, pathological Q wave in 29.6%, and fragmented QRS in 22.2% in inferior/high lateral regions, with a negative correlation with EF (p = 0.006). There was low HRV, without the influence of any variable, including treatment. With the change in position, there was an increase in HR (p = 0.004), but there was no change in HRV. The LF/HF ratio was 2.7 in the DMD group and 0.7 in the control group (p = 0.002). In DMD subjects, prominent R waves in V1 and changes in the inferior/high lateral regions occurred in almost 30% of the cases. Lower vagal tone was observed without the influence of the variables age, ejection fraction, QT dispersion, and treatment. Despite the increase in HR, there was no adequate HRV response to the change in position.

A combination of logical judging circuit and water-resistant ultrathin film PEDOT: PSS electrode for noninvasive ECG measurement

Heart disease-related deaths have increased in recent decades, with most patients dying of sudden cardiac arrest. In such instances, the effect of regular electrocardiogram (ECG) measurements is minimal. Therefore, long-term ECG monitoring has become increasingly important. In this paper, we report a non-adhesive high accuracy ECG monitoring system that can be used in various scenarios without interfering with daily activities. The ECG ultra-thin film electrode is made by water-resistant material based on poly(3,4-ethylenedioxythiophene) poly(4-styrenesulfonate) (PEDOT: PSS) electrode doped with ethylene glycol (EG) and xylitol, to improve the noise signal caused by sweat. The optimal ratio of the three ingredients of PEDOT: PSS/xylitol/EG was determined experimentally to accommodate the ECG monitoring. By using the proposed selectively closed multi-channel single-lead logic circuit, the noise of ECG signal received from the proposed film electrode can be successfully reduced during broad-area electrode measurements, thus to improve ECG measurement accuracy.

Relationship between motion artifacts and fiber length of a flocked electrode in electrocardiogram measurement

In this paper, we report on the relationship between the fiber length of flocked electrodes and the motion artifacts (MAs) that occur in electrocardiogram (ECG) measurements. During ECG measurements, fluctuations in the ECG baseline are observed when slippage occurs at the skin-to-flocked electrode interface. We evaluated the friction behavior and MAs characteristics of four types of flocked electrode with various fiber lengths (0.3 mm, 0.5 mm, 0.7 mm, and 1.2 mm). As a result, it was confirmed that the longer fiber lengths for the flocked electrodes provided more stable contact with the skin phantom. The correlation coefficients between the ECG waveform without MA and the ECG waveform using each fiber length (0.3 mm, 0.5 mm, 0.7 mm, 1.2 mm) were 0.58, 0.58, 0.81, and 0.91. Since the correlation coefficient close to 1 means less MA, it was shown that the longer fiber length, the higher the MA reduction effect. We also conducted an ECG measurement experiment using an ECG measurement chest band. When the flocked electrode with the 1.2-mm-long fibers was used, the ECG signal was measured successfully with only small MAs from body movements caused by respiration.

Intelligent Electrocardiogram Acquisition Via Ubiquitous Photoplethysmography Monitoring.

Recent advances in machine learning, particularly deep neural network architectures, have shown substantial promise in classifying and predicting cardiac abnormalities from electrocardiogram (ECG) data. Such data are rich in information content, typically in morphology and timing, due to the close correlation between cardiac function and the ECG. However, the ECG is usually not measured ubiquitously in a passive manner from consumer devices, and generally requires 'active' sampling whereby the user prompts a device to take an ECG measurement. Conversely, photoplethysmography (PPG) data are typically measured passively by consumer devices, and therefore available for long-period monitoring and suitable in duration for identifying transient cardiac events. However, classifying or predicting cardiac abnormalities from the PPG is very difficult, because it is a peripherally-measured signal. Hence, the use of the PPG for predictive inference is often limited to deriving physiological parameters (heart rate, breathing rate, etc.) or for obvious abnormalities in cardiac timing, such as atrial fibrillation/flutter ("palpitations"). This work aims to combine the best of both worlds: using continuously-monitored, near-ubiquitous PPG to identify periods of sufficient abnormality in the PPG such that prompting the user to take an ECG would be informative of cardiac risk. We propose a dual-convolutional-attention network (DCA-Net) to achieve this ECG-based PPG classification. With DCA-Net, we prove the plausibility of this concept on MIMIC Waveform Database with high performance level (AUROC 0.9 and AUPRC 0.7) and receive satisfactory result when testing the model on an independent dataset (AUROC 0.7 and AUPRC 0.6) which it is not perfectly-matched to the MIMIC dataset.