Reconstruction of the 12-lead electrocardiogram from reduced lead sets

Wearable 12-Lead ECG Acquisition Using a Novel Deep Learning Approach from Frank or EASI Leads with Clinical Validation

The 12-lead electrocardiogram (ECG) is crucial in assessing patient decisions. However, portable ECG devices capable of acquiring a complete 12-lead ECG are scarce. For the first time, a deep learning-based method is proposed to reconstruct the 12-lead ECG from Frank leads (VX, VY, and VZ) or EASI leads (VES, VAS, and VAI). The innovative ECG reconstruction network called M2Eformer is composed of a 2D-ECGblock and a ProbDecoder module. The 2D-ECGblock module adaptively segments EASI leads into multi-periods based on frequency energy, transforming the 1D time series into a 2D tensor representing within-cycle and between-cycle variations. The ProbDecoder module aims to extract Probsparse self-attention and achieve one-step output for the target leads. Experimental results from comparing recorded and reconstructed 12-lead ECG using Frank leads indicate that M2Eformer outperforms traditional ECG reconstruction methods on a public database. In this study, a self-constructed database (10 healthy individuals + 15 patients) was utilized for the clinical diagnostic validation of ECG reconstructed from EASI leads. Subsequently, both the ECG reconstructed using EASI and the recorded 12-lead ECG were subjected to a double-blind diagnostic experiment conducted by three cardiologists. The overall diagnostic consensus among three cardiology experts, reaching a rate of 96%, indicates the significant utility of EASI-reconstructed 12-lead ECG in facilitating the diagnosis of cardiac conditions.

Completing the Cabrera Circle: deriving adaptable leads from ECG limb leads by combining constraints with a correction factor

Objective.We present a concept for processing 6-lead electrocardiography (ECG) signals which can be applied to various use cases in quantitative electrocardiography.Approach.Our work builds upon the mathematics of the well-known Cabrera sequence which is a re-sorting of the six limb leads (I,II,III,aVR,aVL,aVF) into a clockwise and physiologically-interpretable order. By deriving correction factors for harmonizing lead strengths and choosing an appropriate basis for the leads, we extend this concept towards what we call the 'Cabrera Circle' based on a mathematically sound foundation.Main results.To demonstrate the practical effectiveness and relevance of this concept, we analyze its suitability for deriving interpolated leads between the six limb leads and a 'radial' lead which both can be useful for specific use cases. We focus on the use cases of i) determination of the electrical heart axis by proposing a novel interactive tool for reconstructing the heart's vector loop and ii) improving accuracy in time of automatic R-wave detection and T-wave delineation in 6-lead ECG. For the first use case, we derive an equation which allows projections of the 2-dimensional vector loops to arbitrary angles of the Cabrera Circle. For the second use case, we apply several state-of-the-art algorithms to a freely-available 12-lead dataset (Lobachevsky University Database). Out-of-the-box results show that the derived radial lead outperforms the other limb leads (I,II,III,aVR,aVL,aVF) by improving F1 scores of R-peak and T-peak detection by 0.61 and 2.12, respectively. Results of on- and offset computations are also improved but on a smaller scale.Significance.In summary, the Cabrera Circle offers a methodology that might be useful for quantitative electrocardiography of the 6-lead subsystem-especially in the digital age.

Optimization of a 12-Lead Electrocardiography Subset for Automated Early Left Ventricular Activation Localization Approach Based on Pace-Mapping Technology

BACKGROUND

We previously developed an automated approach based on pace mapping to localise early left ventricular (LV) activation origin. To avoid a singular system, we require pacing from at least 2 more known sites than the number of electrocardiography (ECG) leads used. Fewer leads used means fewer pacing sites required. We sought to identify an optimal minimal ECG lead set for the automated approach.

METHODS

We used 1715 LV endocardial pacing sites to create derivation and testing data sets. The derivation data set, consisting of 1012 known pacing sites pooled from 38 patients, was used to identify an optimal 3-lead set by means of random forest regression (RFR), and a second 3-lead set by means of exhaustive search. The performance of these sets and the calculated Frank leads was compared within the testing data set with 703 pacing sites pooled from 25 patients.

RESULTS

The RFR yielded III, V1, and V4, whereas the exhaustive search identified leads II, V2 and V6. Comparison of these sets and the calculated Frank leads demonstrated similar performance when using 5 or more known pacing sites. Accuracy improved with additional pacing sites, achieving mean accuracy of < 5 mm, after including up to 9 pacing sites when they were focused on a suspected area of ventricular activation origin (radius < 10 mm).

CONCLUSIONS

The RFR identified the quasi-orthogonal leads set to localise the source of LV activation, minimizing the training set of pacing sites. Localization accuracy was high with the use of these leads and was not significantly different from using leads identified by exhaustive search or empiric use of Frank leads.

Myocardial infarction detection using ITD, DWT and deterministic learning based on ECG signals

Nowadays, cardiovascular diseases (CVD) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention issue. Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgement and several electrocardiographic (ECG) or vectorcardiographic (VCG) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study, we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector, which is decomposed into a series of proper rotation components (PRCs) by using the intrinsic time-scale decomposition (ITD) method. The novel cardiac vector may fully reflect the pathological alterations provoked by MI and may be correlated to the disparity of cardiac system dynamics between healthy and MI subjects. ITD is employed to measure the variability of cardiac vector and the first PRCs are extracted as predominant PRCs which contain most of the cardiac vector's energy. Second, four levels discrete wavelet transform with third-order Daubechies (db3) wavelet function is employed to decompose the predominant PRCs into different frequency bands, which combines with three-dimensional phase space reconstruction to derive features. The properties associated with the cardiac system dynamics are preserved. Since the frequency components above 40 Hz are lack of use in ECG analysis, in order to reduce the feature dimension, the advisable sub-band (D4) is selected for feature acquisition. Third, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the tenfold cross-validation style, the achieved average classification accuracy is reported to be 98.20%. Results verify the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.

Electrocardiogram lead conversion from single-lead blindly-segmented signals

BACKGROUND

The standard configuration's set of twelve electrocardiogram (ECG) leads is optimal for the medical diagnosis of diverse cardiac conditions. However, it requires ten electrodes on the patient's limbs and chest, which is uncomfortable and cumbersome. Interlead conversion methods can reconstruct missing leads and enable more comfortable acquisitions, including in wearable devices, while still allowing for adequate diagnoses. Currently, methodologies for interlead ECG conversion either require multiple reference (input) leads and/or require input signals to be temporally aligned considering the ECG landmarks.

METHODS

Unlike the methods in the literature, this paper studies the possibility of converting ECG signals into all twelve standard configuration leads using signal segments from only one reference lead, without temporal alignment (blindly-segmented). The proposed methodology is based on a deep learning encoder-decoder U-Net architecture, which is compared with adaptations based on convolutional autoencoders and label refinement networks. Moreover, the method is explored for conversion with one single shared encoder or multiple individual encoders for each lead.

RESULTS

Despite the more challenging settings, the proposed methodology was able to attain state-of-the-art level performance in multiple target leads, and both lead I and lead II seem especially suitable to convert certain sets of leads. In cross-database tests, the methodology offered promising results despite acquisition setup differences. Furthermore, results show that the presence of medical conditions does not have a considerable effect on the method's performance.

CONCLUSIONS

This study shows the feasibility of converting ECG signals using single-lead blindly-segmented inputs. Although the results are promising, further efforts should be devoted towards the improvement of the methodologies, especially the robustness to diverse acquisition setups, in order to be applicable to cardiac health monitoring in wearable devices and less obtrusive clinical scenarios.

Efficient on-site confirmatory testing for atrial fibrillation with derived 12-lead ECG in a wireless body area network

Smartphones that can support and assist the screening of various cardiovascular diseases are gaining popularity in recent years. The timely detection, diagnosis, and treatment of atrial fibrillation (AF) are critical, especially for those who are at risk of stroke. AF detection via screening with wearable devices should always be confirmed by a standard 12-lead electrocardiogram (ECG). However, the inability to perform on-site AF confirmatory testing results in increased patient anxiety, followed by unnecessary diagnostic procedures and treatments. Also, the delay in confirmation procedure may conclude the condition as non-AF while it was indeed present at the time of screening. To overcome these challenges, we propose an efficient on-site confirmatory testing for AF with 12-lead ECG derived from the reduced lead set (RLS) in a wireless body area network (WBAN) environment. The reduction in the number of leads enhances the comfort level of patients as well as minimizes the hurdles associated with continuous telemonitoring applications such as data transmission, storage, and bandwidth of the overall system. The proposed method is characterized by segment-wise regression and a lead selection algorithm, facilitating improved P-wave reconstruction. Further, an efficient AF detection algorithm is proposed by incorporating a novel three-level P-wave evidence score with an RR irregularity evidence score. The proposed on-site AF confirmation test reduces false positives and false negatives by 88% and 53% respectively, compared to single lead screening. In addition, the proposed lead derivation method improves accuracy, F 1 -score, and Matthews correlation coefficient (MCC) for the on-site AF detection compared to existing related methods.

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 μV 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.

Automatic Classification of Myocardial Infarction Using Spline Representation of Single-Lead Derived Vectorcardiography

Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases worldwide and most patients suffer from MI without awareness. Therefore, early diagnosis and timely treatment are crucial to guarantee the life safety of MI patients. Most wearable monitoring devices only provide single-lead electrocardiography (ECG), which represents a major limitation for their applicability in diagnosis of MI. Incorporating the derived vectorcardiography (VCG) techniques can help monitor the three-dimensional electrical activities of human hearts. This study presents a patient-specific reconstruction method based on long short-term memory (LSTM) network to exploit both intra- and inter-lead correlations of ECG signals. MI-induced changes in the morphological and temporal wave features are extracted from the derived VCG using spline approximation. After the feature extraction, a classifier based on multilayer perceptron network is used for MI classification. Experiments on PTB diagnostic database demonstrate that the proposed system achieved satisfactory performance to differentiating MI patients from healthy subjects and to localizing the infarcted area.

Reconstruction of 12-Lead Electrocardiogram from a Three-Lead Patch-Type Device Using a LSTM Network

Reconstructing a standard 12-lead electrocardiogram (ECG) from signals received from electrodes packed into a patch-type device is a challenging task in the field of medical instrumentation. All attempts to obtain a clinically valid 12-lead ECG using a patch-type device were not satisfactory. In this study, we designed the hardware for a three-lead patch-type ECG device and employed a long short-term memory (LSTM) network that can overcome the limitations of the linear regression algorithm used for ECG reconstruction. The LSTM network can overcome the issue of reduced horizontal components of the vector in the electric signal obtained from the patch-type device attached to the anterior chest. The reconstructed 12-lead ECG that uses the LSTM network was tested against a standard 12-lead ECG in 30 healthy subjects and ECGs of 30 patients with pathologic findings. The average correlation coefficient of the LSTM network was found to be 0.95. The ability of the reconstructed ECG to detect pathologic abnormalities was identical to that of the standard ECG. In conclusion, the reconstruction of a standard 12-lead ECG using a three-lead patch-type device is feasible, and such an ECG is an equivalent alternative to a standard 12-lead ECG.

Classification of myocardial infarction based on hybrid feature extraction and artificial intelligence tools by adopting tunable-Q wavelet transform (TQWT), variational mode decomposition (VMD) and neural networks

Cardiovascular diseases (CVD) is the leading cause of human mortality and morbidity around the world, in which myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. Currently, electrocardiogram (ECG) is widely used by the clinicians to diagnose MI patients due to its inexpensiveness and non-invasive nature. Pathological alterations provoked by MI cause slow conduction by increasing axial resistance on coupling between cells. This issue may cause abnormal patterns in the dynamics of the tip of the cardiac vector in the ECG signals. However, manual interpretation of the pathological alternations induced by MI is a time-consuming, tedious and subjective task. To overcome such disadvantages, computer-aided diagnosis techniques including signal processing and artificial intelligence tools have been developed. In this study we propose a novel technique for automatic detection of MI based on hybrid feature extraction and artificial intelligence tools. Tunable quality factor (Q-factor) wavelet transform (TQWT), variational mode decomposition (VMD) and phase space reconstruction (PSR) are utilized to extract representative features to form cardiac vectors with synthesis of the standard 12-lead and Frank XYZ leads. They are combined with neural networks to model, identify and detect abnormal patterns in the dynamics of cardiac system caused by MI. First, 12-lead ECG signals are reduced to 3-dimensional VCG signals, which are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector. Second, this vector is decomposed into a set of frequency subbands with a number of decomposition levels by using the TQWT method. Third, VMD is employed to decompose the subband of the 4-dimensional cardiac vector into different intrinsic modes, in which the first intrinsic mode contains the majority of the cardiac vector's energy and is considered to be the predominant intrinsic mode. It is selected to construct the reference variable for analysis. Fourth, phase space of the reference variable is reconstructed, in which the properties associated with the nonlinear cardiac system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in cardiac system dynamics between normal (healthy) and MI cardiac vector signals. Fifth, cardiac system dynamics can be modeled and identified using neural networks, which employ the ED of 3D PSR of the reference variable as the input features. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, data sets, which include conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls from PTB diagnostic ECG database, are used for evaluation. By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 97.98%. Currently, ST segment evaluation is one of the major and traditional ways for the MI detection. However, there exist weak or even undetectable ST segments in many ECG signals. Since the proposed method does not rely on the information of ST waves, it can serve as a complementary MI detection algorithm in the intensive care unit (ICU) of hospitals to assist the clinicians in confirming their diagnosis. Overall, our results verify that the proposed features may satisfactorily reflect cardiac system dynamics, and are complementary to the existing ECG features for automatic cardiac function analysis.

A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG)

Cardiovascular diseases (CVDs) are the number one cause of death globally. An estimated 17.9 million people die from CVDs each year, representing 31% of all global deaths. Most cardiac patients require early detection and treatment. Therefore, many products to monitor patient's heart conditions have been introduced on the market. Most of these devices can record a patient's bio-metric signals both in resting and in exercising situations. However, reading the massive amount of raw electrocardiogram (ECG) signals from the sensors is very time-consuming. Automatic anomaly detection for the ECG signals could act as an assistant for doctors to diagnose a cardiac condition. This paper reviews the current state-of-the-art of this technology discusses the pros and cons of the devices and algorithms found in the literature and the possible research directions to develop the next generation of ambulatory monitoring systems.

Optimal Lead Position in Patch-Type Monitoring Sensors for Reconstructing 12-Lead ECG Signals with Universal Transformation Coefficient

The aim of this study was to reconstruct a 12-lead electrocardiograph (ECG) with a universal transformation coefficient and find the appropriate electrode position and shape for designing a patch-type ECG sensor. A 35-channel ECG monitoring system was developed, and 14 subjects were recruited for the experiment. A feedforward neural network with one hidden layer was applied to train the transformation coefficient. Three electrode shapes (5 cm × 5 cm square, 10 cm × 10 cm square, and right-angled triangle) were considered for the patch-type ECG sensor. The mean correlation coefficient (CC) and minimum CC methods were applied to evaluate the reconstruction performance. The average CCs between the standard 12-lead ECG and reconstructed 12-lead ECG were 0.860, 0.893, and 0.893 for a 5 cm × 5 cm square, 10 cm × 10 cm square, and right-angled triangle shape. The right-angled triangle showed the highest performance among the considered shapes. The results also suggested that the bottom of the central area of the chest was the most suitable position for attaching the patch-type ECG sensor.

A lightweight piecewise linear synthesis method for standard 12-lead ECG signals based on adaptive region segmentation

This paper presents a lightweight synthesis algorithm, named adaptive region segmentation based piecewise linear (ARSPL) algorithm, for reconstructing standard 12-lead electrocardiogram (ECG) signals from a 3-lead subset (I, II and V2). Such a lightweight algorithm is particularly suitable for healthcare mobile devices with limited resources for computing, communication and data storage. After detection of R-peaks, the ECGs are segmented by cardiac cycles. Each cycle is further divided into four regions according to different cardiac electrical activity stages. A personalized linear regression algorithm is then applied to these regions respectively for improved ECG synthesis. The proposed ARSPL method has been tested on 39 subjects randomly selected from the PTB diagnostic ECG database and achieved accurate synthesis of remaining leads with an average correlation coefficient of 0.947, an average root-mean-square error of 55.4μV, and an average runtime performance of 114ms. Overall, these results are significantly better than those of the common linear regression method, the back propagation (BP) neural network and the BP optimized using the genetic algorithm. We have also used the reconstructed ECG signals to evaluate the denivelation of ST segment, which is a potential symptom of intrinsic myocardial disease. After ARSPL, only 10.71% of the synthesized ECG cycles are with a ST-level synthesis error larger than 0.1mV, which is also better than those of the three above-mentioned methods.

Reconstruction of 12-lead ECG Using a Single-patch Device

Objectives: The aim of this study is to develop an optimal electrode system in the form of a small and wearable single-patch ECG monitoring device that allows for the faithful reconstruction of the standard 12-lead ECG.

Methods: The optimized universal electrode positions on the chest and the personalized transformation matrix were determined using linear regression as well as artificial neural networks (ANNs). A total of 24 combinations of 4 neighboring electrodes on 35 channels were evaluated on 19 subjects. Moreover, we analyzed combinations of three electrodes within the four-electrode combination with the best performance.

Results: The mean correlation coefficients were all higher than 0.95 in the case of the ANN method for the combinations of four neighboring electrodes. The reconstructions obtained using the three and four sensing electrodes showed no significant differences. The reconstructed 12-lead ECG obtained using the ANN method is better than that using the MLR method. Therefore, three sensing electrodes and one ground electrode (forming a square) placed below the clavicle on the left were determined to be suitable for ensuring good reconstruction performance.

Conclusions: Since the interelectrode distance was determined to be 5 cm, the suggested approach can be implemented in a single-patch device, which should allow for the continuous monitoring of the standard 12-lead ECG without requiring limb contact, both in daily life and in clinical practice.

Continuous ECG Monitoring in Patients With Acute Coronary Syndrome or Heart Failure: EASI Versus Gold Standard

The purpose of the study was to compare the EASI system with the standard 12-lead surface electrocardiogram (ECG) for the accuracy in detecting the main electrocardiographic parameters (J point, PR, QT, and QRS) commonly monitored in patients with acute coronary syndromes or heart failure. In this observational comparative study, 253 patients who were consecutively admitted to the coronary care unit with acute coronary syndrome or heart failure were evaluated. In all patients, two complete 12-lead ECGs were acquired simultaneously. A total of 6,072 electrocardiographic leads were compared (3,036 standard and 3,036 EASI). No significant differences were found between the investigate parameters of the two measurement methods, either in patients with acute coronary syndrome or in those with heart failure. This study confirmed the accuracy of the EASI system in monitoring the main ECG parameters in patients admitted to the coronary care unit with acute coronary syndrome or heart failure.

Reconstruction of an 8-lead surface ECG from two subcutaneous ICD vectors

INTRODUCTION

Techniques exist which allow surface ECGs to be reconstructed from reduced lead sets. We aimed to reconstruct an 8-lead ECG from two independent S-ICD sensing electrodes vectors as proof of this principle.

METHODS

Participants with ICDs (N=61) underwent 3minute ECGs using a TMSi Porti7 multi-channel signal recorder (TMS international, The Netherlands) with electrodes in the standard S-ICD and 12-lead positions. Participants were randomised to either a training (N=31) or validation (N=30) group. The transformation used was a linear combination of the 2 independent S-ICD vectors to each of the 8 independent leads of the 12-lead ECG, with coefficients selected that minimized the root mean square error (RMSE) between recorded and derived ECGs when applied to the training group. The transformation was then applied to the validation group and agreement between the recorded and derived lead pairs was measured by Pearson correlation coefficient (r) and normalised RMSE (NRMSE).

RESULTS

In total, 27 patients with complete data sets were included in the validation set consisting of 57,888 data points from 216 full lead sets. The distribution of the r and NRMSE were skewed. Mean r=0.770 (SE 0.024), median r=0.925. NRMSE mean=0.233 (SE 0.015) median=0.171.

CONCLUSIONS

We have demonstrated that the reconstruction of an 8-lead ECG from two S-ICD vectors is possible. If perfected, the ability to generate accurate multi-lead surface ECG data from an S-ICD would potentially allow recording and review of clinical arrhythmias at follow-up.

ECG monitoring leads and special leads

ECG monitoring is common place in the hospital and even pre-hospital setting. The need for different types of lead systems in different settings has been emphasised. Simple three electrode bipolar recording is ubiquitous for monitoring. This can be used to record modified bipolar chest leads as well. Using five leads gives the option of getting a chest lead in addition to bipolar limb leads, enhancing detection of ischemia during procedures. Lead stability is important when the movement of the subject is maximum as in exercise testing. Mason-Likar modification with limb leads shifted to the torso is popular for exercise testing, though the diagnostic value of the ECG is altered. Lund system with leads on proximal part of limbs have both stability and fair diagnostic value. EASI lead system permits derivation of 12 leads from just five electrodes. Lewis lead and the newly devised modified limb lead system are useful in enhancing detection of atrial activity. Fontaine lead has been designed to improve visualization of Epsilon wave in arrhythmogenic right ventricular dysplasia.

Lead transformations and the dipole approximation: Practical applications

The transformation of recorded electrocardiographic leads (source leads) into leads that are wanted but were not recorded (target leads) has many practical applications. In general, two transformation methods are put to use, a purely statistical one and a model-based one. They are briefly reviewed and compared. Lead transformations were first used in the early nineteen-sixties to transform the component leads of one vectorcardiographic lead system into those of another. Since then, the use of lead transformations has proliferated and they are currently applied for a variety of purposes. Lead transformations can be grouped according to the source and target leads that are involved. A few applications of lead transformations from the different groups are presented, with a focus on the practicality of the application. The validity and value of the dipole approximation in relation to lead transformations is discussed.

Reconstruction of Precordial Lead Electrocardiogram From Limb Leads Using the State-Space Model

A new electrocardiogram (ECG) reconstruction method based on a state-space model is presented. This method was applied to reconstruct precordial leads from limb leads (lead I, II, III) for its validity verification. The system matrices of the state-space model were estimated at the model estimation stage by considering the limb lead signals as the input of the system and precordial lead signals as the output. To evaluate the performance of the proposed method, all of the 549 records of the Physikalisch Technische Bundesanstalt diagnostic ECG database were used, and the correlation coefficients (CC) and root-mean-square errors between reconstructed ECG and measured ECG were calculated. For a more objective evaluation, the results were compared with those of linear regression model that has been typically used for ECG reconstruction. The mean and median values of CCs were higher than 0.988 and 0.995, respectively, for healthy subject data, and also higher than 0.981 and 0.993, respectively, for cardiac patient data and comparable to those by linear regression model. In addition, it was found that the reconstruction performance depended on the type of disease rather than lead type. Among cardiac patient data, hypertrophy, myocarditis, valvular heart disease, and stable heart angina showed higher CC (>0.990), while unstable angina and heart failure showed lower CC of 0.932 and 0.914, respectively. Moreover, when ECG contaminated with the noise was used for reconstruction, the proposed method demonstrated better performance than linear regression model in general.

Detection of acute myocardial ischemic injury by gender using a novel cardiac electrical biomarker

OBJECTIVE

The objective of this study us to stratify by gender a new cardiac electrical biomarker (CEB) diagnostic accuracy for detection of acute myocardial ischemic injury (AMII).

METHODS

This is a noninferiority retrospective, case-control, blinded study of 310 archived measured electrocardiograms (ECGs) acquired from 218 men and 92 women. The CEB is constructed from the derived ECG (dECG) synthesized from 3 leads. Electrocardiograms were included if acquired less than or equal to 1 day from patient presentation. Electrocardiograms were interpreted by 2 blinded physicians and adjudicated by consensus. Standard ST analyses and computerized ECG interpretations were active controls. Electrocardiograms were excluded for noise and baseline wander, age younger than 18 years, and ectopic beats in the 10-second ECG acquisition. Diagnostic accuracy measures of sensitivity, specificity, positive and negative predictive values, and likelihood ratios were stratified by gender. Measured vs derived ECG correlations were quantitatively compared using Pearson correlation and qualitatively by percent agreement methodology.

RESULTS

The CEB sensitivities for AMII detection in men and women were 93.9% and 90.5%, respectively, and CEB specificities were 90.7% and 95.2%, respectively, and were superior to active controls. Derived and measured ECGs showed high correlation for both men and women with r = 0.857 and r = 0.893, respectively. Reference standard intra-agreement analysis for measured ECGs and dECGs with AMII was 99.4%.

CONCLUSIONS

The CEB demonstrates high diagnostic accuracy for detection of AMII in men and women. The ECG can be derived with accuracy from 3 leads. This technology is an efficient real-time method of identifying patients with AMII who are being monitored in acute care settings.

Patient-specific 12-lead ECG reconstruction from sparse electrodes using independent component analysis

We propose and evaluate a method of 12-lead electrocardiogram (ECG) reconstruction from a three-lead set. The method makes use of independent component analysis and results in adaptive patient-specific transforms. The required calibration process is short and makes use of a single beat. We apply the method to two sets of leads: leads I, II, V2 and the Frank XYZ leads. Performance is evaluated via percent correlation calculations between reconstructed and original leads from a publicly available database of 549 ECG recordings. Results depict percent correlation exceeding 96% for almost all leads. Adaptability of the method's transform is shown to compensate for changes in signal propagation conditions due to breathing, resulting in reduced variance of reconstruction accuracy across beats. This implies that the method is robust to changes that occur after the time of calibration. Accurate and adaptive reconstruction has the potential to augment the clinical significance of wireless ECG systems since the number of sensor nodes placed on the body is limited and the subject could be mobile.

The effect of precordial lead displacement on ECG morphology

Inaccurate electrode placement and differences in inter-individual human anatomies can lead to misinterpretation of ECG examination. The aim of the study was to investigate the effect of precordial electrodes displacement on morphology of the ECG signal in a group of 60 patients with diagnosed cardiac disease. Shapes of ECG signals recorded from precordial leads were compared with signals interpolated at the points located at a distance up to 5 cm from lead location. Shape differences of the QRS and ST-T-U complexes were quantified using the distribution function method, correlation coefficient, root-mean-square error (RMSE), and normalized RMSE. The relative variability (RV) index was calculated to quantify inter-individual variability. ECG morphology changes were prominent in all shape parameters beyond 2 cm distance to precordial leads. Lead V2 was the most sensitive to displacement errors, followed by leads V3, V1, and V4, for which the direction of electrodes displacement plays a key role. No visible changes in ECG morphology were observed in leads V5 and V6, only scaling effect of signal amplitude. The RV ranged from 0.639 to 0.989. Distortions in ECG tracings increase with the distance from precordial lead, which are specific to chosen electrode, direction of displacement, and for ECG segment selected for calculations.

Estimating the Universal Positions of Wireless Body Electrodes for Measuring Cardiac Electrical Activity

A methodology is presented for estimating the wireless body electrode (WE) positions and for calculating the linear transformations that enable the synthesis of a 12-lead ECG or a multichannel ECG from three WEs, which in turn simplifies and improves the acquisition of ECGs. We present, compare, and evaluate three approaches to the synthesis: fully personalized, fully universal, and combined with universal leads and personalized transformations. The evaluation results show that WEs are an acceptable alternative to the standard 12-lead ECG device for patients with chronic myocardial ischemia, if either the fully personalized or combined approach is used. The median correlation coefficients are all higher than 0.94 and 0.92 for the fully personalized and combined approaches, respectively. The corresponding kappa and percentual diagnostic agreements between the synthesized and target 12-lead ECGs are 0.88 (95%) and 0.83 (92%), respectively. The evaluation additionally shows that the personalization of the transformations has more impact on the quality of the synthesized ECGs than the personalization of the WEs' positions.

Derivation of the 12-lead electrocardiogram and 3-lead vectorcardiogram

OBJECTIVE

The cardiac dipolar field is represented by the measured 12-lead electrocardiogram (ECG) and 3-lead vectorcardiogram (VCG). The objective is to derive the 12-lead ECG and 3-lead VCG from 3 measured leads acquired from only 5 electrodes.

METHODS

This is a retrospective blinded study comparing measured and derived ECG and VCG tracings. A nonlinear optimization model was used to synthesize the derived 12-lead ECG and 3-lead derived VCG from leads I, II, and V2. A total of 367 measured 12-lead electrocardiograms and 3-lead vectorcardiograms of varying morphologies were acquired from archived digital ECG databases. All tracings were interpreted by 2 blinded physician reference standards. The derived vs measured tracings were compared quantitatively using Pearson correlation and root mean square error. Qualitative comparisons were determined by physician percent agreement analysis and adjudication.

RESULTS

The correlations between the measured and derived ECGs and VCGs were high (r=0.867). No clinically significant differences were noted in 98.1% of cases. Electrocardiographic rate, rhythm, segment, axis, and acute myocardial infarction interpretations showed 100% correlation. Root mean square error compared favorably against other synthesis techniques. Overall percent agreements for the various ECG morphologies were noted to be 98.4% to 100%.

CONCLUSIONS

The 12-lead ECG and 3-lead VCG can be derived accurately from 3 measured leads with high quantitative and qualitative correlations. These derived tracings can be acquired instantaneously and displayed in real time from a cardiac rhythm monitor. This will allow for immediate, on-demand, convenient, and cost-effective acquisition and analysis of the 12-lead ECG and 3-lead VCG in areas of acute patient care.

Reconstructing ECG precordial leads from a reduced lead set using independent component analysis

In this paper, precordial lead reconstruction from a reduced set of leads is considered. We propose the use of independent component analysis to train patient-specific transforms from a reduced lead set to the six precordial leads of the standard 12-lead electrocardiogram. The proposed approach is applied to a publicly available database comprising 549 ECG recordings of patients with varying cardiovascular conditions. The fidelity of reconstruction is measured using percent correlation between the actual and reconstructed signals following a 30 seconds time lapse. The mean correlation is over 95% with a standard deviation under 12.7% for all reconstructed leads. The results demonstrate the potential of the suggested approach to provide a reliable solution to precordial leads reconstruction.

Synthesis of the 12-lead electrocardiogram from differential leads

A new approach is proposed for synthesizing the standard 12-lead ECG from three differential leads formed by pairs of proximal electrodes on the body surface. The method is supported by a statistical analysis that gives the best personalized positions of electrodes. The measurements from multichannel ECGs were used to calculate the differential leads. Our algorithm searches for optimal differential leads and the corresponding personalized transformation matrix that is used to synthesize the standard 12-lead ECG. The algorithm has been evaluated on 99 multichannel ECGs measured on 30 healthy subjects and 35 patients scheduled for elective cardiac surgery. It is shown that the algorithm significantly outperforms the synthesis based on the EASI lead system with medians of correlation coefficients greater than 0.954 for all 12 standard leads. To determine the optimal number of differential leads, the syntheses for two, three, and four differential leads were calculated. The analysis shows that 3 is the optimal number of differential leads for practical applications. Because of the proximity of the differential electrodes, the proposed approach offers an opportunity for the synthesis of a standard 12-lead ECG with wireless electrodes.

Standardization of reduced and optimal lead sets for continuous electrocardiogram monitoring: where do we stand?

Mason-Likar or proprietary reduced lead (RL) configurations are used for continuous 12-lead electrocardiogram (ECG) monitoring. Because each RL set has a different electrode configuration and derivation, they are inherently different and should not be compared with each other or with the Mason-Likar or standard ECG to determine changes in an individual over time. Recently, cases have been reported regarding misdiagnosis resulting from such invalid comparisons. This article addresses several relevant questions and presents data collected from 559 subjects (one third, prior myocardial infarction; one third, left ventricular hypertrophy; one third, healthy controls) comparing standard limb leads with body surface potential map (BSPM) leads. We conclude the following: (1) There are few circumstances that justify the use of RL 12-lead ECGs; the convenience should be weighed by the risk of misdiagnosis resulting from serial comparison of nonequivalent ECGs. (2) When RL monitoring is justified, standardization of one universally adopted method would reduce confusion about multiple proprietary lead configurations and minimize invalid ECG comparisons in individuals treated in multiple hospital units with different manufacturers' cardiac monitors. (3) Standard limb lead P-QRS-T waveforms correlate highly with BSPM leads located outside standard unipolar precordial lead sites. Until it is clear that "optimum" BSPM lead sites do not overlap with ECG information already contributed from standard limb leads, it is premature to recommend alternative lead sites for ECG monitoring.

Where do derived precordial leads fail?

A 12-lead electrocardiogram (ECG) reconstructed from a reduced subset of leads is desired in continued arrhythmia and ST monitoring for less tangled wires and increased patient comfort. However, the impact of reconstructed 12-lead lead ECG on clinical ECG diagnosis has not been studied thoroughly. This study compares the differences between recorded and reconstructed 12-lead diagnostic ECG interpretation with 2 commonly used configurations: reconstruct precordial leads V(2), V(3), V(5), and V(6) from V(1),V(4), or reconstruct V(1), V(3), V(4), and V(6) from V(2),V(5). Limb leads are recorded in both configurations. A total of 1785 ECGs were randomly selected from a large database of 50,000 ECGs consecutively collected from 2 teaching hospitals. ECGs with extreme artifact and paced rhythm were excluded. Manual ECG annotations by 2 cardiologists were categorized and used in testing. The Philips resting 12-lead ECG algorithm was used to generate computer measurements and interpretations for comparison. Results were compared for both arrhythmia and morphology categories with high prevalence interpretations including atrial fibrillation, anterior myocardial infarct, right bundle-branch block, left bundle-branch block, left atrial enlargement, and left ventricular hypertrophy. Sensitivity and specificity were calculated for each reconstruction configuration in these arrhythmia and morphology categories. Compared to recorded 12-leads, the V(2),V(5) lead configuration shows weakness in interpretations where V(1) is important such as atrial arrhythmia, atrial enlargement, and bundle-branch blocks. The V(1),V(4) lead configuration shows a decreased sensitivity in detection of anterior myocardial infarct, left bundle-branch block (LBBB), and left ventricular hypertrophy (LVH). In conclusion, reconstructed precordial leads are not equivalent to recorded leads for clinical ECG diagnoses especially in ECGs presenting rhythm and morphology abnormalities. In addition, significant accuracy reduction in ECG interpretation is not strongly correlated with waveform differences between reconstructed and recorded 12-lead ECGs.

Simultaneous comparison of 3 derived 12-lead electrocardiograms with standard electrocardiogram at rest and during percutaneous coronary occlusion

AIM

The aim of the study was to simultaneously test the EASI lead system and two other derived ECG methods against the standard 12-lead ECG during percutaneous coronary intervention (PCI).

METHODS

During 44 percutaneous coronary interventions, a simultaneously recorded 12-lead and EASI ECG were marked at the start of the PCI (baseline) and at known ischemia caused by balloon inflation (peak). ST deviations were measured 60 ms after the J point at baseline and peak in all leads and were summated (SUMST) to assess overall changes. For regional changes, the lead with the highest ST deviation (PEAKST) was marked. For each patient, derived 12-lead ECGs were computed from the EASI leads and a lead subset using patient-specific coefficients (PS) and coefficients based on a patient population (GEN). Absolute differences were computed between each derived and routine ECG for SUMST and PEAKST.

RESULTS

SUMST was at baseline 567 microV (range: 150-1707) and increased at peak to 871 microV (range: 350-2101). SUMST difference at peak was for EASI: 163 microV (CI: 90-236, P <.001), GEN: 46 microV (CI: 2-91, P = .40), and PS: 16 microV (CI: 3-30, P = .15). PEAKST difference at peak was for EASI: 49 microV (CI: 19-220, P = .02), GEN: 48 microV (CI: -43-154, P = .26), and PS: 20 microV (CI: -51-32, P = .65).

CONCLUSION

Simultaneous direct comparison of three derived ECG methods shows overall and regional differences in accuracy across PS, GEN, and EASI. Median SUMST and PEAKST differences for PS are lower than for GEN and EASI, and show a more accurate reconstruction.

Synthesising the 12-lead electrocardiogram: Trends and challenges

An area of electrocardiography which has received much interest of late is that of synthesising the 12-lead ECG from a reduced number of leads. The main advantage of this approach is obvious, as fewer recording sites are required to capture the same information. This, in turn, streamlines the ECG acquisition process with little detriment to the integrity of information used for interpretation. In the current article, we provide an overview of ECG synthesis along with a description of various 'limited lead' systems that have been reported in the literature. Based on this, several suggestions as to what the ECG of the future may entail have been made.

Pitfalls and artifacts in electrocardiography

The standard 12-lead ECG is a common diagnostic test that provides a wealth of diagnostic information of value for clinical decision making. Its value, however, depends upon the accuracy of its recording. This article presents common errors in clinical electrocardiography including inaccurate lead placement, inappropriate serial comparisons using different lead sets, lead wire reversals, inappropriate filter settings, and excessively noisy signals. Practical information is provided to prevent errors and to improve the quality and utility of ECGs.

How many ECG leads do we need?

The number of leads needed in clinical electrocardiography depends on the clinical problem to be solved. The standard 12-lead ECG is so well established that alternative lead systems must prove their advantage through well-conducted clinical studies to achieve clinical acceptance. Certain additional leads seem to add valuable information in specific patient groups. The use of a large number of leads (eg, in body surface potential mapping) may add clinically relevant information, but it is cumbersome and its clinical advantage is yet to be proven. Reduced lead sets emulate the 12-lead ECG reasonably well and are especially advantageous in emergency situations.

AHA scientific statement: practice standards for electrocardiographic monitoring in hospital settings: an American Heart Association Scientific Statement from the Councils on Cardiovascular Nursing, Clinical Cardiology, and Cardiovascular Disease in the Young: endorsed by the International Society of Computerized electrocardiology and the American Association of Critical-Care Nurses

The goals of electrocardiographic (ECG) monitoring in hospital settings have expanded from simple heart rate and basic rhythm determination to the diagnosis of complex arrhythmias, myocardial ischemia, and prolonged QT interval. Whereas Computerized arrhythmia analysis is automatic in cardiac monitoring systems, computerized ST-segment ischemia analysis is available only in newer-generation monitors, and computerized QT-interval monitoring is currently unavailable. Even in hospitals with ST-monitoring capability, ischemia monitoring is vastly underutilized by healthcare professionals. Moreover, because no computerized analysis is available for QT monitoring, healthcare professionals must determine when it is appropriate to manually measure QT intervals (eg, when a patient is started on a potentially proarrhythmic drug). The purpose of the present review is to provide "best practices" for hospital ECG monitoring. Randomized clinical trials in this area are almost nonexistent; therefore, expert opinions are based upon clinical experience and related research in the field of electrocardiography. This consensus document encompasses all areas of hospital cardiac monitoring in both children and adults. The emphasis is on information clinicians need to know to monitor patients safely and effectively. Recommendations are made with regard to indications, time frames, and strategies to improve the diagnostic accuracy of cardiac arrhythmia, ischemia, and QT-interval monitoring. Currently available ECG lead systems are described, and recommendations related to staffing, training, and methods to improve quality are provided.