Important ECG Research Articles

Personalized ECG monitoring and adaptive machine learning

This non-technical review introduces key concepts in personalized ECG monitoring (pECG), which aims to optimize the detection of clinical events and their warning signs as well as the selection of alarm thresholds. We review several pECG methods, including anomaly detection and adaptive machine learning (ML), in which learning is performed sequentially as new data are collected.We describe a distributed-network multiscale pECG system to show how the computational load and time associated with adaptive ML could be optimized. In this architecture, the limited analysis of ECG waveforms is performed locally (e.g., on a smart phone) to determine a small number of clinically important ECG elements, and an adaptive ML engine is located on a remote server (Internet cloud) to determine an individual's “fingerprint” basis patterns and to detect anomalies in those patterns.

Deep neural network technique for automated detection of ADHD and CD using ECG signal

Background and objectiveAttention Deficit Hyperactivity problem (ADHD) is a common neurodevelopment problem in children and adolescents that can lead to long-term challenges in life outcomes if left untreated. Also, ADHD is frequently associated with Conduct Disorder (CD), and multiple research have found similarities in clinical signs and behavioral symptoms between both diseases, making differentiation between ADHD, ADHD comorbid with CD (ADHD+CD), and CD a subjective diagnosis. Therefore, the goal of this pilot study is to create the first explainable deep learning (DL) model for objective ECG-based ADHD/CD diagnosis as having an objective biomarker may improve diagnostic accuracy. MethodsThe dataset used in this study consist of ECG data collected from 45 ADHD, 62 ADHD+CD, and 16 CD patients at the Child Guidance Clinic in Singapore. The ECG data were segmented into 2 s epochs and directly used to train our 1-dimensional (1D) convolutional neural network (CNN) model. ResultsThe proposed model yielded 96.04% classification accuracy, 96.26% precision, 95.99% sensitivity, and 96.11% F1-score. The Gradient-weighted class activation mapping (Grad-CAM) function was also used to highlight the important ECG characteristics at specific time points that most impact the classification score. ConclusionIn addition to achieving model performance results with our suggested DL method, Grad-CAM's implementation also offers vital temporal data that clinicians and other mental healthcare professionals can use to make wise medical judgments. We hope that by conducting this pilot study, we will be able to encourage larger-scale research with a larger biosignal dataset. Hence allowing biosignal-based computer-aided diagnostic (CAD) tools to be implemented in healthcare and ambulatory settings, as ECG can be easily obtained via wearable devices such as smartwatches.

The impact of fragmented QRS on clinical findings and outcomes in children with dilated cardiomyopathy with or without left ventricular non-compaction.

The aim of this study is to investigate the frequency of fragmented QRS and its associations with clinical findings and prognosis in children diagnosed with dilated cardiomyopathy with or without left ventricular non-compaction. This retrospective study was conducted between 2010 and 2020. Patients with dilated cardiomyopathy were classified into two groups according to the presence of left ventricular non-compaction: Dilated cardiomyopathy with left ventricular non-compaction and dilated cardiomyopathy without left ventricular non-compaction. Patients were also divided into two groups according to the presence of fragmented QRS (fragmented QRS group and non-fragmented QRS group). Twenty-three of 44 patients (52.3%) were male. Among left ventricular non-compaction patients, the fragmented QRS group had more complex ventricular arrhythmias (p = 0.003). Patients with fragmented QRS had a significantly higher rate of major adverse cardiac events and/or cardiac death in both cardiomyopathy groups (p = 0.003 and p = 0.005). However, the rate of major adverse cardiac events and/or cardiac death was similar between dilated cardiomyopathy patients with and without left ventricular non-compaction. Multivariate logistic regression analysis showed that the presence of fragmented QRS strongly predicts major adverse cardiac events and/or cardiac death (odds ratio, 31.186; 95% confidence interval, 2.347-414.307). Although the survival rates between cardiomyopathy groups were similar, patients with fragmented QRS had a markedly lower survival rate during the follow-up period, as mean of 15 months (p = 0.001). Our study showed that the presence of fragmented QRS may be an important ECG sign predicting an major adverse cardiac event and/or cardiac death in patients with dilated cardiomyopathy. We believe that recognising fragmented QRS could be valuable in forecasting patient prognosis and identifying high-risk patients who require additional support.

Stacked machine learning models to classify atrial disorders based on clinical ECG features: a method to predict early atrial fibrillation.

Atrial Tachycardia (AT) and Left Atrial Enlargement (LAE) are atrial diseases that are significant precursors to Atrial Fibrillation (AF). There are ML models for ECG classification; clinical features-based classification is required. The suggested work aims to create stacked ML models that categorize Sinus Rhythm (SR), Sinus Tachycardia (ST), AT, and LAE signals based on clinical parameters for AF prognosis. The classification was based on thirteen clinical parameters, such as amplitude, time domain ECG aspects, and P-Wave Indices (PWI), such as the ratio of P-wave length and amplitude ((P (ms)/P (µV)), P-wave area (µV*ms), and P-wave terminal force (PTFV1(µV*ms). Apart from classifying the ECG signals, the stacked ML models prioritized the clinical features using a pie formula-based technique. The Stack 1 model achieves 99% accuracy, sensitivity, precision, and F1 score, while the Stack 2 model achieves 91%, 91%, 94%, and 92% for identifying SR, ST, LAE, and AT, respectively. Both stack models obtained a computational time of 0.06 seconds. PTFV1 (µV*ms), P (ms)/P (µV)), and P-wave area (µV*ms) were ranked as crucial clinical features. Clinical feature-based stacking ML models may help doctors obtain insight into important clinical ECG aspects for early AF prediction.

Abstract 12636: ECG-Based Risk Stratification of Long-Term Mortality in Suspected Acute Coronary Syndrome

Introduction: Non-invasive risk stratification can play a significant role in the initial triage of patients with suspected acute coronary syndrome. Numerous ECG features have been previously linked with mortality in both the general and cardiac populations. We sought to build an ECG-based model that can predict time-to-death in high-risk chest pain patients transported by Emergency Medical Services. Methods: This was a prospective observational cohort study of patients evaluated for suspected acute coronary syndrome. The study recruited consecutive patients evaluated at the emergency department of three UPMC-affiliated tertiary care hospitals. We included consecutive patients in whom a prehospital echocardiogram was performed during indexed encounter. Baseline ECGs were preprocessed using manufacturer specific algorithms. We evaluated 554 temporal-spatial features of the 12-lead ECG in multivariate proportional hazards Cox regression model for predicting all-cause death during long term follow up. Death was ascertained using phenotyping UPMC electronic hospital records and through the CDC National Death Index database. Model fit was evaluated using C-statistic and AIC. We set alpha at 0.01 for two-tailed hypothesis testing. Results: Our sample included 2311 patients (aged 59 ± 16.6, 47% females, 41% Black). Over a median follow up period of 1868 days (IQR = 688; [Q1, Q3] = [1511, 2199]), there was 622 (26.9%) adjudicated deaths. During univariate analysis, 288 features were significantly associated with time-to-death. In the final multivariate model (c-statistic = 0.79, AIC = 9066.05), controlling for demographic and clinical data, 15 ECG features remained predictive of the study outcome, with Selvester score being the strongest predictor (Hazard Ratio = 1.09, 95% CI = 1.03 - 1.16). Other clinically important ECG features included horizontal-plane QRS axis, QRS-T angle, QTc interval, QTpeak, T peak-end, S wave morphology the inferior leads, QRS morphology in lead aVR, and T wave morphology in the anterior leads. Conclusions: The presenting 12-lead ECG can serve as a powerful risk stratification tool of long-term mortality in patients with chest pain. Selvester score can serve as a plausible and easy to use clinical decision tool.

Abstract 14074: Machine Learning Applied on ECG Improves Cardiovascular Risk Prediction When Added to Coronary Artery Calcium Scoring in Patients With Cancer

Introduction: Patients with cancer are at increased cardiovascular (CV) risk. Both Coronary Artery Calcium (CAC) and 12-lead ECG features predict CV risk in the general population. However, the impact of these measures has not been investigated in patients with cancer. Hypothesis: We hypothesized that machine learning-based analysis of automatically-extracted ECG features, when added to CAC, can improve CV risk stratification in patients with cancer. Methods: We analyzed 1825 cancer patients with at least 1 CV risk factor who underwent CAC scoring and a standard 12-ECG as part of the CLARIFY (ClinicalTrials.gov Identifier: NCT04075162) registry. Events were defined as myocardial infarction, revascularization, stroke or death. A total of 445 ECG features were automatically extracted using a commercially available software (ECG Muse, GE healthcare). A LASSO-Cox method was used with 10-fold cross validation to select the most important ECG features with a novel ECG score derived using individual feature coefficients. We evaluated the ability of the combined ECG score with age, sex, and CAC to prognosticate a CV event in comparison to a model that was limited to age, sex, and CAC alone. The models were compared using an ANOVA test. Results: Over a median follow-up of 14 months, 224 patients had an event. Using the LASSO-Cox model, 25 ECG features were selected. The novel ECG score was associated with CV events (HR 8.6, 95% CI: 5.1-14.3, P<0.001), which was not attenuated after adjusting for age, sex, and CACS (HR 7.3, 95% CI: 4.3-12.4, P<0.001). Addition of ECG score to a model including age, sex, and CAC improved discrimination (C-Index 0.63 vs 0.72, P=0.0001). Conclusions: A novel ML-based ECG risk score using automatically extracted ECG features can improve CV risk stratification when added to CAC and demographics in patients with cancer.

Wide Complex Tachycardia in a Young Woman

A 27-year-old woman presented with palpitations and was found to have episodes of a non-sustained wide complex tachycardia. In this report, we discuss a differential diagnosis for the patient’s wide complex tachycardia and the important ECG findings which lead to her diagnosis. (Level of Difficulty: Advanced.)

The long and short of it!

AimsThe aim of this study was to conduct a literature search on long and short QTc and its implications on prescribing medications.We also intended to assess the knowledge of psychiatry core trainees in the South Yorkshire region regarding QTc and its implications on prescribing for patients.BackgroundThe majority of emphasis lies in ensuring the QTc interval is within range for our patients before initiation of psychotropic medication and as part of monitoring during the maintenance phase. The main dread for most psychiatrists is a prolonged QTc interval, however, a short QTc is equally important to identify and manage.MethodA literature search was performed using the key words “QTc, psychotropics, and ECG”. Results revealed extensive data on long QTc, but very few articles on prescribing psychotropics and short QTc. Most psychotropics are known to prolong QTc interval, which is what clinicians are worried about most when deciding to prescribe medications in mental health services. However, short QTc is also an equally important ECG finding which should not be ignored. We conducted a survey amongst core trainees in the South Yorkshire training scheme to gauge trainees’ knowledge of QTc and its implications when prescribing psychotropic medications. The survey was designed with SurveyMonkey and had seven questions to keep it user friendly.ResultThe survey was distributed to 47 core trainees working in the South Yorkshire region with a response rate of 42.5%. CT1s comprised 30%, CT2s comprised 40% and CT3s comprised 30% of the total number of responders. 60% trainees reported performing and reviewing ECGs as an integral part of their jobs. 50% trainees believed both a short and long QTc interval were life threatening with 50% considering only long QTc as being fatal. 95% of the responders reported not knowing any medications causing QTc shortening; however 100% reported knowing medications causing QTc prolongation.ConclusionThe results clearly show that we need to increase awareness regarding short QTc interval and its implications on patient health. Review of literature also highlights the challenges in treating patients with QTc abnormalities. In such situations, it's advised to seek advice from Cardiology colleagues to ensure safe and effective patient care. It would also be beneficial to arrange refresher workshops to help psychiatrists brush on their ECG skills.

Multiscale Residual Network Based on Channel Spatial Attention Mechanism for Multilabel ECG Classification.

Automatic classification of ECG is very important for early prevention and auxiliary diagnosis of cardiovascular disease patients. In recent years, many studies based on ECG have achieved good results, most of which are based on single-label problems; one record corresponds to one label. However, in actual clinical applications, an ECG record may contain multiple diseases at the same time. Therefore, it is very important to study the multilabel ECG classification. In this paper, a multiscale residual deep neural network CSA-MResNet model based on the channel spatial attention mechanism is proposed. Firstly, the residual network is integrated into a multiscale manner to obtain the characteristics of ECG data at different scales and then increase the channel spatial attention mechanism to better focus on more important channels and more important ECG data fragments. Finally, the model is used to classify multilabel in large databases. The experimental results on the multilabel CCDD show that the CSA-MResNet model has an average F1 score of 88.2% when the multilabel classification of 9 ECGs is performed. Compared with the benchmark model, the F1 score of CSA-MResNet in the multilabel ECG classification increased by up to 1.7%. And, in the model verification on another database HF-challenge, the final average F1 score is 85.8%. Compared with the state-of-the-art methods, CSA-MResNet can help cardiologists perform early-stage rapid screening of ECG and has a certain generalization performance, providing a feasible analysis method for multilabel ECG classification.

In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.

BackgroundClassical ST‐T waveform changes on standard 12‐lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decision during patient evaluation, yet their clinical utility remains unclear.Methods and ResultsThis was an observational study of consecutive patients evaluated for suspected ACS (Cohort 1 n=745, age 59±17, 42% female, 15% ACS; Cohort 2 n=499, age 59±16, 49% female, 18% ACS). Out of 554 temporal‐spatial ECG waveform features, we used domain knowledge to select a subset of 65 physiology‐driven features that are mechanistically linked to myocardial ischemia and compared their performance to a subset of 229 data‐driven features selected by multiple machine learning algorithms. We then used random forest to select a final subset of 73 most important ECG features that had both data‐ and physiology‐driven basis to ACS prediction and compared their performance to clinical experts. On testing set, a regularized logistic regression classifier based on the 73 hybrid features yielded a stable model that outperformed clinical experts in predicting ACS, with 10% to 29% of cases reclassified correctly. Metrics of nondipolar electrical dispersion (ie, circumferential ischemia), ventricular activation time (ie, transmural conduction delays), QRS and T axes and angles (ie, global remodeling), and principal component analysis ratio of ECG waveforms (ie, regional heterogeneity) played an important role in the improved reclassification performance.ConclusionsWe identified a subset of novel ECG features predictive of ACS with a fully interpretable model highly adaptable to clinical decision support applications.RegistrationURL: https://www.clinicaltrials.gov; Unique Identifier: NCT04237688.

Electrocardiogram Classification Based on Deep Convolutional Neural Networks: A Review

Due to many new medical uses, the value of ECG classification is very demanding. There are some Machine Learning (ML) algorithms currently available that can be used for ECG data processing and classification. The key limitations of these ML studies, however, are the use of heuristic hand-crafted or engineered characteristics of shallow learning architectures. The difficulty lies in the probability of not having the most suitable functionality that will provide this ECG problem with good classification accuracy. One choice suggested is to use deep learning algorithms in which the first layer of CNN acts as a feature. This paper summarizes some of the key approaches of ECG classification in machine learning, assessing them in terms of the characteristics they use, the precision of classification important physiological keys ECG biomarkers derived from machine learning techniques, and statistical modeling and supported simulation.

Abstract 13596: Identifying the Most Important Ecg Predictors of Reduced Ejection Fraction in Patients With Suspected Acute Coronary Syndrome

Background: Non-invasive screening tools of cardiac function can play a significant role in the initial triage of patients with suspected acute coronary syndrome. Numerous ECG features have been previously linked with cardiac contractility in the general population. We sought to identify ECG features that are most predictive for real-time screening of reduced left ventricular ejection fraction (LVEF) in the acute care setting. Methods: We performed a secondary analysis of a prospective, observational cohort study of patients evaluated for suspected acute coronary syndrome. The parent study recruited consecutive patients evaluated at the emergency department of three UPMC-affiliated tertiary care hospitals. We included consecutive patients in whom an echocardiogram was performed during indexed encounter. Baseline ECGs were preprocessed using manufacturer specific algorithms. We evaluated 554 automated 12-lead ECG features in multivariate linear regression for predicting LVEF. We then used regression trees to identify the most important predictive ECG features. Results: Our final sample included 297 patients (aged 63 ± 15, 45% females). The mean LVEF was 57% ± 13 (IQR 50%-65%). In multivariate analysis, depolarization dispersion in the horizontal plane; global repolarization dispersion; and abnormal temporal indices in inferolateral leads were all independent predictors of LVEF (R 2 = 0.452, F = 6.679, p < 0.001). Horizontal QRS axis deviation and prolonged ventricular activation (VAT) time in left ventricular apex were the most important determinants of reduced LVEF (Figure), while global QRS duration was of less importance. Conclusions: Poor R wave progression in precordial leads with dominant QS pattern in V3 is the most predictive feature of reduced LVEF in suspected ACS. This feature constitutes a simple visual marker to aid clinicians in identifying those with impaired cardiac function.

Identifying the most important ECG predictors of reduced ejection fraction in patients with suspected acute coronary syndrome

BackgroundNon-invasive screening tools of cardiac function can play a significant role in the initial triage of patients with suspected acute coronary syndrome. Numerous ECG features have been previously linked with cardiac contractility in the general population. We sought to identify ECG features that are most predictive for real-time screening of reduced left ventricular ejection fraction (LVEF) in the acute care setting. MethodsWe performed a secondary analysis of a prospective, observational cohort study of patients evaluated for suspected acute coronary syndrome. We included consecutive patients in whom an echocardiogram was performed during indexed encounter. We evaluated 554 automated 12-lead ECG features in multivariate linear regression for predicting LVEF. We then used regression trees to identify the most important predictive ECG features. ResultsOur final sample included 297 patients (aged 63 ± 15, 45% females). The mean LVEF was 57% ± 13 (IQR 50%–65%). In multivariate analysis, depolarization dispersion in the horizontal plane; global repolarization dispersion; and abnormal temporal indices in inferolateral leads were all independent predictors of LVEF (R2 = 0.452, F = 6.679, p < 0.001). Horizontal QRS axis deviation and prolonged ventricular activation time in left ventricular apex were the most important determinants of reduced LVEF, while global QRS duration was of less importance. ConclusionsPoor R wave progression in precordial leads with dominant QS pattern in V3 is the most predictive feature of reduced LVEF in suspected ACS. This feature constitutes a simple visual marker to aid clinicians in identifying those with impaired cardiac function.

Atrial Fibrillation Detection Algorithm Based on Manual Extraction Features and Automatic Extraction Features

Atrial Fibrillation is a common arrhythmia. RR interval irregularity and P wave disappeared and replaced by continuous f-wave is the two important ECG manifestations of atrial fibrillation. RR interval irregularity can also be reflected in other types of arrhythmia, P Wave or f wave is a weak signal, its feature point detection is difficult and shape features are difficult to grasp. Therefore, this paper proposes a method combining manual extraction features and neural network extraction features for atrial fibrillation detection. Experiments were performed using the MIT-BIH atrial fibrillation database. The ECG signals were first processed into equal-length data, and then the 180 dimensional time-frequency domain features were manually extracted and combined with the improved 128 dimensional features extracted by the neural network. The extracted feature input integration model, the sub-model includes Decision Tree, Random forest, GBDT, XGBoost, LightGBM.In the final experiment, the stacking model was used. The accuracy, accuracy, recall rate, F1 and AUC were 99.1%, 98.9%, 98.9%, 98.9%, and 99.4%. This method is better than the single model, which provided a very good measure for detecting atrial fibrillation.

Thyroid dysfunction and electrocardiographic changes in subjects without arrhythmias: a cross-sectional study of primary healthcare subjects from Copenhagen

ObjectiveThe objective of the present study was to investigate associations of both overt and subclinical thyroid dysfunction with common ECG parameters in a large primary healthcare population.DesignCross-sectional study.Setting and participantsThe...

Analytical accuracy of the handheld PICO monitoring device during emergencies

IntroductionGeneral practitioners (GP) use few technical devices during emergency situations. They have to recognise which patients have a life-threatening disease in order to treat or refer them appropriately. Monitoring by...

Diagnostic and prognostic values of the V-index, a novel ECG marker quantifying spatial heterogeneity of ventricular repolarization, in patients with symptoms suggestive of non-ST-elevation myocardial infarction

BackgroundThe V-index is an ECG marker quantifying spatial heterogeneity of ventricular repolarization. We prospectively assessed the diagnostic and prognostic values of the V-index in patients with suspected non-ST-elevation myocardial infarction (NSTEMI). MethodsWe prospectively enrolled 497 patients presenting with suspected NSTEMI to the emergency department (ED). Digital 12-lead ECGs of five-minute duration were recorded at presentation. The V-index was automatically calculated in a blinded fashion. Patients with a QRS duration >120ms were ruled out from analysis. The final diagnosis was adjudicated by two independent cardiologists. The prognostic endpoint was all-cause mortality during 24months of follow-up. ResultsNSTEMI was the final diagnosis in 14% of patients. V-index levels were higher in patients with AMI compared to other causes of chest pain (median 23ms vs. 18ms, p<0.001). The use of the V-index in addition to conventional ECG-criteria improved the diagnostic accuracy for the diagnosis of NSTEMI as quantified by area under the ROC curve from 0.66 to 0.73 (p=0.001) and the sensitivity of the ECG for AMI from 41% to 86% (p<0.001). Cumulative 24-month mortality rates were 99.4%, 98.4% and 88.3% according to tertiles of the V-index (p<0.001). After adjustment for age and important ECG and clinical parameters, the V-index remained an independent predictor of death. ConclusionsThe V-index, an ECG marker quantifying spatial heterogeneity of ventricular repolarization, significantly improves the accuracy and sensitivity of the ECG for the diagnosis of NSTEMI and independently predicts mortality during follow-up.

Comparison of Baseline Wander Removal Techniques considering the Preservation of ST Changes in the Ischemic ECG: A Simulation Study.

The most important ECG marker for the diagnosis of ischemia or infarction is a change in the ST segment. Baseline wander is a typical artifact that corrupts the recorded ECG and can hinder the correct diagnosis of such diseases. For the purpose of finding the best suited filter for the removal of baseline wander, the ground truth about the ST change prior to the corrupting artifact and the subsequent filtering process is needed. In order to create the desired reference, we used a large simulation study that allowed us to represent the ischemic heart at a multiscale level from the cardiac myocyte to the surface ECG. We also created a realistic model of baseline wander to evaluate five filtering techniques commonly used in literature. In the simulation study, we included a total of 5.5 million signals coming from 765 electrophysiological setups. We found that the best performing method was the wavelet-based baseline cancellation. However, for medical applications, the Butterworth high-pass filter is the better choice because it is computationally cheap and almost as accurate. Even though all methods modify the ST segment up to some extent, they were all proved to be better than leaving baseline wander unfiltered.

Electrocardiographic aspects of morphogenesis of neuroleptic cardiopathy

To elucidate dynamics of ECG at different clinical stages of NCMP and evaluate its diagnostic value. Methods. We determined the frequency and informative values of 8 different ECG signs includingQTinterval corrected based at theBazettformula (QTc). Their informative value was estimated from the Kullback formula. The data obtained were analyzed by parametric and non-parametric statistics methods. Pathological changes of ECG parameters associated with NCM Preflect processes of structural rearrangement in the heart at different levels of its organization. The analysis of the available information showed that almost all ECG parameters have high diagnostic value listed in the following rank order (frequency/informativeness): rhythm disorder - 1/8, prolongation of the QTc interval - 4/3, conduction disorder - 2/2, diffuse muscular changes - 3/1, right heart overload - 5/6, left ventricular hypertrophy - 7-8/5, impaired electrical activity of myocardium - 7-8/5, deviation of the electrical axis to the left - 6/4. It seems appropriate to distinguish the following three maximally informative (and therefore most important) ECG signs indicative of developing NCMP: diffuse muscular changes, conduction disorder, and prolongation of the QTc interval. The analysis of the available information provides a new insight into dynamics of ECG signs associated with NCMP and permits to estimate the diagnostic value of pathological changes for its earlier detection.

Multi-channel ECG data compression using compressed sensing in eigenspace

In recent years, compressed sensing (CS) has emerged as a potential alternative to traditional data compression techniques for resource-constrained telemonitoring applications. In the present work, a CS framework of data reduction is proposed for multi-channel electrocardiogram (MECG) signals in eigenspace. The sparsity of dimension-reduced eigenspace MECG signals is exploited to apply CS. First, principal component analysis (PCA) is applied over the MECG data to retain diagnostically important ECG features in a few principal eigenspace signals based on maximum variance. Then, the significant eigenspace signals are randomly projected over a sparse binary sensing matrix to obtain the reduced dimension compressive measurement vectors. The compressed measurements are quantized using a uniform quantizer and encoded by a lossless Huffman encoder. The signal recovery is carried out by an orthogonal matching pursuit (OMP) algorithm. The proposed method is evaluated on the MECG signals from PTB and CSE multilead measurement library databases. The average value of percentage root mean square difference (PRD) across the PTB database is found to be 5.24% at a compression ratio (CR)=17.76 in Lead V3 of PTB database. The visual signal quality of the reconstructed MECG signals is validated through mean opinion score (MOS), found to be 6.66%, which implies very good quality signal reconstruction.