Electrocardiogram Recognition Research Articles

Application of Extension Neural Network Type 2 and Chaos Theory to the Electrocardiogram Recognition System

Application of Extension Neural Network Type 2 and Chaos Theory to the Electrocardiogram Recognition System

A Pathognomonic Electrocardiogram That Requires Urgent Percutaneous Intervention: A Case of Wellens Syndrome in a Previously Healthy 55-Year-Old Male

Patient: Male, 55Final Diagnosis: Wellens’ syndromeSymptoms: Chest painMedication: —Clinical Procedure: —Specialty: CardiologyObjective:Rare diseaseBackground:Acute chest pain is a common presentation in emergency departments worldwide. Ruling out acute coronary syndrome is essential in ensuring patient safety. Workup includes electrocardiogram (ECG) and cardiac biomarkers. Wellens syndrome is characterized by a history of chest pain, normal or minimally elevated biomarkers, no STEMI/Q-waves, and specific ECG changes. These changes consist of biphasic T waves in lead V2 and V3 or deep symmetrically inverted T waves in leads V1–V4.Case Report:A 55-year-old male presented to the emergency department with acute chest pain in a background of active smoking, hypertension, and hyperlipidemia. His ECG was characteristic of Wellens syndrome type 1 and negative cardiac biomarkers. His TIMI (thrombolysis in myocardial infarction) score was 2, however, he failed conservative management necessitating urgent coronary angiogram. Critical stenosis of the proximal left anterior descending (LAD) coronary artery was found which required 2 drug eluting stents. He was discharged home asymptomatic on optimal medical therapy.Conclusions:Conventional management of patients with NSTEMI (non-ST-elevation myocardial infarction) and unstable angina with risk stratification utilizing TIMI score may not be appropriate in patient with Wellens syndrome. This highlights the importance of ECG recognition and urgent percutaneous intervention in patients with Wellens syndrome. Failure to identify this clinical syndrome could result in significant morbidity and mortality because it relates to critical stenosis and imminent large myocardial infarction.

Arrhythmia recognition and classification through deep learning-based approach

Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, which can be life-threatening. Electrocardiogram (ECG) is the principal diagnostic tool used to detect arrhythmias or heart abnormalities. It contains information about the different types of arrhythmias. However, due to the complexity and nonlinearity of ECG signals, such as the presence of noise, the time dependence of ECG signals and the irregularity of the heartbeat, it is troublesome to analyse ECG signals manually. Moreover, the interpretation of ECG signals is subjective and might vary among experts in the field. Therefore, an automatic, high-precision ECG recognition method is important to arrhythmia detection. For such, a method is proposed in this paper for arrhythmia classification, which is based on deep learning-based approach long short-term memory (LSTM), where five classes of arrhythmias as recommended by the Association for Advancement of Medical Instrumentation (AAMI) are analysed. The method has been tested on the MIT-BIH arrhythmia database with a number of useful performance evaluation measures, showing that is a promising and better performance than other artificial intelligence methods used.

Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine

Electrocardiogram (ECG) classification is an important process in identifying arrhythmia, and neural network models have been widely used in this field. However, these models are often disrupted by heartbeat noise and are negatively affected by skewed data. To address these problems, a novel heartbeat recognition method is presented. The aim of this study is to apply a principal component analysis network (PCANet) for feature extraction based on a noisy ECG signal. To improve the classification speed, a linear support vector machine (SVM) was applied. In our experiments, we identified five types of imbalanced original and noise-free ECGs in the MIT-BIH arrhythmia database to verify the effectiveness of our algorithm and achieved 97.77% and 97.08% accuracy, respectively. The results show that our method has high recognition accuracy in the classification of skewed and noisy heartbeats, indicating that our method is a practical ECG recognition method with suitable noise robustness and skewed data applicability.

Feature Selection for Nonstationary Data: Application to Human Recognition Using Medical Biometrics.

Electrocardiogram (ECG) and transient evoked otoacoustic emission (TEOAE) are among the physiological signals that have attracted significant interest in biometric community due to their inherent robustness to replay and falsification attacks. However, they are time-dependent signals and this makes them hard to deal with in across-session human recognition scenario where only one session is available for enrollment. This paper presents a novel feature selection method to address this issue. It is based on an auxiliary dataset with multiple sessions where it selects a subset of features that are more persistent across different sessions. It uses local information in terms of sample margins while enforcing an across-session measure. This makes it a perfect fit for aforementioned biometric recognition problem. Comprehensive experiments on ECG and TEOAE variability due to time lapse and body posture are done. Performance of the proposed method is compared against seven state-of-the-art feature selection algorithms as well as another six approaches in the area of ECG and TEOAE biometric recognition. Experimental results demonstrate that the proposed method performs noticeably better than other algorithms.

Arrhythmia recognition and classification through deep learning based approach

Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, which can be life-threatening. Electrocardiogram (ECG) is the principal diagnostic tool used to detect arrhythmias or heart abnormalities. It contains information about the different types of arrhythmias. However, due to the complexity and nonlinearity of ECG signals, such as the presence of noise, the time dependence of ECG signals and the irregularity of the heartbeat, it is troublesome to analyse ECG signals manually. Moreover, the interpretation of ECG signals is subjective and might vary among experts in the field. Therefore, an automatic, high-precision ECG recognition method is important to arrhythmia detection. For such, a method is proposed in this paper for arrhythmia classification, which is based on deep learning-based approach long short-term memory (LSTM), where five classes of arrhythmias as recommended by the Association for Advancement of Medical Instrumentation (AAMI) are analysed. The method has been tested on the MIT-BIH arrhythmia database with a number of useful performance evaluation measures, showing that is a promising and better performance than other artificial intelligence methods used.

An Approach of Neural Network For Electrocardiogram Classification

ECG is basically the graphical representation of the electrical activity of cardiac muscles during contraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to this early detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated by the cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). It acts as a vital physiological parameter, which is being used exclusively to know the state of the cardiac patients. Feature extraction of ECG plays a vital role in the manual as well as automatic analysis of ECG. In this paper the study of the concept of pattern recognition of ECG is done. It refers to the classification of data patterns and characterizing them into classes of predefined set. The analysis ECG signal falls under the application of pattern recognition. The ECG signal generated waveform gives almost all information about activity of the heart. The ECG signal feature extraction parameters such as spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includes artificial neural network as a classifier for identifying the abnormalities of heart disease.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

Independent Component Analysis Methods to Improve Electrocardiogram Patterns Recognition in the Presence of Non-Trivial Artifacts

 Abstract—Electrocardiogram (ECG) signals are affected by various kinds of noise and artifacts that may impede correct recognition by automated monitoring or diagnosis systems. Independent component analysis (ICA) is considered as a new technique suitable for the separation and removal of diverse noises independent of ECG signals. This paper first proposes the application of independent component analysis to ECG signal pre-processing and then compares the performances of two major types of ICAs namely Infomax and Fast ICAs in ECG signal de-noising. The annotated benchmark samples from MIT-BIH arrhythmia database are used for experiments. We compare the signal to noise ratio improvements in the real ECG data with different ICA algorithms and the recognition rates. It is found that both types of ICA can effectively improve the ECG recognition in the presence of non-trivial artifacts, but FastICA slightly outperforms. However, it is worth mentioning that the Infomax algorithm might be further optimized.

4 Improving Undergraduate and Postgraduate Medical Trainees Confidence and Competence in Electrocardiogram Interpretation: Abstract 4 Table 1

Introduction The Electrocardiogram (ECG) is the most commonly used diagnostic test in modern medicine. 1 Competency in ECG interpretation is central to undergraduate and postgraduate clinical training and minimising potential adverse consequences on patient care. Several studies have demonstrated postgraduates interpret ECG’s sub-optimally. 2,3 Our study compares the effectiveness of two cost neutral learning strategies to improve competence and confidence in this core clinical skill. Method A prospective randomised study was carried out over a one-month period comparing the learning strategies in two cohorts: undergraduate 3 rd year medical students and postgraduate foundation year one (FY1) doctors.Both had blinded randomisation to one of these strategies; focused teaching program (FTP) and self-directed learning (SDL). All volunteers completed a confidence questionnaire on ECG interpretation before and after completing the intervention. Additionally, all undertook an ECG recognition multiple choice question (MCQ) paper at the end of the learning period to assess competence. Results A total of 21 FY1’s and 25 3rd year medical students participated. The undergraduates in the FTP group demonstrated a significant difference in successfully interpreting ‘Ventricular Tachycardia’ (p = 0.046) and ‘Narrow Complex Tachycardia’ (p = 0.009) than the SDL group. Overall, participant confidence increased in both groups in each cohort. The FTP group demonstrated a greater improvement in confidence compared to SDL in both cohorts. Conclusion A dedicated teaching program can improve trainee confidence and competence in ECG interpretation. A larger benefit is observed in those undertaking a focused teaching program. ECG interpretation can be improved at any training level, especially undergraduates with a dedicated teaching program. References Rubinstein J, Dhole A, Ferenchick G. Puzzle based teaching versus traditional instruction in electrocardiogram interpretation for medical students – a pilot study. BMC Medical Education 2009; 9(4):1–7 Sur D, Kaye L, Mikus M, Goad J, Morena A. Accuracy of electrocardiogram reading by family practice residents. Fam Med 2000; 32(5): 315–9 Jensen MSA, Thomasen JL, Jensen SE, Lauritzen T, Engberg M. Electrocardiogram interpretation in general practice. Family Practice 2005; 22:109–113

Improving ECG Competence in Medical Trainees in a UK District General Hospital.

BackgroundCompetency in electrocardiogram (ECG) interpretation is central to undergraduate and postgraduate clinical training. Studies have demonstrated ECGs are interpreted sub-optimally. Our study compares the effectiveness of two learning strategies to improve competence and confidence.MethodA 1-month prospective randomized study compared the strategies in two cohorts: undergraduate third year medical students and postgraduate foundation year one (FY1) doctors. Both had blinded randomization to one of these learning strategies: focused teaching program (FTP) and self-directed learning (SDL). All volunteers completed a confidence questionnaire before and after allocation learning strategy and an ECG recognition multiple choice question (MCQ) paper at the end of the learning period.ResultsThe FTP group of undergraduates demonstrated a significant difference in successfully interpreting “ventricular tachycardia” (P = 0.046) and “narrow complex tachycardia” (P = 0.009) than the SDL group. Participant confidence increased in both learning strategies. FTP confidence demonstrated a greater improvement than SDL for both cohorts.ConclusionA dedicated teaching program can improve trainee confidence and competence in ECG interpretation. A larger benefit is observed in undergraduates and those undertaking a FTP.

Diagnosis and Management of Idiopathic Ventricular Tachycardia

Idiopathic ventricular tachycardia (VT) refers to VT occurring in structurally normal hearts. It is commonly seen in young patients and typically has a benign course. Because the origin is typically focal and the heart is without scar, the 12-lead electrocardiogram is extremely useful for localizing the origin of idiopathic VT. Treatment options include reassurance, medical therapy, and catheter ablation. This review describes the clinical features, electrocardiogram recognition, and management of idiopathic monomorphic VT.

A DNA Algorithm for the Electrocardiogram Recognition

A DNA Algorithm for the Electrocardiogram Recognition

A Journal of Real Peak Recognition of Electrocardiogram (ECG) Signals Using Neural Network

This paper describes about the analysis of electrocardiogram (ECG) signals using neural network approach. Heart structure is a unique system that can generate ECG signals independently via heart contraction. Basically, an ECG signal consists of PQRST wave. All these waves are represented respective heart functions. Normal healthy heart can be simply recognized by normal ECG signal while heart disorder or arrhythmias signals contain differences in terms of features and morphological attributes in their corresponding ECG waveform. Some major important features will be extracted from ECG signals such as amplitude, duration, pre-gradient, post-gradient and so on. These features will then be fed as an input to neural network system. The target output represented real peaks of the signals is also being defined using a binary number. Result obtained showing that neural network pattern recognition is able to classify and recognize the real peaks accordingly with overall accuracy of 81.6% although there might be limitations and misclassification happened. Future recommendations have been highlighted to improve network’s performance in order to get better and more accurate result.

ECG Biometric Recognition: A Comparative Analysis

The electrocardiogram (ECG) is an emerging biometric modality that has seen about 13 years of development in peer-reviewed literature, and as such deserves a systematic review and discussion of the associated methods and findings. In this paper, we review most of the techniques that have been applied to the use of the electrocardiogram for biometric recognition. In particular, we categorize the methodologies based on the features and the classification schemes. Finally, a comparative analysis of the authentication performance of a few of the ECG biometric systems is presented, using our inhouse database. The comparative study includes the cases where training and testing data come from the same and different sessions (days). The authentication results show that most of the algorithms that have been proposed for ECG-based biometrics perform well when the training and testing data come from the same session. However, when training and testing data come from different sessions, a performance degradation occurs. Multiple training sessions were incorporated to diminish the loss in performance. That notwithstanding, only a few of the proposed ECG recognition algorithms appear to be able to support performance improvement due to multiple training sessions. Only three of these algorithms produced equal error rates (EERs) in the single digits, including an EER of 5.5% using a method proposed by us.

Continuing Professional Development: Cocaine toxicity: assessment and cardiac risk

Overview The UK has the highest prevalence of drug use within Europe, with a 13% increase in cocaine and ecstasy related deaths between 2004–2005. This is significant to emergency medical personnel as cocaine toxicity may present clinically as acute coronary syndrome (ACS) minus typical associated risk factors. Cocaine use has an immediate effect on the body within seconds to minutes of administration. The resultant effect is manifold and can be divided into the two broad categories of central nervous system (CNS) and cardiovascular. Cocaine has a strong association as a trigger of ACS, acute myocardial infarction (MI) and sudden death in a population of patients largely free of classic cardiovascular risk factors. This module will assist in understanding the effects of acute cocaine toxicity, recognizing electrocardiogram (ECG) changes and implementing an appropriate treatment plan. Learning Outcomes After completing this module you will be able to: ▪ Refresh your understanding of the anatomy and physiology of cardio-vasculature and the central nervous system (CNS) ▪ Have an understanding of the effect of cocaine on the body, and how this may present in the clinical setting ▪ Further develop ECG recognition in the symptomatic patient, and understand the consequences of significant cardiac changes. ▪ Explore the concept of critical thinking within clinical decision-making ▪ Be able to better anticipate and implement a treatment plan in accordance to JRCALC guidelines.

Electrocardiogram Recognition and Ablation of Atrial Tachycardia

Focal atrial tachycardia (AT) is a relatively uncommon cause of supraventricular tachycardia, but when present is frequently difficult to treat medically. Atrial tachycardias tend to originate from anatomically determined atrial sites. The P-wave morphology on surface electrocardiogram (ECG) together with more sophisticated contemporary mapping techniques facilitates precise localisation and ablation of these ectopic foci. Catheter ablation of focal AT is associated with high long-term success and may be viewed as a primary treatment strategy in symptomatic patients.

Electrocardiogram Recognition and Ablation of Atrial Tachycardia

Focal atrial tachycardia (AT) is a relatively uncommon cause of supraventricular tachycardia, but when present is frequently difficult to treat medically. Atrial tachycardias tend to originate from anatomically determined atrial sites. The P-wave morphology on surface electrocardiogram (ECG) together with more sophisticated contemporary mapping techniques facilitates precise localisation and ablation of these ectopic foci. Catheter ablation of focal AT is associated with high long-term success and may be viewed as a primary treatment strategy in symptomatic patients.

Kernel-based nonlinear dimensionality reduction for electrocardiogram recognition

The human heart is a complex system that reveals many clues about its condition in its electrocardiogram (ECG) signal, and ECG supervising is the most important and efficient way of preventing heart attacks. ECG analysis and recognition are both important and tempting topics in modern medical research. The purpose of this paper is to develop an algorithm which investigates kernel method, locally linear embedding (LLE), principal component analysis (PCA), and support vector machine(SVM) algorithms for dimensionality reduction, features extraction, and classification for recognizing and classifying the given ECG signals. In order to do so, a nonlinear dimensionality reduction kernel method based LLE is proposed to reduce the high dimensions of the variational ECG signals, and the principal characteristics of the signals are extracted from the original database by means of the PCA, each signal representing a single and complete heart beat. SVM method is applied to classify the ECG data into several categories of heart diseases. Experimental results obtained demonstrated that the performance of the proposed method was similar and sometimes better when compared to other ECG recognition techniques, thus indicating a viable and accurate technique.

Idiopathic Ventricular Tachycardia: Diagnosis and Management

Idiopathic ventricular tachycardia (VT) is an uncommon form of VT that is seen in patients without structural heart disease. It is commonly seen in young patients and usually has a benign course. Recent studies have delineated the mechanisms and anatomical locations of this form of VT. Recognition of various forms of idiopathic VT based on characteristic QRS morphology from the 12-lead electrocardiogram (ECG) has important prognostic and therapeutic implications. The understanding of the mechanisms of idiopathic VT has led to the use of specific antiarrhythmic drugs targeting particular arrhythmias. Recent technological advances in the field of mapping and catheter ablation have led to a suitable alternative to drug therapy with a very high cure rate. This review describes the clinical features, ECG recognition, and management of idiopathic monomorphic VT.