Concurrent Electrocardiogram Research Articles

Heartbeat Detection in Gyrocardiography Signals without Concurrent ECG Tracings.

A heartbeat generates tiny mechanical vibrations, mainly due to the opening and closing of heart valves. These vibrations can be recorded by accelerometers and gyroscopes applied on a subject's chest. In particular, the local 3D linear accelerations and 3D angular velocities of the chest wall are referred to as seismocardiograms (SCG) and gyrocardiograms (GCG), respectively. These signals usually exhibit a low signal-to-noise ratio, as well as non-negligible amplitude and morphological changes due to changes in posture and the sensors' location, respiratory activity, as well as other sources of intra-subject and inter-subject variability. These factors make heartbeat detection a complex task; therefore, a reference electrocardiogram (ECG) lead is usually acquired in SCG and GCG studies to ensure correct localization of heartbeats. Recently, a template matching technique based on cross correlation has proven to be particularly effective in recognizing individual heartbeats in SCG signals. This study aims to verify the performance of this technique when applied on GCG signals. Tests were conducted on a public database consisting of SCG, GCG, and ECG signals recorded synchronously on 100 patients with valvular heart diseases. The results show that the template matching technique identified heartbeats in GCG signals with a sensitivity and positive predictive value (PPV) of 87% and 92%, respectively. Regression, correlation, and Bland-Altman analyses carried out on inter-beat intervals obtained from GCG and ECG (assumed as reference) reported a slope of 0.995, an intercept of 4.06 ms (R2 > 0.99), a Pearson's correlation coefficient of 0.9993, and limits of agreement of about ±13 ms with a negligible bias. A comparison with the results of a previous study obtained on SCG signals from the same database revealed that GCG enabled effective cardiac monitoring in significantly more patients than SCG (95 vs. 77). This result suggests that GCG could ensure more robust and reliable cardiac monitoring in patients with heart diseases with respect to SCG.

Color M-Mode Ultrasound to Assess Carotid Arteries: A Feasibility Study

Introduction: The gradual process of atherosclerosis involves both structural and functional changes that can accumulate over years and contribute to cardiovascular risk. We propose the use of color M-mode ultrasound to evaluate the distensibility of the carotid artery as a quantitative measurement of dynamic function that could evaluate age-related vascular change when considering atherosclerotic cardiovascular disease evaluation and risk. The aim of the study was to assess the feasibility of color M-mode ultrasound to evaluate age-related changes in the carotid arteries. Methods: Color M-mode ultrasound of the distal common carotid artery (CCA) with concurrent electrocardiogram tracing was performed on 62 adults (19 men and 43 women) in 3 age groups: young adult (20-44 years; n = 22), middle age (45-64 years; n = 20), and senior (≥65 years; n = 20). Color M-mode ultrasound images of the CCA were processed offline using ImageJ to quantify total, red, green, and blue (RGB) pixels in systole and diastole. The CCA distensibility was calculated by using the following equation: distensibility coefficient (DC) = (systolic − diastolic diameter)/(diastolic diameter × pulse pressure). The difference in ultrasound parameters among the 3 age groups was analyzed by one-way analysis of variance (ANOVA). Intraobserver and interobserver reliability of offline imaging processing was tested using intraclass correlation coefficient (ICC). Results: We observed a significant difference in CCA distensibility among the 3 age groups ( P < .05). The interobserver reproducibility and intraobserver repeatability in performing offline image processing were excellent (ICC >0.85). Conclusion: These findings suggest that color M-mode ultrasound is feasible in evaluating changes in CCA distensibility and systolic pixel values representing CCA blood flow significantly decreased in seniors.

Irregular Autonomic Modulation Predicts Risky Drinking and Altered Ventromedial Prefrontal Cortex Response to Stress in Alcohol Use Disorder.

Autonomic dysfunction has been associated with risky drinking and alcohol use disorder (AUD). Although autonomic nervous system (ANS) activity has been attributed to the ventromedial prefrontal cortex (VmPFC)-limbic-striatal regions, the specific role of ANS disruption in AUD and its association with these regions remain unclear. Using functional magnetic resonance imaging (fMRI) and concurrent electrocardiogram (ECG), the current study examined neural correlates of ANS activity in AUD and its role in AUD pathology. Demographically matched 20 AUD patients and 20 social drinkers (SD) completed an fMRI task involving repeated exposure to stress, alcohol-cue and neutral-relaxing images in a block design. Based on the known VmPFC-limbic-striatal functions involved in emotions, reward and the ANS, we performed a regions of interest (ROI) analysis to examine the associations between ANS activity and neural responses in the VmPFC, amygdala, and ventral striatum. Across conditions, AUD patients showed significantly higher levels of overall heart rate (HR) and approximate entropy (ApEn) compared to SD (Ps < 0.05). In all participants, increased HR was associated with greater drinking volume (P < 0.05). In addition, higher ApEn levels were associated with greater drinking volume (P < 0.05) and decreased right VmPFC response to stress (P < 0.05). Our findings demonstrate ANS disruption in AUD indexed by high overall HR and ApEn. The association between ApEn and rVmPFC response suggests that ApEn may play a role in modulating drinking via interactions with neural regions of emotion regulation. These findings provide insight into patterns of ANS disruption and their relevance to AUD pathology.

Initial Experience in Monitoring QT Intervals Using a Six-lead Contactless Mobile Electrocardiogram in an Inpatient Setting.

Mobile electrocardiograms (ECGs) (mECGs) using smartphone applications are an emerging technology. In the coronavirus disease 2019 (COVID-19) era, minimizing patient contact has gained increasing importance. Additionally, increased QT/corrected QT (QTc) monitoring has concurrently been required. The KardiaMobile 6L ECG device, cleared by the United States Food and Drug Administration (FDA) for recording ECGs, along with the KardiaStation tablet application is a platform (AliveCor, Mountain View, CA, USA) that addresses these two issues. A team of residents, fellows, hospitalists, and cardiologists identified inpatients in need of QT/QTc interval monitoring to pilot the adoption of a system composed of a KardiaMobile 6L ECG device with the accompanying KardiaStation tablet application. Concurrent standard ECGs provided validation. Adoption and performance issues were recorded. Four patients agreed to participate in QT/QTc interval monitoring, three of whom were positive for severe acute respiratory syndrome coronavirus 2 viral infection. After basic instructions were given to the patients and their clinical nurses, all patients recorded mECGs successfully. Patients were able to record their own mECG tracings at least once without any assistance. The 12-lead ECGs and mECGs each showed the correct rhythm, and the measured QTc intervals on each modality were consistently acceptable (< 500 ms). Contactless ECGs were successfully uploaded to KardiaStation for QT/QTc interval measurement and archiving. In this study, we showed that an FDA-cleared product, KardiaMobile 6L, has the ability to provide high-quality contactless ECGs for reliable QT/QTc interval measurements. Hospitalized patients were able to perform recordings when requested after receiving simple instructions at the time of first use. This technology has applications during the COVID-19 pandemic and beyond.

Intramuscular Diphenhydramine Does Not Affect Acute Doxorubicin Infusion-Related Arrhythmia Number or Severity in a Prospective Crossover Study in Canine Lymphoma: A Pilot Study.

Background: Doxorubicin (DOX) is one of the most effective chemotherapeutics for canine high-grade lymphoma. In addition to dose-dependent chronic cardiotoxicity, DOX can trigger acute cardiac arrhythmias during drug infusion. Diphenhydramine premedication is commonly used, as histamine release is a proposed mechanism for DOX-associated arrhythmogenesis.Hypothesis/Objectives: The study objectives were to evaluate the incidence and severity of DOX infusion-related cardiac arrhythmias in dogs with high-grade lymphoma and evaluate the effect of diphenhydramine premedication on arrhythmia frequency and severity during and after DOX infusion.Animals: Twenty-two client-owned dogs with cytologically/histopathologically confirmed high-grade lymphoma were recruited, of which 19 were enrolled and 9 completed the study.Methods: Dogs were screened by echocardiogram and concurrent electrocardiogram for this randomized prospective crossover study. Group A received no premedication for DOX #1 and was premedicated with diphenhydramine for DOX #2; Group B received diphenhydramine with DOX #1 and no premedication for DOX #2. For both visits, Holter monitor data were collected for 1 h pre-DOX and 3 h post-DOX administration.Results: Nineteen dogs were enrolled and 9 dogs [Group A (5), Group B (4)] completed the protocol. There was no statistical difference between the DOX alone and DOX + diphenhydramine when evaluating the total number of ventricular premature complexes (VPCs, P = 0.34), change in VPCs/hour (P = 0.25), total number of atrial premature complexes (APCs, P = 0.5), change in APCs/hour (P = 0.06), or ventricular arrhythmia severity score (P > 0.99).Conclusions and clinical importance: This study demonstrates that in these dogs with rigorous pretreatment cardiovascular screening, DOX infusion did not induce significant arrhythmias. Furthermore, these data suggest that, with this screening approach, diphenhydramine may not alter the arrhythmia number or severity in canine DOX recipients.

Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study

Market-applicable concurrent electrocardiogram (ECG) diagnosis for multiple heart abnormalities that covers a wide range of arrhythmias, with better-than-human accuracy, has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated multilabel diagnosis of heart rhythm or conduction abnormalities by real-time ECG analysis. We used a dataset of ECGs (standard 10 s, 12-channel format) from adult patients (aged ≥18 years), with 21 distinct rhythm classes, including most types of heart rhythm or conduction abnormalities, for the diagnosis of arrhythmias at multilabel level. The ECGs were collected from three campuses of Tongji Hospital (Huazhong University of Science and Technology, Wuhan, China) and annotated by cardiologists. We used these datasets to develop a convolutional neural network approach to generate diagnoses of arrythmias. We collected a test dataset of ECGs from a new group of patients not included in the training dataset. The test dataset was annotated by consensus of a committee of board-certified, actively practicing cardiologists. To evaluate the performance of the model we assessed the F1 score and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, as well as quantifying sensitivity and specificity. To validate our results, findings for the test dataset were compared with diagnoses made by 53 ECG physicians working in cardiology departments who had a wide range of experience in ECG interpretation (range 0 to >12 years). An external public validation dataset of 962 ECGs from other hospitals was used to study generalisability of the diagnostic model. Our training and validation dataset comprised 180 112 ECGs from 70 692 patients, collected between Jan 1, 2012, and Apr 30, 2019. The test dataset comprised 828 ECGs corresponding to 828 new patients, recorded between Sept 11, 2012, and Aug 30, 2019. At the multilabel level, our deep learning approach to diagnosing heart abnormalities resulted in an exact match in 658 (80%) of 828 ECGs, exceeding the mean performance of physicians (552 [67%] for physicians with 0-6 years of experience; 571 [69%] for physicians with 7-12 years of experience; 621 [75%] for physicians with more than 12 years of experience). Our model had an overall mean F1 score of 0·887 compared with 0·789 for physicians with 0-6 years of experience, 0·815 for physicians with 7-12 years of experience, and 0·831 for physicians with more than 12 years of experience. The model had a mean AUC ROC score of 0·983 (95% CI 0·980-0·986), sensitivity of 0·867 (0·849-0·885) and specificity of 0·995 (0·994-0·996). Promising F1 scores were also obtained from the external public database using our proposed model without any model modifications (mean F1 scores of 0·845 in multilabel and 0·852 in single-label ECGs). Our model is more accurate than physicians working in cardiology departments at distinguishing a range of distinct arrhythmias in single-label and multilabel ECGs, laying a promising foundation for computational decision-support systems in clinical applications. National Natural Science Foundation of China and Hubei Science and Technology Project.

Accurate Heartbeat Detection on Ballistocardiogram Accelerometric Traces

This article presents an automated procedure for acquisition and analysis of BallistoCardioGraphy (BCG) traces. A triaxial accelerometer and a microcontroller unit are used to record heart-induced recoil forces generated from a lying subject. The problem of BCG J-peak annotation is split into two subtasks: candidates extraction, based on a detection signal, and actual annotation, guided by subject-specific search windows. Such a procedure is derived from an automatic calibration, which is carried out with no need of concurrent ElectroCardioGram (ECG) or user intervention. The algorithm also implements postannotation checks for refinement of annotation, which effectively reduces the number of missed J-peaks. The impact of each algorithm phase is analyzed, assessing statistical significance of each step; finally, performance is optimized in a data-driven fashion. Results show that the proposed methodology is able to achieve high sensitivity and precision (the median score is 98.9% and 98.1%, respectively) in J-peak detection. The quality of J-peaks timing annotation is further demonstrated by a very low discrepancy between BCG and ECG heart rate (HR) estimates. Overall population, the standard deviation of such error was found to be approximately 6.56 ms, whereas the mean absolute error was just 4.7 ms (i.e., ≈ 1.18; Ts, where Ts = 4 ms is the sampling period). Such scores, indeed, improve over recent related literature.

6 Accuracy of Electrocardiography and Agreement with Echocardiography in the Diagnosis of Left Atrial Enlargement in Pediatric Patients: A Case-Control Study

Left atrial enlargement (LAE) has been established as a marker for diastolic dysfunction in left-sided cardiac disease. The gold-standard for diagnosis is the echocardiogram. In clinical practice pediatric patients who are suspicious for LAE often receive echocardiograms with concurrent electrocardiograms (ECGs) to exclude other diagnoses. Given the wider access and lower economic cost of ECGs compared to echocardiograms, ECGs have great potential to be used as a screening test to identify patients who would benefit from further investigation by echocardiography. However, the use of ECG criteria for the diagnosis of LAE has not been validated in the pediatric population. To identify the diagnostic utility of independent or combinations of ECG criteria to diagnose LAE in the pediatric population. A large retrospective case-control study (n=492) was conducted at a pediatric hospital in patients identified by echocardiography as meeting ≥1 of the following LAE criteria: 1) left atrial indexed diameter z-score ≥2.0, or 2) increased left atrial to aortic root ratio at various cutoffs (≥1.4, ≥1.6, ≥1.8). ECG criteria included: 1) a P wave ≥110 msec, 2) P mitrale ≥40 msec, and 3) a terminal negative portion of P wave in lead V1 >40 msec, 4) P/PR segment >1.4, 1.6, or 1.8 in lead II. Sensitivity, specificity, Cohen’s Kappa coefficient (κ), and receiver operating characteristic curves (ROC) were constructed to investigate the diagnostic utility of single and combination criteria. Diagnostic performance of ECG criteria had low to moderate sensitivity (20.8–55.0%) and moderate to high specificity (73.6–96.1%). The greatest agreement occurred when using two ECG criteria: P mitrale ≥40 msec and a terminal negative portion of the P wave in lead V1 >40 msec (κ = 0.221, 95% CI 0.060–0.382). ROC curve analysis further demonstrated that terminal negative P wave in V1 is the most strongly correlated with the echocardiographic criteria (AUC:0.580–0.581). The presence of positive ECG criteria indicates a probable diagnosis of LAE, however the absence of ECG criteria cannot be used to exclude this diagnosis. It should be noted that the presence of these criteria should not be used in isolation. Further research is required to determine correlation with clinical presentation and implications for clinical management.

Sleep monitoring sensor using flexible metal strain gauge

This paper presents a sleep monitoring sensor based on a flexible metal strain gauge. As quality of life has improved, interest in sleep quality, and related products, has increased. In this study, unlike a conventional single sensor based on a piezoelectric material, a metal strain gauge-based array sensor based on polyimide and nickel chromium (NiCr) is applied to provide movement direction, respiration, and heartbeat data as well as contact-free use by the user during sleeping. Thin-film-type resistive strain gage sensors are fabricated through the conventional flexible printed circuit board (FPCB) process, which is very useful for commercialization. The measurement of movement direction and respiratory rate during sleep were evaluated, and the heart rate data were compared with concurrent electrocardiogram (ECG) data. An algorithm for analyzing sleep data was developed using MATLAB, and the error rate was 4.2% when compared with ECG for heart rate.

Toward Automated Analysis of Electrocardiogram Big Data by Graphics Processing Unit for Mobile Health Application

With the rapid development of mobile health technologies and applications in recent years, large amounts of electrocardiogram (ECG) signals that need to be processed timely have been produced. Although the CPU-based sequential automated ECG analysis algorithm (CPU-AECG) designed for identifying seven types of heartbeats has been in use for years, it is single-threaded and handling lots of concurrent ECG signals still poses a severe challenge. In this paper, we propose a novel GPU-based automated ECG analysis algorithm (GPU-AECG) to effectively shorten the program executing time. A new concurrency-based GPU-AECG, named cGPU-AECG, is also developed to handle multiple concurrent signals. Compared with the CPU-AECG, our cGPU-AECG achieves a 35 times speedup when handling 24-h-long ECG data, without reducing the classification accuracy. With cGPU-AECG, we can handle 24-h-ECG signals from thousands of users in a few seconds and provide prompt feedback, which not only greatly improves the user experience of mobile health services, but also reduces the economic cost of building healthcare platforms.

Automatic and Robust Delineation of the Fiducial Points of the Seismocardiogram Signal for Non-invasive Estimation of Cardiac Time Intervals.

The purpose of this research was to design a delineation algorithm that could detect specific fiducial points of the seismocardiogram (SCG) signal with or without using the electrocardiogram (ECG) R-wave as the reference point. The detected fiducial points were used to estimate cardiac time intervals. Due to complexity and sensitivity of the SCG signal, the algorithm was designed to robustly discard the low-quality cardiac cycles, which are the ones that contain unrecognizable fiducial points. The algorithm was trained on a dataset containing 48,318 manually annotated cardiac cycles. It was then applied to three test datasets: 65 young healthy individuals (dataset 1), 15 individuals above 44 years old (dataset 2), and 25 patients with previous heart conditions (dataset 3). The algorithm accomplished high prediction accuracy with the rootmean- square-error of less than 5 ms for all the test datasets. The algorithm overall mean detection rate per individual recordings (DRI) were 74, 68, and 42 percent for the three test datasets when concurrent ECG and SCG were used. For the standalone SCG case, the mean DRI was 32, 14 and 21 percent. When the proposed algorithm applied to concurrent ECG and SCG signals, the desired fiducial points of the SCG signal were successfully estimated with a high detection rate. For the standalone case, however, the algorithm achieved high prediction accuracy and detection rate for only the young individual dataset. The presented algorithm could be used for accurate and non-invasive estimation of cardiac time intervals.

Human &amp; swine studies of concurrent 12-lead ECG &amp; MRI

Background 12-lead Electrocardiogram (ECG) is a clinical standard for patient physiological monitoring. An MRI-conditional 12-lead ECG should permit detection of acute myocardial ischemia during MR imaging or MRI-guided therapy, which may improve the handling of patients with ischemic histories. MRI visualization of ischemic episodes can also enhance the understanding of ischemic progression. Previously an MR-conditional 12-lead ECG system was presented. The system was equipped with GradientRampR (2) detect S-wave to T-wave (ST) ECG elevation & perform MR imaging of a Left Anterior Descending (LAD) balloon occlusion from the onset of ischemia to death in a swine model.

A Hybrid Low Power Biopatch for Body Surface Potential Measurement

This paper presents a wearable biopatch prototype for body surface potential measurement. It combines three key technologies, including mixed-signal system on chip (SoC) technology, inkjet printing technology, and anisotropic conductive adhesive (ACA) bonding technology. An integral part of the biopatch is a low-power low-noise SoC. The SoC contains a tunable analog front end, a successive approximation register analog-to-digital converter, and a reconfigurable digital controller. The electrodes, interconnections, and interposer are implemented by inkjet-printing the silver ink precisely on a flexible substrate. The reliability of printed traces is evaluated by static bending tests. ACA is used to attach the SoC to the printed structures and form the flexible hybrid system. The biopatch prototype is light and thin with a physical size of 16 cm × 16 cm. Measurement results show that low-noise concurrent electrocardiogram signals from eight chest points have been successfully recorded using the implemented biopatch.

Retrospective review of the frequency of ECG changes in hyperkalemia.

Experimentally elevated potassium causes a clear pattern of electrocardiographic changes, but, clinically, the reliability of this pattern is unclear. Case reports suggest patients with renal insufficiency may have no electrocardiographic changes despite markedly elevated serum potassium. In a prospective series, 46% of patients with hyperkalemia were noted to have electrocardiographic changes, but no clear criteria were presented. Charts were reviewed for patients who were admitted to a community-based hospital with a diagnosis of hyperkalemia. Inclusion criteria were potassium >/=6 with a concurrent electrocardiogram. Data were abstracted regarding comorbid diagnoses, medications, and treatment. Potassium concentrations were documented along with other electrolytes, pH, creatinine, and biomarkers of cardiac injury. Coincident, baseline, and follow-up electrocardiograms were examined for quantitative and qualitative changes in the QRS and T waves as well as the official cardiology readings. Ninety patients met criteria; two thirds were older than 65, and 48% presented with renal failure. Common medications included beta blockers, insulin, and aspirin; 80% had potassium <7.2. The electrocardiogram was insensitive for diagnosing hyperkalemia. Quantitative assessments of T-wave amplitude corroborated subjective assessments of T-wave peaking; however, no diagnostic threshold could be established. The probability of electrocardiographic changes increased with increasing potassium. The correlation between readers was moderate. Given the poor sensitivity and specificity of electrocardiogram changes, there is no support for their use in guiding treatment of stable patients. Without identifiable electrocardiographic markers of the risk for complications, management of hyperkalemia should be guided by the clinical scenario and serial potassium measurements.

Nonlinear organization of electrocardiogram and optical signals during ventricular fibrillation

Background Although sympathetic activation may induce ventricular fibrillation (VF), little is known about how the autonomic nervous system influences its nonlinear organization. This study tested the hypothesis that autonomic receptor activation altered the nonlinear organization of VF. Methods Isolated rabbit hearts underwent retrograde perfusion with acetylcholine or norepinephrine added to the perfusate. Voltage-sensitive fluorescent images of the ventricular surface were obtained during sustained VF. Concurrent electrocardiogram and optical pixel signals underwent recurrence quantification analysis, which detects and quantifies patterns of repeating data sequences. Recurrence quantification analysis variables signify different aspects of nonlinearity. Results Recurrence quantification analysis results showed that the electrocardiogram and pixel signals did not exhibit the same pattern of nonlinear organization during VF. Recurrence quantification analysis values were not dramatically altered from baseline by acetylcholine and norepinephrine but instead exhibited considerable variation. Conclusion An alteration in autonomic milieu diminished the nonlinear organization of VF, that is, autonomic receptor activation made VF less likely to behave in a repetitive pattern over time.

Comparison of three methods for beat-to-beat-interval extraction from continuous blood pressure and electrocardiogram with respect to heart rate variability analysis / Vergleich von drei Methoden der Schlag-zu-Schlag-Intervall-Extraktion aus kontinuierlichen Blutdruckverläufen und Elektrokardiogrammen zur Herzratenvariabilitätsanalyse

In recent years the analysis of heart rate variability (HRV) has become a suitable method for characterizing autonomous cardiovascular regulation. The aim of this study was to investigate the differences in HRV estimated from continuous blood pressure (BP) measurement by different methods in comparison to electrocardiogram (ECG) signals. The beat-to-beat intervals (BBI) were simultaneously extracted from the ECG and blood pressure of 9 cardiac patients (10 min, Colin system, 1000-Hz sampling frequency). For both data types, slope, peak, and correlation detection algorithms were applied. The short-term variability was calculated using concurrent 10-min BP and ECG segments. The root mean square errors in comparison to ECG slope detection were: 1.74 ms for ECG correlation detection; 5.42 ms for ECG peak detection; 5.45 ms for BP slope detection; 5.75 ms for BP correlation detection; and 11.96 ms for BP peak detection. Our results show that the variability obtained with ECG is the most reliable. Moreover, slope detection is superior to peak detection and slightly superior to correlation detection. In particular, for ECG signals with higher frequency characteristics, peak detection often exhibits more artificial variability. Besides measurement noise, respiratory modulation and pulse transit time play an important role in determining BBI. The slope detection method applied to ECG should be preferred, because it is more robust as regards morphological changes in the signals, as well as physiological properties. As the ECG is not recorded in most animal studies, distal pulse wave measurement in combination with correlation or slope detection may be considered an acceptable alternative.

Mild Supravalvular Aortic Stenosis With Left Coronary Obstruction in a Neonate

A premature infant with a large patent ductus arteriosus and mild supravalvular aortic stenosis had evidence of severe myocardial ischemia after ductus ligation. Urgent reoperation was required to relieve severe obstruction of the left coronary orifice and the supravalvular stenosis was repaired. This unusual presentation early in infancy emphasizes that left coronary artery obstruction should be suspected even in the presence of mild supravalvular stenosis.

Electrocardiographic diagnosis of left ventricular hypertrophy: The effect of left ventricular wall thickness, size, and mass on the specific criteria for left ventricular hypertrophy

The purpose of our study was to determine the relative importance and effect of an increased left ventricle wall thickness, left ventricular diastolic diameter, and left ventricular mass (LVM) on the performance of the 4 major electrocardiogram (ECG) criteria of left ventricular hypertrophy (LVH) and to determine how these findings could be incorporated into the routine ECG interpretation of LVH. The ECG criteria of LVH that we chose to examine were voltage, repolarization abnormalities, left atrial abnormality, and ventricular conduction time. We analyzed data from 608 consecutive patients with left ventricular wall thickness of >13 mm on the echocardiogram and with a concurrent ECG. We arbitrarily divided patients into 3 groups (groups I-III) according to the calculated LVM. Group I had an LVM of <400 g; group II had an LVM from 400 to 600 g, and group III had an LVM of >600 g. We evaluated the effect of increasing LVM, wall thickness, and ventricular diameter on the performance of the 4 ECG criteria at different severity of thickness, diameter, and mass. An increase in the echocardiogram-derived LVM had significant effect on all 4 ECG criteria. As LVM progressively increased from groups I to III, the frequency of voltage criteria for LVH increased from 52% to 83%; left atrial abnormality rose from 46% to 68%; ST-T wave changes increases from 55% to 95%, and QRS prolongation significantly increased from 42% to 70%. Increased wall thickness and ventricular diameter failed to correlate with the overall ECG score or significantly influence the frequency of any of the 4 ECG criteria for LVH in patients when LVM was held relatively constant. We also demonstrated that an increasing number of criteria on the ECG are associated with a greater mean LVM.

The location of chronic pregnancy hypertension on the vasoconstriction-volume spectrum: Working hypothesis

Objective: The purpose of this study was to examine whether volume loading is a factor in chronic pregnancy hypertension. Study Design: Five patients with chronic hypertension performed home self-monitoring daily with a noninvasive pressure transducer from a fingertip. Determinants of mean arterial pressure were computed from the pressure data and a concurrent electrocardiogram. For this study blood pressure and rapid ejection time, which is an empiric indicator of arterial compliance, were evaluated during antihypertensive therapy. Data were transmitted by facsimile to the hospital. Results: One patient with hypertension who was admitted to the study at 10 weeks’ gestation became normotensive with a diuretic (furosemide) alone. Medication was discontinued at 23 weeks’ gestation. This patient remained normotensive until delivery at 41 weeks’ gestation. Blood pressure was controlled with furosemide combined with nifedipine and labetolol in the other 4 cases. Three patients were studied with concurrent blood pressure and rapid ejection time measurements made before and after antihypertensive medication. All 3 showed decreases in these measurements with medication. Conclusion: Both volume excess and vasoconstriction should be considered in the pathogenesis of chronic pregnancy hypertension. (Am J Obstet Gynecol 1999;181:137-42.)

Frequency content and sex difference of the frank lead signal-averaged ECG in a population with significant coronary artery disease: Comparison with concurrent 12-lead ECG morphology

The 12-lead electrocardiogram (ECG) and a 100-beat signal-averaged Frank lead ECG (SAECG) at a sampling rate of 1,000 Hz and with 16-bit resolution were recorded from 52 women and 256 men with significant coronary artery disease presenting for coronary artery revascularization. The QRS portion of each Frank lead was digitally filtered in four bandwidths: 0–10, 10–60, 60–150, and 150–250 Hz. The root-mean-square (RMS) voltage of each filtered signal was calculated as an absolute value and normalized as a percentage of the sum of the four filters, creating 27 variables. Three groups were formed using the presenting 12-lead ECG: N-ECG, ST-ECG, and MI-ECG. Despite variation in 12-lead morphology, concordance was dominant in the RMS values of the SAECG in sex comparisons within and between groups. There was significant sex difference of the RMS values in 6 of the 15 absolute RMS variables within the three groups. Women had no significant between-group difference, and men had a significant between-group differences in five absolute values and one normalized RMS value. P was considered significant at < .05.