12-lead System Research Articles

Abstract 4138269: The Association Between Automatically Assessed Broad P-wave and Cardiovascular Events in Patients with Cardiovascular Risks

Background: A broad P wave in electrocardiography reflects atrial remodeling and is associated with cardiovascular events. Although there are several reports on the association between a broad P wave and cardiovascular events, the association between automatically assessed P-wave duration and cardiovascular events in cardiovascular high-risk patients has not been clarified. Hypothesis: We hypothesized that a greater automatically assessed P-wave duration would be associated with cardiovascular events in patients with cardiovascular risk factors. Methods: We included 4,654 patients with access to automated electrocardiography (ECG) analysis of those in the Cardiovascular Prognostic Coupling Study in Japan (a registry of subjects with one or more cardiovascular risk factors, excluding patients with atrial fibrillation). Twelve-lead ECG was conducted, and the P-wave duration in each lead was analyzed automatically using the 12-lead ECG Analysis system (Fukuda Denshi, Tokyo). We selected the maximum P wave duration (Pmax) among the 12 leads. The primary endpoint was a composite endpoint (cardiovascular death, nonfatal myocardial infarction/stroke), and the secondary endpoint was heart failure hospitalization. We assessed two P-wave cut-offs (Pmax ≥ 140, 150 msec) in relation to cardiovascular events. Results: There were 218 patients with Pmax ≥140 ms and 123 patients with Pmax ≥150 ms. N-Terminal pro brain natriuretic peptide (NT-proBNP) was significantly higher in patients with Pmax ≥140 ms than in those with Pmax <140 ms (median 134.0 vs. 65.7 pg/mL, p<0.001). NT-ProBNP was also higher in patients with Pmax ≥150 ms than in those with Pmax <150 ms (median 192.0 vs. 66.9 pg/mL, p<0.001). The mean follow-up period was 53 ± 17 months. The primary endpoint occurred in 180 cases and the secondary endpoint in 73 cases. A broad P-wave defined as Pmax ≥140 ms was independently associated with the primary endpoint (hazard ratio [HR] 1.68, 95% confidence interval [CI] 1.04–2.70, p=0.034) but not with the secondary endpoint (HR 1.96, 95% CI 0.99–3.90, p=0.055) after adjustment for covariates including NT-proBNP. A broad P-wave defined as Pmax ≥150 ms was independently associated with both the primary endpoint (HR 2.08, 95% CI 1.06–4.09, p=0.034) and the secondary endpoint (HR 3.22, 95% CI 1.38–7.52, p=0.007). Conclusions: Automatically assessed P-wave duration of Pmax ≥ 150 ms was a significant predictor of cardiovascular events and heart failure.

Abstract 4137989: Notched P-wave by digital ECG analysis was associated with new-onset atrial fibrillation onset and ischemic stroke

Background: A bimodal P wave in lead II reflects left atrial remodeling and is described as a notched P wave. A notched P wave is usually defined as a dip over the smallest unit of electrocardiography (ECG) recording paper, 40 ms, but automated analysis of ECGs has shown that even a notch of 20 ms is associated with cardiovascular events. A notched P wave is also known to predict atrial fibrillation (AF) after catheter ablation. However, the relationship between automatically assessed notched P waves and new-onset AF and ischemic stroke in patients without documented AF has not been clarified. Hypothesis: A notched P-wave by digital ECG analysis was associated with both new-onset AF and ischemic stroke. Methods: We enrolled 4,216 subjects from the Cardiovascular Prognostic Coupling Study in Japan (Coupling Registry) who had one or more cardiovascular risk factors. Twelve-lead electrocardiography was conducted, and the peak-to-peak distance in the M shape was calculated automatically using a 12-lead ECG analysis system. We compared two definitions: P-waves defined as “notched” at the the peak-to-peak (“M shape”) distance in lead II of ≥20 ms or ≥40 ms. New-onset AF was confirmed through routine medical care as well as annual ECGs. We defined the primary endpoint as new-onset AF and the secondary endpoint as ischemic stroke. Results: The mean follow-up period was 53 ± 17 months, during which 17 AF cases developed. When a notched P-wave was defined as ≥20 ms (n = 319), it was a significant predictor of both new-onset AF (hazard ratio [HR] 2.91, 95% confidence interval [CI] 1.41–6.01, p=0.004) and ischemic stroke (HR 3.02, 95% CI 1.56–5.83, P=0.001). A notched P-wave defined as ≥40 ms (n = 63) was also a predictor of new-onset AF (HR 4.57; 95% CI:1.42–14.71, p = 0.011) and ischemic stroke (HR 3.84, 95% CI 1.20–12.29, P=0.024). Conclusions: A notched P-wave by digital ECG analysis was associated with ischemic stroke as well as the new-onset AF.

Atrial lead system for enhanced P-wave recording: A comparative study on optimal leads using gradient boosting and deep learning algorithms

Morphological characteristics of the P-wave in an Electrocardiogram (ECG) provide insights into interatrial and atrioventricular (AV) conduction pathologies. However, accurate diagnosis through visual inspection is hindered by the P-wave’s diminutive size and susceptibility to noise. Furthermore, conventional 12-lead systems often fail to capture these elusive P-waves. Consequently, our study describes a new Atrial Lead System (ALS) aimed at enhancing P-wave signal strength and ranking leads using advanced Gradient-Boosting (GB) and Deep Learning (DL) algorithms. ECG data from 75 healthy volunteers (mean age: 45 ± 17.2 years; 40 % women) were recorded using ALS and specially designed optimal bipolar leads to improve atrial activity. Employing Gradient Boosting Classifier (GBC), Extreme Gradient Boosting Classifier (XGBoost), Light Gradient Boosting Machine (LightGBM), and a synergistic One-dimensional Convolutional Neural Network (1D-CNN) with Bidirectional Long Short-Term Memory (Bi-LSTM) layers, we assessed and ranked optimal bipolar leads based on P-wave parameters, indices, and AV ratios. Notably, AL-I and AL-II exhibited significantly higher median amplitudes, RMS, and area for the recorded P-wave compared to other leads (p < 0.001). Evaluations using LightGBM, GBC, XGBoost, and 1D-CNN+Bi-LSTM models identified P-lead, AL-II, and AL-I as the top three leads, with accuracies of 0.96, 0.95, and 0.94; 0.96, 0.94, and 0.94; 0.96, 0.95, and 0.94; and 0.96, 0.94, and 0.93, respectively. These findings underscore the efficacy of ALS in elevating P-wave signal strength and AV ratios, suggesting its potential as a valuable tool for improved clinical screening and diagnosis of atrial arrhythmias.

Artificial intelligence–based electrocardiogram analysis improves atrial arrhythmia detection from a smartwatch electrocardiogram

Abstract Aims Smartwatch electrocardiograms (SW ECGs) have been identified as a non-invasive solution to assess abnormal heart rhythm, especially atrial arrhythmias (AAs) that are related to stroke risk. However, the performance of these tools is limited and could be improved with the use of deep neural network (DNN) algorithms, particularly for specific populations encountered in clinical cardiology practice. Methods and results A total of 400 patients from the electrophysiology department of one tertiary care hospital were included in two similar clinical trials (respectively, 200 patients per study). Simultaneous ECGs were recorded with the watch and a 12-lead recording system during consultation or before and after an electrophysiology procedure if any. The SW ECGs were processed by using the DNN and with the Apple watch ECG software (Apple app). Corresponding 12-lead ECGs (12L ECGs) were adjudicated by an expert electrophysiologist. The performance of the DNN was assessed vs. the expert interpretation of the 12L ECG, and inconclusive rates were reported. Overall, the DNN and the Apple app presented, respectively, a sensitivity of 91% [95% confidence interval (CI) 85–95%] and 61% (95% CI 44–75%) with a specificity of 95% (95% CI 91–97%) and 97% (95% CI 93–99%) when compared with the physician 12L ECG interpretation. The DNN was able to provide a diagnosis on 99% of ECGs, while the Apple app was able to classify only 78% of strips (22% of inconclusive diagnosis). Conclusion In this study, by including patients from a cardiology department, a DNN-based algorithm applied to an SW ECG provided an accurate diagnosis for AA detection on virtually all tracings, outperforming the SW algorithm.

Screening and diagnosis of atrial fibrillation using wearable devices.

In recent years, the development and use of various devices for the screening of atrial fibrillation (AF) have significantly increased. Such devices include 12-lead electrocardiogram (ECG), photoplethysmography systems, and single-lead ECG and ECG patches. This review outlines several studies that have focused on the feasibility and efficacy of such devices for AF screening, and summarizes the risks and benefits involved in the initiation of anticoagulant therapy after early detection of AF. We also describe several ongoing trials on unresolved issues associated with AF screening. Overall, this review provides a comprehensive summary of the current state of AF screening and its implications for patient care.

Fully Screen-Printed and Gentle-to-Skin Wet ECG Electrodes with Compact Wireless Readout for Cardiac Diagnosis and Remote Monitoring.

Recent advances in electrocardiogram (ECG) diagnosis and monitoring have triggered a demand for smart and wearable ECG electrodes and readout systems. Here, we report the development of a fully screen-printed gentle-to-skin wet ECG electrode integrated with a scaled-down printed circuit board (PCB) packaged inside a 3D-printed antenna-on-package (AoP). All three components of the wet ECG electrode (i.e., silver nanowire-based conductive part, electrode gel, and adhesive gel) are screen-printed on a flexible plastic substrate and only require 265 times less metal for the conductive part and 176 times less ECG electrode gel than the standard commercial wet ECG electrodes. In addition, our electrically small AoP achieved a maximum read range of 142 m and offers a 4 times larger wireless communication range than the typical commercial chip antenna. The adult volunteers' study results indicated that our system recorded ECG data that correlated well with data from a commercial ECG system and electrodes. Furthermore, in the context of a 12-lead ECG diagnostic system, the fully printed wet ECG electrodes demonstrated a performance similar to that of commercially available wet ECG electrodes while being gentle on the skin. This was confirmed through a blind review method by two cardiology consultants and one family medicine consultant, validating the consistency of the diagnostic information obtained from both electrodes. In conclusion, these findings highlight the potential of fully screen-printed wet ECG electrodes for both monitoring and diagnostic purposes. These electrodes could serve as potential candidates for clinical practice, and the screen-printing method has the capability to facilitate industrial mass production.

The Mobile Phone Electromagnetic Radiation Effects on Heart Rate Variability Function

Background: The proliferation of mobile phone usage has given rise to concerns about the potential health effects of electromagnetic fields (EMF), particularly in relation to heart rate variability (HRV), a key indicator of cardiac health. Previous studies have yielded inconsistent results using lower-order statistical measures, leaving a gap in understanding the nonlinear interactions between EMF exposure and HRV. Objective: This study aimed to investigate the effects of mobile phone EMF on HRV by employing bicoherence analysis of ECG data. It sought to determine whether the position of EMF exposure relative to the heart and the duration of exposure affected HRV parameters. Methods: Twenty subjects were recruited for the study, with ECG and EEG data collected under EMF and non-EMF conditions. ECG data were captured using a 12-lead system, with electrodes placed according to standard guidelines. EEG electrodes were positioned following the 10-20 system. Bicoherence and coherence analyses were conducted to assess nonlinear interactions in HRV activity and the relationship between heart and brain signals. The study also considered the duration of EMF exposure, comparing the effects of 10-minute and 40-minute sessions. Results: The bicoherence values for ECG data during EMF exposure at the left ear showed negligible differences, with values ranging between 0.0 to 0.04. However, chest positions V1 and V2 revealed statistically significant larger bicoherence values during non-EMF trials as opposed to EMF trials. Coherence analysis between ECG and EEG demonstrated significantly higher values across the 16-30Hz frequency band during EMF trials. No significant differences were observed for 10-minute EMF exposure, whereas 40-minute exposure sessions indicated a correlation with changes in HRV. Conclusion: The study's findings suggest that mobile phone EMF can affect HRV parameters, with the effects being more pronounced during longer exposure durations and when the source of EMF is closer to the heart. These results support the need for guidelines on safe mobile phone usage and further research into the effects of EMF on cardiac function.

Coronary Artery Occlusion Detection Using 3-Lead ECG System Suitable for Credit Card-Size Personal Device Integration

BackgroundEarly coronary occlusion detection by portable personal device with limited number of electrocardiographic (ECG) leads might shorten symptom-to-balloon time in acute coronary syndromes. ObjectivesThe purpose of this study was to compare the accuracy of coronary occlusion detection using vectorcardgiographic analysis of a near-orthogonal 3-lead ECG configuration suitable for credit card-size personal device integration with automated and human 12 lead ECG interpretation. MethodsThe 12-lead ECGs with 3 additional leads (“abc”) using 2 arm and 2 left parasternal electrodes were recorded in 66 patients undergoing percutaneous coronary intervention prior to (“baseline”, n = 66), immediately before (“preinflation”, n = 66), and after 90-second balloon coronary occlusion (“inflation”, n = 120). Performance of computer-measured ST-segment shift on vectorcardgiographic loops constructed from “abc” and 12 leads, standard 12-lead ECG, and consensus human interpretation in coronary occlusion detection were compared in “comparative” and “spot” modes (with/without reference to “baseline”) using areas under ROC curves (AUC), reliability, and sensitivity/specificity analysis. ResultsComparative “abc”-derived ST-segment shift was similar to two 12-lead methods (vector/traditional) in detecting balloon coronary occlusion (AUC = 0.95, 0.96, and 0.97, respectively, P = NS). Spot “abc” and 12-lead measurements (AUC = 0.72, 0.77, 0.68, respectively, P = NS) demonstrated poorer performance (P < 0.01 vs comparative measurements). Reliability analysis demonstrated comparative automated measurements in “good” agreement with reference (preinflation/inflation), while comparative human interpretation was in “moderate” range. Spot automated and human reading showed “poor” agreement. ConclusionsVectorcardiographic ST-segment analysis using baseline comparison of 3-lead ECG system suitable for credit card-size personal device integration is similar to established 12-lead ECG methods in detecting balloon coronary occlusion.

Comparison of the diagnostic value of a small, single channel, electrocardiogram monitoring patch with a standard 3-lead Holter system over 24 hours in dogs

Introduction/ObjectivesThe aim of this study was to compare a novel small event recorder device, the Carnation Ambulatory Monitor (CAM), with a standard Holter. AnimalsNineteen adult dogs. Material and methodsComparative and explorative study. The two devices were simultaneously applied for approximately 24 h. ResultsAnalysis time (P=0.013) and percentage of artefacts (P<0.001) were greater for the CAM (110 min [40–264]; and 9% [0–34], respectively) compared to a standard Holter (30 min [18–270]; and 0.3% [0–9], respectively). The total number of beats (P=0.017) and maximum (P=0.02) and mean (P=0.037) heart rates were lower for the CAM (113,806 ± 23,619 beats; 227 ± 35 bpm; and 88 ± 22 bpm, respectively) compared to the standard Holter (131,640 ± 40,037 beats; 260 ± 64 bpm; and 92 ± 26 bpm, respectively). The minimal heart rate (P=0.725), number of pauses (P=0.078), duration of the longest pause (P=0.087), number of ventricular ectopic complexes (P=0.55), ventricular couplets (P=0.186), ventricular triplets (P=0.203), ventricular tachycardia (P=0.05), Lown grade (P=0.233), presence or absence of ventricular bigeminy, trigeminy, supraventricular tachycardia, and atrial fibrillation (P=0.98) did not differ. The CAM missed some relevant events, like complex ventricular arrhythmias, and the Lown grade did not match in 5/19 dogs when comparing the devices. ConclusionsCardiac Ambulatory Monitor can be used to record ECG traces in dogs over a prolonged period, allowing to detect arrhythmias. Due to some clinically relevant limitations, including a higher percentage of artefacts, a longer reading time (which precludes quantitative counts of >300ventricular premature complexes), and underestimation of complex ventricular arrhythmias, the CAM appears not suitable for quantitative arrhythmia analysis in dogs.

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

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

A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis.

Mathematical models of cardiac electrical activity are one of the most important tools for elucidating information about heart diagnostics. In this paper, we present an efficient mathematical formulation for this modeling simple enough to be easily parameterized and rich enough to provide realistic signals. It relies on a five dipole representation of the cardiac electric source, each one associated with the well-known waves of the electrocardiogram signal. Beyond the physical basis of the model, the parameters are physiologically interpretable as they characterize the wave shape, similar to what a physician would look for in signals, thus making them very useful in diagnosis. The model accurately reproduces the electrocardiogram signals of any diseased or healthy heart. This new discovery represents a significant advance in electrocardiography research. It is especially useful for diagnosis, patient follow-up or decision-making on new therapies; is also a promising tool for well-performing, transparent and interpretable AI approaches.

Multifunctional, breathable MXene-PU mesh electronic skin for wearable intelligent 12-lead ECG monitoring system

With the advent of an aging society and increasing concern for cardiovascular health, comfortable long-term electrocardiographic (ECG) monitoring systems in daily life are much in demand. If the system can give real-time feedback on cardiovascular status and offer timely personalized advice like doctors, it will be beneficial to promote cardiovascular health and alleviate the imbalance of medical resources. Herein, we report a multifunctional, conformal, and air-permeable electronic skin (e-skin) based on MXene-Polyurethane mesh (MPM). When used as ECG electrodes, the e-skin shows ultra-low electrode–skin contact impedance (4.68 kΩ at 1 kHz), high signal-to-noise ratio (16.5 dB), and high breathability (2.1838 kg/m2/day), and can be continuously worn for at least 7 days. As a strain sensor, the e-skin exhibits a large measurement range (>40 %) and unique segmental sensitivity (GF = 172.54 in 0 ∼ 18.64 % and 79.38 in 18.64 ∼ 50 %), which is suitable for detecting both subtle and large physiological signals. More significantly, by combining the MPM e-skin with a flexible printed circuit board (FPCB) and a merged convolutional neural network & long-short-term memory (CNN & LSTM) intelligent algorithm, the acquired wearable intelligent e-skin system can realize daily 12-hour 12-lead ECG monitoring with diagnostic accuracy over 99 % for four arrhythmias. These results show that the MPM e-skin has huge potential in multifunctional intelligent healthcare systems. In particular, an intelligent ECG system similar to a “daily doctor” is proposed for daily long-term cardiovascular health monitoring.

Graphene nano-platelets polyvinyl alcohol nanocomposite electrode for real time ECG signal acquisition

This paper highlights the development of graphene nanoplatelets polyvinyl alcohol(GNP@PVA) nanocomposite electrode to acquire real time Electrocardiogram(ECG) signal. In order to create GNP@PVA nanocomposite, solution casting was used. By drop casting the GNP@PVA nanocomposite onto a commercial silver/silver chloride (Ag/AgCl) electrode with different GNP concentrations, namely 0, 0.5, 0.75, and 1 wt/wt% are created. The 3-lead ECG system is designed using Arduino microcontroller, GNP@PVA electrode and AD8232 sensor. To investigate the physical, spectral, and microcrystalline characteristics of the prepared nanocomposites, the prepared GNP@PVA electrode is subjected to various analyses, including scanning electron microscope (SEM) analysis, X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR) and Raman spectroscopy. The results demonstrated that ECG signals acquired by GNP@PVA (1%) has increased R peak value along with high sensitivity. To validate the performance and efficiency of designed GNP@PVA electrode, the acquired real time ECG signal is compared with commercial Ag/AgCl electrode. In contrast to commercial Ag/AgCl electrode, the designed GNP@PVA dry electrode has high signal to noise ratio of 21 due to high conductivity.

Comparative analysis of ECG records depending on body position in domestic swine (Sus scrofa domestica)

BackgroundElectrocardiography is a method widely applied in diagnosing abnormalities in the functioning of the heart muscle in veterinary medicine. It is a non-invasive and easy to perform test helpful in the general examination and a widely used patient monitoring method during anesthesia. Since the 1980s, pigs have become more and more popular companion animals. Moreover, the pig is a widely used model animal in biomedical research. Therefore, there is need to provide them with higher-quality veterinary services, also in emergency situations. It creates new challenges for veterinarians and the need to expand their knowledge of pigs’ treatment as pets. The aim of the planned experiment was to compare the ECG recordings made with two different body positions and determine if any differences occurred. Standard ECG in swine is performed under general anesthesia in the lying position on the left side, for this position of the body have been developed and reported standards in the literature. However, some procedures performed on swine require a different body position, for which there is less data in the literature.MethodsThe study was carried out on 29 Polish landrace pigs weighing in the range of 33–44 kg. The tests were performed under general anesthesia with the same protocol for each animal, placing the animals first lying down on their right side, and then on their backs. The anesthesia protocol included medetomidine, midazolam, ketamine, and propofol. During the examination, ECG records were performed and analyzed in a 12-lead system with software support.ResultsThe results show significant differences in electrocardiogram recordings depending on the animal's body position. Those differences mainly concern the amplitude of the P wave and R wave in the recordings and are even more visible comparing the electrocardiograms of the same specimen. There are also some significant differences in the duration of intervals. Based on the obtained results, reference ranges for the right lateral and dorsal positions were developed.ConclusionIn conclusion, the body position has a significant impact on the ECG recording in swine, therefore performing this examination, chosen normative value tables should be compatible with the position of the examined animal.

From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-versus-rest classification strategy

Objective. Detecting different cardiac diseases using a single or reduced number of leads is still challenging. This work aims to provide and validate an automated method able to classify ECG recordings. Performance using complete 12-lead systems, reduced lead sets, and single-lead ECGs is evaluated and compared. Approach. Seven different databases with 12-lead ECGs were provided during the PhysioNet/Computing in Cardiology Challenge 2021, where 88 253 annotated samples associated with none, one, or several cardiac conditions among 26 different classes were released for training, whereas 42 896 hidden samples were used for testing. After signal preprocessing, 81 features per ECG-lead were extracted, mainly based on heart rate variability, QRST patterns and spectral domain. Next, a One-versus-Rest classification approach made of independent binary classifiers for each cardiac condition was trained. This strategy allowed each ECG to be classified as belonging to none, one or several classes. For each class, a classification model among two binary supervised classifiers and one hybrid unsupervised-supervised classification system was selected. Finally, we performed a 3-fold cross-validation to assess the system’s performance. Main results. Our classifiers received scores of 0.39, 0.38, 0.39, 0.38, and 0.37 for the 12, 6, 4, 3 and 2-lead versions of the hidden test set with the Challenge evaluation metric (). Also, we obtained a mean -score of 0.80, 0.78, 0.79, 0.79, 0.77 and 0.74 for the 12, 6, 4, 3, 2 and 1-lead subsets with the public training set during our 3-fold cross-validation. Significance. We proposed and tested a machine learning approach focused on flexibility for identifying multiple cardiac conditions using one or more ECG leads. Our minimal-lead approach may be beneficial for novel portable or wearable ECG devices used as screening tools, as it can also detect multiple and concurrent cardiac conditions.

Review of Processing Pathological Vectorcardiographic Records for the Detection of Heart Disease.

Vectorcardiography (VCG) is another useful method that provides us with useful spatial information about the electrical activity of the heart. The use of vectorcardiography in clinical practice is not common nowadays, mainly due to the well-established 12-lead ECG system. However, VCG leads can be derived from standard 12-lead ECG systems using mathematical transformations. These derived or directly measured VCG records have proven to be a useful tool for diagnosing various heart diseases such as myocardial infarction, ventricular hypertrophy, myocardial scars, long QT syndrome, etc., where standard ECG does not achieve reliable accuracy within automated detection. With the development of computer technology in recent years, vectorcardiography is beginning to come to the forefront again. In this review we highlight the analysis of VCG records within the extraction of functional parameters for the detection of heart disease. We focus on methods of processing VCG functionalities and their use in given pathologies. Improving or combining current or developing new advanced signal processing methods can contribute to better and earlier detection of heart disease. We also focus on the most commonly used methods to derive a VCG from 12-lead ECG.

Notched P-Wave on Digital Electrocardiogram Predicts Cardiovascular Events in Patients with Cardiovascular Risks: The Japan Morning Surge Home Blood Pressure Study

Background: The relationship between notched P-wave characteristics on digital electrocardiogram (ECG) and long-term cardiovascular events remains unclear. Methods: We enrolled 810 subjects from the Japan Morning Surge Home Blood Pressure study who had one or more cardiovascular risk factors. Twelve-lead electrocardiography was conducted, and the peak-to-peak distance in the M shape was calculated automatically using a 12-lead ECG analysis system. We compared two definitions: P-waves were defined as “notched” if the peak-to-peak distance in the M shape was ≥20 ms or ≥40 ms in lead II. We assessed the left atrial diameter and left ventricular mass index (LVMI) by echocardiography. The primary endpoint was defined as a composite endpoint that combines fatal events (stroke, heart failure, coronary artery disease, and sudden death) and nonfatal events (acute myocardial infarction, angina, congestive heart failure, stroke, and aortic dissection). Results: The mean follow-up period was 101 ± 34 months, during which 85 cardiovascular events occurred. When we defined a notched P-wave as ≥20 ms in the M shape (n = 92), a notched P-wave was a significant predictor of cardiovascular events after adjustment for age, gender, and comorbidity (hazard ratio: 1.83; 95% confidence interval: 1.01–3.31, p = 0.045). When we defined a notched P-wave as ≥40 ms in the M shape (n = 25), the hazard ratio of cardiovascular events in the notched P-wave group was not significant after adjustment for covariates (hazard ratio: 1.52; 95% confidence interval: 0.51–4.53, p = 0.455). The left atrial diameter and LVMI in the patients in the notched P-wave group (peak-to-peak distance of ≥20 ms in the M shape) were significantly higher than those in the control group (left atrial diameter: 38.8 ± 5.9 vs. 36.8 ± 5.0 mm, p = 0.001; LVMI: 103.9 ± 27.7 vs. 96.3 ± 25.7 g/m2, p = 0.010). Conclusions: The notched P-wave by digital ECG analysis was associated with cardiovascular events and left atrial enlargement.

Lead Reconstruction Using Artificial Neural Networks for Ambulatory ECG Acquisition.

One of the most powerful techniques to diagnose cardiovascular diseases is to analyze the electrocardiogram (ECG). To increase diagnostic sensitivity, the ECG might need to be acquired using an ambulatory system, as symptoms may occur during a patient’s daily life. In this paper, we propose using an ambulatory ECG (aECG) recording device with a low number of leads and then estimating the views that would have been obtained with a standard ECG location, reconstructing the complete Standard 12-Lead System, the most widely used system for diagnosis by cardiologists. Four approaches have been explored, including Linear Regression with ECG segmentation and Artificial Neural Networks (ANN). The best reconstruction algorithm is based on ANN, which reconstructs the actual ECG signal with high precision, as the results bring a high accuracy (RMS Error < 13 and CC > 99.7%) for the set of patients analyzed in this paper. This study supports the hypothesis that it is possible to reconstruct the Standard 12-Lead System using an aECG recording device with less leads.

Design of a Wireless Single Arm Electrocardiograph System

Electrocardiograms (ECGs) are a method of assessment of cardiac electrical activity. It is the heart’s electrophysiological activity which occurs by the conduction of electrical impulses from the Sinoatrial (SA) node and the Atrioventricular (AV) node across the cardiac muscles and is displayed as a voltage-time graph. Conventionally, they have been acquired using a system of ten surface electrodes placed in different locations throughout the body in a 12-lead system. Single Arm ECG systems, where the ECG rhythm is obtained from only a single part of the body, replace the need for ten different electrodes to detect the heart's activity. Three pre-gelled electrodes are applied in different locations of the left arm to derive the sinus rhythm signal of a subject. An op-amp based hardware instrument has been developed for this purpose with active filters and amplifiers to perform signal conditioning. This paper demonstrates the implementation of a single arm hardware system of small size to improve portability, used as an ECG detection method and the results from various subjects taken at different gain values to improve the view of the signal, while also presenting a method to transmit the data of the signals wirelessly through an Internet-of-things (IoT) platform.

ECG Localization Method Based on Volume Conductor Model and Kalman Filtering.

The 12-lead electrocardiogram was invented more than 100 years ago and is still used as an essential tool in the early detection of heart disease. By estimating the time-varying source of the electrical activity from the potential changes, several types of heart disease can be noninvasively identified. However, most previous studies are based on signal processing, and thus an approach that includes physics modeling would be helpful for source localization problems. This study proposes a localization method for cardiac sources by combining an electrical analysis with a volume conductor model of the human body as a forward problem and a sparse reconstruction method as an inverse problem. Our formulation estimates not only the current source location but also the current direction. For a 12-lead electrocardiogram system, a sensitivity analysis of the localization to cardiac volume, tilted angle, and model inhomogeneity was evaluated. Finally, the estimated source location is corrected by Kalman filter, considering the estimated electrocardiogram source as time-sequence data. For a high signal-to-noise ratio (greater than 20 dB), the dominant error sources were the model inhomogeneity, which is mainly attributable to the high conductivity of the blood in the heart. The average localization error of the electric dipole sources in the heart was 12.6 mm, which is comparable to that in previous studies, where a less detailed anatomical structure was considered. A time-series source localization with Kalman filtering indicated that source mislocalization could be compensated, suggesting the effectiveness of the source estimation using the current direction and location simultaneously. For the electrocardiogram R-wave, the mean distance error was reduced to less than 7.3 mm using the proposed method. Considering the physical properties of the human body with Kalman filtering enables highly accurate estimation of the cardiac electric signal source location and direction. This proposal is also applicable to electrode configuration, such as ECG sensing systems.