The Role of Electrocardiogram DETERMINE Score in Prediction of Coronary Artery Disease Severity

Background: A major cause of mortality and disability on a global scale is myocardial infarction (MI). These days, the most reliable way to detect and measure MI is via cardiovascular magnetic resonance imaging (CMR). Aims and Objectives: To evaluate the effectiveness of the Electrocardiogram DETERMINE Score in predicting the severity of coronary artery disease (CAD) in patients who have experienced an Acute Myocardial Infarction (AMI) & to assess improvements in left ventricular function at 6 months following coronary artery bypass grafting (CABG). Subjects and Methods: This Observational cohort study was done at the Cardiology and Radiology department and cardiac surgery department, Al-Azhar university hospitals and Helwan University hospital. The study involved 700 cases who patients diagnosed with Acute Myocardial Infarction and fulfilled specific criteria for selection. Result: There was highly statistically significant relation between Myocardial infarction size and ECG Marker Score as mean infarct size elevated When the number of ECG markers increased. There was a highly statistically significant relation between myocardial infarct segments, myocardial infarction size and improvement of cardiac function 6 months post-CABG. Conclusion: The study found that larger myocardial infarctions corresponded with higher DETERMINE Scores. It concluded that an ECG-based score better estimates infarct size than LVEF alone. Additionally, there was a significant statistical correlation between the size and segmentation of myocardial infarction and better cardiac function six months after CABG.

Unfamiliar waveforms spanning from the ST to TP segments only observed in certain limb leads of the standard 12-lead electrocardiogram due to Aslanger’ s sign

Aslanger’s sign,also known as the arterial pulse tapping artifact or electromechanical association artifact,is an electrocardiographic artifact caused by arterial pulsation at the site where the limb leads of the standard 12-lead electrocardiogram near the radial or posterior tibial arteries are positioned,particularly in hyperdynamic states.[1–8]It occurs in every cardiac cycle with a constant coupling interval between the QRS complex and artifact.This synchronization with the underlying heart rhythm makes it less likely to be recognized as an artifact compared to unsynchronized artifacts,such as those caused by limb movement and inadequate contact between the electrode and skin.[1,2,7,8]Almost all reported cases of Aslanger’s sign exhibit an unusual waveform morphology in all 12 leads except one of the standard 12-lead electrocardiogram.This sign is often confused with an electrocardiographic finding commonly observed during acute coronary events.

Intelligent Electrocardiogram Analysis in Medicine:Data,Methods,and Applications

Electrocardiogram(ECG)is a low-cost,simple,fast,and non-invasive test.It can reflect the heart’s electrical activity and provide valuable diagnostic clues about the health of the entire body.Therefore,ECG has been widely used in various biomedical applications such as arrhythmia detection,disease-specific detection,mortality prediction,and biometric recognition.In recent years,ECG-related studies have been carried out using a variety of publicly available datasets,with many differences in the datasets used,data preprocessing methods,targeted challenges,and modeling and analysis techniques.Here we systematically summarize and analyze the ECGbased automatic analysis methods and applications.Specifically,we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing processes.Then we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these applications.Finally,we elucidated some of the challenges in ECG analysis and provided suggestions for further research.

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

Electrocardiogram(ECG)signal is a measure of the heart’s electrical activity.Recently,ECG detection and classification have benefited from the use of computer-aided systems by cardiologists.The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification accuracy.In addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall performance.To prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing approaches.Moreover,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests.Experimental results confirmed the superiority and effectiveness of the proposed approach.The classification accuracy achieved by the proposed approach is(99.98%).

Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

Arrhythmia has been classified using a variety of methods.Because of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more appealing.Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives.Cardiac arrhythmia classification and prediction have greatly improved in recent years.Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish.Every year,it is one of the main reasons of mortality for both men and women,worldwide.For the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)classifier.The proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia dataset.The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features.The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients.This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature.The achieved classification accuracy using the proposed approach is 99.8%.

EFFECTS OF BODY TEMPERATURE ON ELECTROCARDIOGRAMS OF LIZARD Eremias multiocellata *

Electrocardiograms (ECG) of Eremias multiocellata were studied at 5-35℃ in body temperature. Electrocardiogram wave intervals (R-R,P-R,QRS,T-P,and R-T) shortened while heart rate increased with the increasing of body temperature. The average heart rate was 14.6/min at 5℃,whereas it was 201/min at 35℃. The duration of wave intervals of ECG and the heart rate were related significantly to the body temperature (P<0.001). Among the components of a cardiac cycle the cardiac rest period (TP intervals) and the atria-ventricular conduction time (PR interval) were affected mostly by body temperature. In the other hand the ventricular depolarization and repolarization (QRS and R-T intervals) were relatively less affected by the body temperature. The increasing of heart rate with body temperature was mainly caused by the shortening of ECG wave intervals,and the T-P interval (the cardiac rest period) was shortened more noticeably than other intervals.

Effect of Exogenous Hydrogen Sulfide(H_2S) on the Electrocardiogram(ECG) of Rats Generally Anaesthetized by Zoletil

Hydrogen sulfide （H2S） is the third gaseous signaling molecule discovered in recent years, and plays an important physiological role in the cardivascular system. To explore the effects of different doses of exogenous H2S on the electrocardiogram （ECG） of rats generally anesthetized by zoletil, different doses of NariS solution were used for the intervention of intraperitoneal injection 20 rain before the zoletil anesthesia. The ECGs of rats from each treatment group during the time range of 10^th-50^th min were determined under general anesthesia, and then were compared with those from the control group. The results showed that exogenous H2S could significantly reduce the Q-T interval time limit, thus played a role in slowing tachycardia or arrhythmia and other anomalies, thereby protecting the heart. S-T segment and T segment evaluation values were significantly reduced, which might be associated with bradycardia.

Early association of electrocardiogram alteration with infarct size and cardiac function after myocardial infarction

Objective: Myocardial infarction (MI) is the main cause of heart failure, but the relationship between the extent of MI and cardiac function has not been clearly determined. The present study was undertaken to investigate early changes in the electrocardiogram associated with infarct size and cardiac function after MI. Methods: MI was induced by ligating the left anterior descending coronary artery in rats. Electrocardiograms, echocardiographs and hemodynamic parameters were assessed and myocardial infarct size was measured from mid-transverse sections stained with Masson抯 trichrome. Results: The sum of pathological Q wave amplitudes was strongly correlated with myocardial infarct size (r = 0.920, P < 0.0001), left ventricular ejection fraction (r = -0.868, P < 0.0001) and left ventricular end diastolic pressure (r = 0.835, P < 0.0004). Furthermore, there was close relationship between MI size and cardiac function as assessed by left ventricular ejection fraction (r = -0.913, P < 0.0001) and left ventricular end diastolic pressure (r = 0.893, P < 0.0001). Conclusion: The sum of pathological Q wave amplitudes after MI can be used to estimate the extent of MI as well as cardiac function.

Idiopathic ventricular fibrillation with fragmented QRS complex and J wave in resting electrocardiogram

Objective To describe the clinical characteristics of idiopathic ventricular fibrillation （IVF） with fragmented QRS complex （f-QRS） and J wave in resting electrocardiogram. Methods We reviewed data from 21 case subjects in our hospital who were resuscitated after cardiac arrest due to IVF and assessed the prevalence of f-QRS and J wave in resting electrocardiogram （ECG）. All the case subjects were classified among three groups based on the electrocardiographic morphology： group I, both f-QRS and J wave were observed （n = 6）, group II, only J wave was observed （n = 9）, group III, neither f-QRS nor J wave was observed （n = 6）. Population characteristics, history of syncope or sudden cardiac arrest, incidence of ventricular fibrillation （VF）, and circumstance of VF were evaluated among the three groups. Results The incidence of index events （syncope, survived cardiac arrest and VF episodes recorded in implantable cardioverter defibrillator （ICD） or pacemakers） was 13.4 ~ 5.6 per-year in group I, 10.8 ~ 3.9 per-year in group II, and 9.8 -4- 4.2 per-year in group HI. There were significant differences in incidences among the three groups, the most frequent index events were observed in group I. The hazard ratio for incidence was 3.2 （95%CI, 1.1-7.9; P = 0.01）. The history and circumstance of the index events were different among the groups. In group I, all the index events occurred during sleep in early morning. In group II, four subjects suffered VF during strenuous physical activities or agitation state, two during sleep in early morning, three in usual activity. In group III, one subject suffered VF during sleep in early morning, one in agitation state, four in usual activity. Conclusions This study suggests that the IVF patients with the combined appearance of f-QRS and J wave in the resting ECG suffer an increased risk of VF, this subgroup of IVF patients has a unique clinical feature.

The history, hotspots, and trends of electrocardiogram

The electrocardiogram （ECG） has broad applications in clinical diagnosis and prognosis of cardiovascular disease. Many researchers have contributed to its progressive development. To commemorate those pioneers, and to better study and promote the use of ECG, we reviewed and present here a systematic introduction about the history, hotspots, and trends of ECG. In the historical part, information including the invention, improvement, and extensive applications of ECG, such as in long QT syndrome （LQTS）, angina, and myocardial infarction （MI）, are chronologi- cally presented. New technologies and applications from the 1990s are also introduced. In the second part, we use the bibliometric analysis me- thod to analyze the hotspots in the field of ECG-related research. By using total citations and year-specific total citations as our main criteria, four key hotspots in ECG-related research were identified from 11 articles, including atrial fibrillation, LQTS, angina and MI, and heart rate variability. Recent studies in those four areas are also reported. In the final part, we discuss the future trends concerning ECG-related research. The authors believe that improvement of the ECG instrumentation, big data mining for ECG, and the accuracy of diagnosis and application will be areas of continuous concern.

Brugada phenocopy: A new electrocardiogram phenomenon

Brugada phenocopies(BrP) are clinical entities that are etiologically distinct from true congenital Brugada syndrome. BrP are characterized by type 1 or type 2 Brugada electrocardiogram(ECG) patterns in precordial leads V1-V3. However, BrP are elicited by various un-derlying clinical conditions such as myocardial ischemia, pulmonary embolism, electrolyte abnormalities, or poor ECG filters. Upon resolution of the inciting underlying pathological condition, the BrP ECG subsequently nor-malizes. To date, reports have documented BrP in the context of singular clinical events. More recently, recur-rent BrP has been demonstrated in the context of re-current hypokalemia. This demonstrates clinical repro-ducibility, thereby advancing the concept of this new ECG phenomenon. The key to further understanding the pathophysiological mechanisms behind BrP requires experimental model validation in which these phenom-ena are reproduced under strictly controlled environ-mental conditions. The development of these validation models will help us determine whether BrP are tran-sient alterations of sodium channels that are not repro-ducible with a sodium channel provocative test or al-ternatively, a malfunction of other ion channels. In this editorial, we discuss the conceptual emergence of BrP as a new ECG phenomenon, review the progress made to date and identify opportunities for further investiga-tion. In addition, we also encourage investigators that are currently reporting on these cases to use the term BrP in order to facilitate literature searches and to help establish this emerging concept.

Coronary angiography findings in cardiac arrest patients with non-diagnostic post-resuscitation electrocardiogram:A comparison of shockable and non-shockable initial rhythms

AIM To investigate the impact of coronary artery disease in a cohort of patients resuscitated from cardiac arrest with non-diagnostic electrocardiogram.METHODS From March 2004 to February 2016, 203 consecutive patients resuscitated from in or out-of-hospital sudden cardiac arrest and non-diagnostic post-resuscitation electrocardiogram(defined as ST segment elevation or pre-sumably new left bundle branch block) whounderwent invasive coronary angiogram during hospitalization were included. For purpose of analysis and comparison, patients were classified in two groups: Initial shockable rhythm(ventricular tachycardia or ventricular fibrillation; n = 148, 72.9%) and initial non-shockable rhythm(n = 55, 27.1%). Baseline characteristics, coronary angiogram findings including Syntax Score and long-term survival rates were compared. RESULTS Sudden cardiac arrest was witnessed in 95.2% of cases, 66.7% were out-of-hospital patients and 72.4% were male. There were no significant differences in baseline characteristics between groups except for higher mean age(68.1 years vs 61 years, P = 0.001) in the nonshockable rhythm group. Overall 5-year mortality of the resuscitated patients was 37.4%. Patients with non-shockable rhythms had higher mortality(60% vs 29.1%, P < 0.001) and a worst neurological status at hospital discharge based on cerebral performance category score(CPC 1-2: 32.7% vs 53.4%, P = 0.02). Although there were no significant differences in global burden of coronary artery disease defined by Syntax Score(mean Syntax Score: 10.2 vs 10.3, P = 0.96) there was a trend towards a higher incidence of acute coronary lesions in patients with shockable rhythm(29.7% vs 16.4%, P = 0.054). There was also a higher need for ad-hoc percutaneous coronary intervention in this group(21.9% vs 9.1%, P = 0.03). CONCLUSION Initial shockable group of patients had a trend towards higher incidence of acute coronary lesions and higher need of ad-hoc percutaneous intervention vs nonshockable group.

Automatic detection of respiratory rate from electrocardiogram,respiration induced plethysmography and 3D acceleration signals

Respiratory monitoring is increasingly used in clinical and healthcare practices to diagnose chronic cardio-pulmonary functional diseases during various routine activities.Wearable medical devices have realized the possibilities of ubiquitous respiratory monitoring,however,relatively little attention is paid to accuracy and reliability.In previous study,a wearable respiration biofeedback system was designed.In this work,three kinds of signals were mixed to extract respiratory rate,i.e.,respiration inductive plethysmography(RIP),3D-acceleration and ECG.In-situ experiments with twelve subjects indicate that the method significantly improves the accuracy and reliability over a dynamic range of respiration rate.It is possible to derive respiration rate from three signals within mean absolute percentage error 4.37%of a reference gold standard.Similarly studies derive respiratory rate from single-lead ECG within mean absolute percentage error 17%of a reference gold standard.

Early detection of sudden cardiac death by using classical linear techniques and time-frequency methods on electrocardiogram signals

Early detection of sudden cardiac death may be used for surviving the life of cardiac patients. In this paper we have investigated an algorithm to detect and predict sudden cardiac death, by processing of heart rate variability signal through the classical and time-frequency methods. At first, one minute of ECG signals, just before the cardiac death event are extracted and used to compute heart rate variability (HRV) signal. Five features in time domain and four features in frequency domain are extracted from the HRV signal and used as classical linear features. Then the Wigner Ville transform is applied to the HRV signal, and 11 extra features in the time-frequency (TF) domain are obtained. In order to improve the performance of classification, the principal component analysis (PCA) is applied to the obtained features vector. Finally a neural network classifier is applied to the reduced features. The obtained results show that the TF method can classify normal and SCD subjects, more efficiently than the classical methods. A MIT-BIH ECG database was used to evaluate the proposed method. The proposed method was implemented using MLP classifier and had 74.36% and 99.16% correct detection rate (accuracy) for classical features and TF method, respectively. Also, the accuracy of the KNN classifier were 73.87% and 96.04%.

Brugada type 1 electrocardiogram:Should we treat the electrocardiogram or the patient?

Patients with a Brugada type 1 electrocardiogram(ECG) pattern may suffer sudden cardiac death(SCD). Recognized risk factors are spontaneous type 1 ECG and syncope of presumed arrhythmic origin. Familial sudden cardiac death(f-SCD) is not a recognized independent risk factor. Finally, positive electrophysiologic study(+EPS) has a controversial prognostic value. Current ESC guidelines recommend implantable cardioverter defibrillator(ICD) implantation in patients with a Brugada type 1 ECG pattern if they have suffered a previous resuscitated cardiac arrest(class Ⅰrecommendation) or if they have syncope of presumed cardiac origin(class Ⅱa recommendation). In clinical practice, however, many other patients undergo ICD implantation despite the suggestions of the guidelines. In a 2014 cumulative analysis of the largest available studies(including over 2000 patients), we found that 1/3 of patients received an ICD in primary prevention. Interestingly, 55% of these latter were asymptomatic, while 80% had a + EPS. This means that over 30% of subjects with a Brugada type 1 ECG pattern were considered at high risk of SCD mainly on the basis of EPS, to which a class Ⅱb indication for ICD is assigned by the current ESC guidelines. Follow-up data confirm that in clinical practice single, and often frail, risk factors overestimate the real risk in subjects with the Brugada type 1 ECG pattern. We can argue that, in clinical practice, many cardiology centers adopt an aggressive treatment in subjects with a Brugada type 1 ECG pattern who are not at high risk. As a result, many healthy persons may be treated in order to save a few patients with a true Brugada Syndrome. Better risk stratification is needed. A multi-parametric approach that considers the contemporary presence of multiple risk factors is a promising one.

Preliminary abnormal electrocardiogram segment screening method for Holter data based on long short-term memory networks

Holter usually monitors electrocardiogram(ECG)signals for more than 24 hours to capture short-lived cardiac abnormalities.In view of the large amount of Holter data and the fact that the normal part accounts for the majority,it is reasonable to design an algorithm that can automatically eliminate normal data segments as much as possible without missing any abnormal data segments,and then take the left segments to the doctors or the computer programs for further diagnosis.In this paper,we propose a preliminary abnormal segment screening method for Holter data.Based on long short-term memory(LSTM)networks,the prediction model is established and trained with the normal data of a monitored object.Then,on the basis of kernel density estimation,we learn the distribution law of prediction errors after applying the trained LSTM model to the regular data.Based on these,the preliminary abnormal ECG segment screening analysis is carried out without R wave detection.Experiments on the MIT-BIH arrhythmia database show that,under the condition of ensuring that no abnormal point is missed,53.89% of normal segments can be effectively obviated.This work can greatly reduce the workload of subsequent further processing.

Electrocardiogram manifestations in hyperkalemia

Hyperkalemia is defined as serum potassium level of more than 5 mmol/L. Prompt identification of hyper-kalemia and appropriate management are critical, since severe hyperkalemia can lead to lethal cardiac dysrhythmias. There is a wide range of electrocardiogram (EKG) changes associated with hyperkalemia. The sequence of EKG changes has been previously described with limited information to correlate the level of potassium to a particular change in the EKG. This study aims to describe a correlation between the level of potassium and EKG changes in the presence or absence of certain diagnoses, to determine which EKG finding in the context of level of hyperkalemia, should be considered life-threatening and prompt emergency intervention. If a relationship between serum levels of potassium and EKG changes is significant, clinicians may be able to better monitor and treat hyperkalemic patients. This paper reviews the literature on hyperkalemia, potassium homeostasis and EKG changes attributed to elevated potassium.

Electrocardiogram changes during hematoma enlargement in intracerebral hemorrhage patients

BACKGROUND： It has been reported that cerebrovascular disease causes changes in electrocardiogram results. OBJECTIVE： To investigate changes in electrocardiogram results in patients with intracerebral bematoma enlargement. DESIGN, TIME AND SETTING： The present case-retrospective analysis study was performed at the Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from January 2005 to October 2006. PARTICIPANTS： A total of 225 intracerebral hemorrhage patients （142 males and 83 females） that were hospitalized at the Department of Neurology were enrolled the present study. The patient selection was in accordance with diagnostic criteria from the Fourth National Cerebrovascular Disease Congress of China in 1995, and diagnosis was confirmed using computed tomography. All patients underwent computed tomography twice within 24 hours following intracerebral hemorrhage, and were subjected to electrocardiogram examination after admission. METHODS： According to hematoma enlargement following intracerebral hemorrhage, all patients were divided into hematoma enlargement （n = 20） and non-hematoma enlargement （n = 205） groups. Because of the large patient number difference between the two groups, the hematoma enlargement group was matched with the non-hematoma enlargement group. Patients meeting these conditions were included in the non-hematoma enlargement group. Finally, 75 patients were included in the final analysis, 19 in the hematoma enlargement group and 56 in the non-hematoma enlargement group. Clinical data from the two groups were statistically analyzed. MAIN OUTCOME MEASURES： The incidence of electrocardiographic abnormalities between the hematoma enlargement and non-hematoma enlargement groups. RESULTS： In the hematoma enlargement group, 15 patients （79%） developed electrocardiographic abnormafities. In the non-hematoma enlargement group, 24 patients （43%） presented with electrocardiographic abnormalities. There were significant differences in electrocardiographic abnormalities between the groups （P 〈 0.01）. CONCLUSION： Patients with electrocardiographic abnormalities suffered from hematoma enlargement following admission.

Electrocardiograms changes in children with functional gastrointestinal disorders on low dose amitriptyline

AIM: To study the effects of low dose amitriptyline on cardiac conduction in children.

An unexpected electrocardiogram sign of subacute left ventricular free wall rupture: Its early awareness may be lifesaving

BACKGROUND:Post-infarct left ventricular free wall rupture(LVFWR)is not always an immediately catastrophic complication.The rupture can be subacute,allowing time for diagnosis and intervention.Accordingly,early recognition of the entity may be lifesaving.METHODS:We present an electrocardiogram(ECG)change pattern in two cases,which was erroneously attributed to ischemia.Two women in their 80s were admitted to our institute after experiencing the sudden onset of chest pain.They were managed as anterior ST-segment elevation myocardial infarction without reperfusion treatment.Unfortunately,they experienced a recurrence of severe chest pain with cardiogenic shock during hospitalisation.The ECG recorded at that time showed a ST-segment re-elevation in infract-related leads.RESULTS:The two cases were regrettably received a misjudgement of reinfarction at first,and one of the patients even was administrated with tirofi ban.Afterwards the diagnosis of subacute LVFWR was made through antemortem echocardiography.CONCLUSION:New ST-segment elevation(STE)in infarct-associated leads,coupled with recurrence of chest pain and new-onset hypotension,may constitute the premonitory signs of a subacute LVFWR.