Electrocardiogram Recognition Research Articles

Abnormal recognition-assisted and onset-offset aware network for pathological wearable ECG delineation

Electrocardiogram (ECG) delineation is essential to the identification of abnormal cardiac status, especially when ECG signals are remotely monitored with wearable devices. The complexity and diversity of cardiac conditions generate numerous pathological ECG patterns, not only requiring the recognition of normal ECG but also addressing an extensive range of abnormal ECG patterns, posing a challenging task. Therefore, we propose an abnormal recognition-assisted network to integrate supplementary information on diverse ECG patterns. Simultaneously, we design an onset-offset aware loss to enhance precise waveform localization. Specifically, we establish a two-branch framework where ECG delineation serves as the target task, producing the final segmentation results. Additionally, the abnormal recognition-assisted network serves as an auxiliary task, extracting multi-label pathological information from ECGs. This joint learning approach establishes crucial correlations between ECG delineation and associated ECG abnormalities. The correlations enable the model to demonstrate sufficient generalization in the presence of diverse abnormal ECG patterns. Besides, onset-offset aware loss focuses intensively on wave onsets and offsets by applying biased weights to various waveform positions. This approach ensures a focus on precise localization, facilitating seamless integration into cross-entropy loss function. A large-scale wearable 12-lead dataset containing 4,913 signals is collected, offering an extensive range of ECG data for model training. Results demonstrate that our method achieves outstanding performance on two test datasets, attaining sensitivity of 94.97% and 94.27% and an error tolerance lower than 20 ms. Furthermore, our method is effective for various aberrant ECG signals, including ST-segment changes, atrial premature beats, and right and left bundle branch blocks.

Analyzing the impact of transfer learning on explanation accuracy in deep learning-based ECG recognition systems

Abstract Electrocardiogram (ECG) recognition systems now play a leading role in the early detection of cardiovascular diseases. However, the explanation of judgments made by deep learning models in these systems is prominent for clinical acceptance. This article reveals the effect of transfer learning in ECG recognition systems on decision precision. This article investigated the role of transfer learning in ECG image classification using a customized convolutional neural network (CNN) with and without a VGG16 architecture. The customized CNN model with the VGG16 achieved a good test accuracy of 98.40%. Gradient-weighted class activation mapping (Grad-CAM), for this model, gave the wrong information because it focused on parts of the ECG that were not important for making decisions instead of features necessary for clinical diagnosis, like the P wave, QRS complex, and T wave. A proposed model that only used customized CNN layers and did not use transfer learning performed 99.08% on tests gave correct Grad-CAM explanations and correctly identified the influencing areas of decision-making in the ECG image. Because of these results, it seems that transfer learning might provide good performance metrics, but it might also make things harder to understand, which could make it harder for deep learning models that use ECG recognition to be reliable for diagnosis. This article concludes with a call for careful consideration when using transfer learning in the medical field, as model explanations resulting from such learning may not be appropriate when it comes to domain-specific interpretations.

An Ensemble Deep Neural Network-Based Method for Person Identification Using Electrocardiogram Signals Acquired on Different Days

Electrocardiogram (ECG) signals are a measure minute electrical signals generated during the cardiac cycle, a biometric signal that occurs during vital human activity. ECG signals are susceptible to various types of noise depending on the data acquisition conditions, with factors such as sensor placement and the physiological and mental states of the subject contributing to the diverse shapes of these signals. When the data are acquired in a single session, the environmental variables are relatively similar, resulting in similar ECG signals; however, in subsequent sessions, even for the same person, changes in the environmental variables can alter the signal shape. This phenomenon poses challenges for person identification using ECG signals acquired on different days. To improve the performance of individual identification, even when ECG data is acquired on different days, this paper proposes an ensemble deep neural network for person identification by comparing and analyzing the ECG recognition performance under various conditions. The proposed ensemble deep neural network comprises three streams that incorporate two well-known pretrained models. Each network receives the time-frequency representation of ECG signals as input, and a stream reuses the same network structure under different learning conditions with or without data augmentation. The proposed ensemble deep neural network was validated on the Physikalisch-Technische Bundesanstalt dataset, and the results confirmed a 3.39% improvement in accuracy compared to existing methods.

Securing Internet-of-Medical-Things networks using cancellable ECG recognition

Reinforcement of the Internet of Medical Things (IoMT) network security has become extremely significant as these networks enable both patients and healthcare providers to communicate with each other by exchanging medical signals, data, and vital reports in a safe way. To ensure the safe transmission of sensitive information, robust and secure access mechanisms are paramount. Vulnerabilities in these networks, particularly at the access points, could expose patients to significant risks. Among the possible security measures, biometric authentication is becoming a more feasible choice, with a focus on leveraging regularly-monitored biomedical signals like Electrocardiogram (ECG) signals due to their unique characteristics. A notable challenge within all biometric authentication systems is the risk of losing original biometric traits, if hackers successfully compromise the biometric template storage space. Current research endorses replacement of the original biometrics used in access control with cancellable templates. These are produced using encryption or non-invertible transformation, which improves security by enabling the biometric templates to be changed in case an unwanted access is detected. This study presents a comprehensive framework for ECG-based recognition with cancellable templates. This framework may be used for accessing IoMT networks. An innovative methodology is introduced through non-invertible modification of ECG signals using blind signal separation and lightweight encryption. The basic idea here depends on the assumption that if the ECG signal and an auxiliary audio signal for the same person are subjected to a separation algorithm, the algorithm will yield two uncorrelated components through the minimization of a correlation cost function. Hence, the obtained outputs from the separation algorithm will be distorted versions of the ECG as well as the audio signals. The distorted versions of the ECG signals can be treated with a lightweight encryption stage and used as cancellable templates. Security enhancement is achieved through the utilization of the lightweight encryption stage based on a user-specific pattern and XOR operation, thereby reducing the processing burden associated with conventional encryption methods. The proposed framework efficacy is demonstrated through its application on the ECG-ID and MIT-BIH datasets, yielding promising results. The experimental evaluation reveals an Equal Error Rate (EER) of 0.134 on the ECG-ID dataset and 0.4 on the MIT-BIH dataset, alongside an exceptionally large Area under the Receiver Operating Characteristic curve (AROC) of 99.96% for both datasets. These results underscore the framework potential in securing IoMT networks through cancellable biometrics, offering a hybrid security model that combines the strengths of non-invertible transformations and lightweight encryption.

An improved Bi-LSTM method based on heterogeneous features fusion and attention mechanism for ECG recognition

Electrocardiogram (ECG) plays a critical role in early prevention and diagnosis of cardiovascular diseases. However, extracting powerful deep features from ECG signal for recognition is still a challenging problem today due to the variable abnormal rhythms and noise distribution. This work proposes a Bi-LSTM algorithm based on heterogeneous features fusion and attention mechanism (HFFAM + Bi-LSTM). Combining the empirical features and the features learned by the deep learning network, HFFAM + Bi-LSTM can comprehensively extract the temporal frequency information and spatial structure information of the ECG signal. Meanwhile, a novel attention mechanism based on improved DTW (AM-DTW) is designed to analyze and control the fusion process of features. The role of AM-DTW in HFFAM + Bi-LSTM is twofold, one is to measure the feature similarity between ECG signal sets with different labels using the improved DTW, and the other is to distinguish the features into isomorphic and heterogeneous features as well as adaptive weighting of the features. It is worth mentioning that overly similar isomorphic features are filtered out to further optimize the algorithm. Thus, HFFAM + Bi-LSTM has the advantage of strengthening the heterogeneous information in the feature subspace while accounting for the isomorphic features. The accuracy of HFFAM + Bi-LSTM reaches up to 98.1 % and 97.1 % on the simulated and real datasets, respectively. Compared to the all benchmark models, the classification accuracy of HFFAM + Bi-LSTM is 1.3 % higher than the best. The experiments also demonstrate that HFFAM + Bi-LSTM has better performance compared with existing methods, which provides a new scheme for automatic detection of ECG signal.

An Evaluation of Nurse Anesthetists’ Acquisition and Retention of Knowledge and Skill Performance for Brief Basic Life Support and Advanced Cardiac Life Support Training

OBJECTIVE The purpose of this study was to evaluate the acquisition and retention of knowledge and skills of basic (BLS) and advanced cardiac life support (ACLS) for nurse anesthetists at Maharaj Nakorn Chiang Mai Hospital. METHODS The one-group pretest-posttest design study was used. BLS and ACLS training comprised a brief BLS and ACLS review and practice. Knowledge was assessed by multiple-choice questions. The BLS skills were assessed by observing CPR performance on a Resusci- Anne-skill meter manikin and ACLS skills were evaluated using ACLS scenarios. The knowledge and skills were assessed before and after the training weekly for four weeks, and then at three months after the training ended. RESULTS A total of 38 nurse anesthetists participated in the study. There were significant increases in BLS and ACLS knowledge and skills after the training. However, there was short retention of BLS and ACLS knowledge and skills at three months after the training. Foreign body airway obstruction and assessing unresponsiveness were the two most common of BLS skills degradation. While the primary and secondary assessment, assessing ACLS algorithms, electrocardiogram recognition and electrical therapy were the common skills performance of ACLS skills degradation. CONCLUSIONS After the BLS and ACLS training, the knowledge and skills were significantly improved but there was short retention of knowledge and skills at three months after the training. Regular refreshment of CPR education is recommended to enhance the retention of knowledge and practice of nurse anesthetists KEYWORDS knowledge and skills retention, nurse anesthetists, acquisition, basic life support, advanced cardiac life support

A new intelligent ECG recognition approach based on CNN and improved ALO-SVM

Cardiovascular disease is one of the most common diseases, which seriously threatens people’s life and health. Therefore, cardiovascular disease prevention becomes one of the most attractive research topics in health care system design. Intelligent recognition of electrocardiogram (ECG) signals represents an effective method for rapid diagnosis and the evaluation of cardiovascular diseases in medicine. Realization and efficiency of the classification of ECG signals in real time play major roles in the detection of cardiovascular diseases. This paper is concerned with the proposition of an intelligent ECG signal recognition method based on a convolutional neural network (CNN) and support vector machines (SVM) with an improved antlion algorithm (ALO). First, the ECG signal is denoised and pre-processed by lifting the wavelet. Subsequently, CNN is used to extract the signal characteristics of the denoising signal, and the extracted signal characteristics are used as the input characteristics of the SVM. Finally, an improved ALO algorithm is used to optimize the relevant input functions of the SVM to achieve a better signal classification. In our algorithm, the performance is enhanced by improving the threshold estimation method of the lifting wavelet, to improve the filtering effect. The proposed CNN architecture is tested with multi-lead ECG signals from the MIT-BIH ECG signal data set. The results display that the method has obtained an average accuracy, sensitivity, and specificity values of \(99.97\%\), \(99.97\%\), and \(99.99\%\), respectively. Compared with the existing results, the proposed approach has a better recognition performance.

CRT-Net: A generalized and scalable framework for the computer-aided diagnosis of Electrocardiogram signals

Electrocardiogram (ECG) signals play critical roles in the clinical screening and diagnosis of many types of cardiovascular diseases. Despite the fact that deep neural networks have greatly facilitated the computer-aided diagnosis (CAD) in many clinical tasks, the variability and complexity of ECG in practical scenarios still pose significant challenges in diagnostic performance and clinical applications, especially under the current demand of medical cloud computing and remote diagnosis. In this paper, we propose a generalized and scalable framework for the clinical recognition of ECG. Considering the fact that hospitals generally record ECG signals in the form of graphic waves of 2-D images, we first extract the graphic waves of 12-lead images into numerical 1-D ECG signals by a proposed bi-directional connected method. Subsequently, a novel deep neural network, named CRT-Net, is designed to explore waveform features, morphological characteristics, and time-domain features of ECG by embedding convolution neural network (CNN), recurrent neural network (RNN), and transformer module in a scalable deep model, which is especially suitable in clinical scenarios with different lengths of ECG signals captured from different devices. The proposed framework is first evaluated on two widely investigated public repositories, demonstrating superior performance to the state-of-the-art. We also evaluate the effectiveness of the developed framework on clinically collected ECG images from a local hospital. Moreover, based on the proposed algorithms, we have developed a cloud-based ECG system for the computer-aided diagnosis of different cardiovascular diseases.

Quantitative analysis of heart rate variability parameter and mental stress index.

BackgroundCardiovascular disease not only occurs in the elderly but also tends to become a common social health problem. Considering the fast pace of modern life, quantified heart rate variability (HRV) indicators combined with the convenience of wearable devices are of great significance for intelligent telemedicine. To quantify the changes in human mental state, this article proposes an improved differential threshold algorithm for R-wave detection and recognition of electrocardiogram (ECG) signals.MethodsHRV is a specific quantitative indicator of autonomic nerve regulation of the heart. The recognition rate is increased by improving the starting position of R wave and the time-window function of the traditional differential threshold method. The experimental platform is a wearable sign monitoring system constructed based on body area networks (BAN) technology. Analytic hierarchy process (AHP) is used to construct the mental stress assessment model, the weight judgment matrix is constructed according to the influence degree of HRV analysis parameters on mental stress, and the consistency check is carried out to obtain the weight value of the corresponding HRV analysis parameters.ResultsExperimental results show that the recognition rate of R wave of real-time 5 min ECG data collected by this algorithm is >99%. The comprehensive index of HRV based on weight matrix can greatly reduce the deviation caused by the measurement error of each parameter. Compared with traditional characteristic wave recognition algorithms, the proposed algorithm simplifies the process, has high real-time performance, and is suitable for wearable analysis devices with low-configuration requirements.ConclusionOur algorithm can describe the mental stress of the body quantitatively and meet the requirements of application demonstration.

Cancelable electrocardiogram biometric system based on chaotic encryption using three‐dimensional logistic map for biometric‐based cloud services

AbstractBiometric features are extensively employed for security purposes, but they are vulnerable to threats and can be lost or compromised. Electrocardiogram (ECG) has been utilized as one of the most favorable biometrics. However, it contains confidential patient health information and personal identification details. Moreover, security flaws take place in hacking scenarios. Therefore, original biometrics must be secured by preventing them from being used in biometric databases. The enhanced security trend in biometric authentication is cancelable biometrics. Cancelable biometric systems can be built by generating regularly repeated distortions of biometric features to secure the sensitive data of the users. When cancelable features are disclosed, distortion parameters are modified, and new cancelable templates are generated. This paper presents a proposed cancelable ECG recognition system based on the 3D chaotic logistic map encryption, which has highly efficient random characteristics with confusion and diffusion properties. Normal and abnormal ECG signals have been used to test the proposed cancelable biometric recognition system, and good access results have been obtained. The results of the simulation indicate that the proposed system is secure, reliable, and practicable even with ECG signals for patients.

Three-Heartbeat Multilead ECG Recognition Method for Arrhythmia Classification

Electrocardiogram (ECG) is the primary basis for the diagnosis of cardiovascular diseases. However, the amount of ECG data of patients makes manual interpretation time-consuming and onerous. Therefore, the intelligent ECG recognition technology is an important means to decrease the shortage of medical resources. This study proposes a novel classification method for arrhythmia that uses for the very first time a three-heartbeat multi-lead (THML) ECG data in which each fragment contains three complete heartbeat processes of multiple ECG leads. The THML ECG data pre-processing method is formulated which makes use of the MIT-BIH arrhythmia database as training samples. Four arrhythmia classification models are constructed based on one-dimensional convolutional neural network (1D-CNN) combined with a priority model integrated voting method to optimize the integrated classification effect. The experiments followed the recommended inter-patient scheme of the Association for the Advancement of Medical Instrumentation (AAMI) recommendations, and the practicability and effectiveness of THML ECG data are proved with ablation experiments. Results show that the average accuracy of the N, V, S, F, and Q classes is 94.82%, 98.10%, 97.28%, 98.70%, and 99.97%, respectively, with the positive predictive value of the N, V, S, and F classes being 97.0%, 90.5%, 71.9%, and 80.4%, respectively. Compared with current studies, the THML ECG data can effectively improve the morphological integrity and time continuity of ECG information and the 1D-CNN model of ECG sequence has a higher accuracy for arrhythmia classification. The proposed method alleviates the problem of insufficient samples, meets the needs of medical ECG interpretation and contributes to the intelligent dynamic research of cardiac disease.

Using Simulation to Improve Adherence to Get With the Guidelines Time to First Shock.

Evidence demonstrates that shocking patients in ventricular fibrillation or pulseless ventricular tachycardia in 2 minutes or less leads to improved outcomes. At our facility in Orlando, Florida, 4 of 7 time to first shock fallouts occurred in the intensive care unit. No standardization for conducting code situations existed in the intensive care unit. To develop nurse simulation education and training to standardize intensive care unit code processes and improve compliance with timely defibrillation. The sample consisted of intensive care unit nurses. Interventions included online education, simulation, and a postintervention survey. Analysis was conducted using the Get With the Guidelines program as well as descriptive statistics. Online education assigned to all intensive care unit nurses included electrocardiogram recognition and code documentation. Nurses and physicians collaborated to develop a diagram to identify roles needed for successful conduct of a code situation. A code simulation video was created, reflecting these roles, and embedded in the online education. The education was graded and remediated one-on-one with nurses. Intensive care unit nurses completed structured code simulations, allowing them to practice serving as the leader. Three months after the intervention, a survey was distributed to nurses. The time to first shock fallouts in the intensive care unit decreased by 100%, and the facility's fallouts decreased by 71%. The facility's adherence to the time to first shock metric increased from 42% to 83%. In a postintervention survey, 89% of nurses reported perceived improvement in knowledge, team leadership and communication, and confidence associated with code events. Online education and code simulation positively affected time to first shock in code situations and empowered nurses to confidently function in these situations.

C82. Rare electrocardiography findings of Wellen’s Type A evolving into Type B on late onset Myocardial Infarction: A Case Report

Abstract Background Wellens' Syndrome is a left anterior descending (LAD) coronary artery disease which first described in 1982 by de Zwaan et al. A byphasic T or deeply inverted T are the typical Wellen's electrocardiogram (ECG) changes which represent a critical stenosis. It may be presented without any typical symptoms, and normal or mildly elevated enzymes depends on its onset. Case Summary A 56-years-old male, admitted to ER with typical chest pain within 1 day. The first ECG showed byphasic T wave on V2 and V3, and an hour serial ECG showed a deeply inverted T wave on V2 and V3. Cardiac marker showed an elevated Troponin I levels of 1,590.6 ng/L. The patient was diagnosed with acute NSTEMI and underwent an early invasive strategy on the next day. Coronary angiography revealed total occlusion of the proximal LAD, and implantation of drug eluting stent was done accordingly. The patient remained symptoms-free after the procedure and discharged in stable condition. Discussion This patient suffering a left coronary total occlusion with no appearance of any ST-elevation on ECG. Therefore, a critical stenosis does not always came up with a typical ST-segment changes. Wellen’s type ECG showed how the T waves evolution would represent a critical stenosis. Early recognition of Wellen's typical ECG in this patient lead to early invasive strategy decision and later resulted a good outcome.

C82. Rare electrocardiography findings of Wellen’s Type A evolving into Type B on late onset Myocardial Infarction: A Case Report

Abstract Background Wellens' Syndrome is a left anterior descending (LAD) coronary artery disease which first described in 1982 by de Zwaan et al. A byphasic T or deeply inverted T are the typical Wellen's electrocardiogram (ECG) changes which represent a critical stenosis. It may be presented without any typical symptoms, and normal or mildly elevated enzymes depends on its onset. Case Summary A 56-years-old male, admitted to ER with typical chest pain within 1 day. The first ECG showed byphasic T wave on V2 and V3, and an hour serial ECG showed a deeply inverted T wave on V2 and V3. Cardiac marker showed an elevated Troponin I levels of 1,590.6 ng/L. The patient was diagnosed with acute NSTEMI and underwent an early invasive strategy on the next day. Coronary angiography revealed total occlusion of the proximal LAD, and implantation of drug eluting stent was done accordingly. The patient remained symptoms-free after the procedure and discharged in stable condition. Discussion This patient suffering a left coronary total occlusion with no appearance of any ST-elevation on ECG. Therefore, a critical stenosis does not always came up with a typical ST-segment changes. Wellen’s type ECG showed how the T waves evolution would represent a critical stenosis. Early recognition of Wellen's typical ECG in this patient lead to early invasive strategy decision and later resulted a good outcome.

Visual saliency detection approach for long-term ECG analysis

Background and objectiveDetection and analysis of QRS-complex as well as the processing of electrocardiogram (ECG) signal using computers are being practiced for over the last fifty-eight years, approximately, and yet the thirst of designing superior ECG processing and recognition algorithms still captures researchers’ attention around the globe. A saliency detection-based technique for the processing of one-dimensional biomedical signals such as ECG is proposed here for the first time, to the best or our knowledge. Methods and resultsIn this proposed research work, first, a trigonometric threshold-based technique is used to identify the QRS-complexes from the ECG signal. Motion-artifact (MA) and sudden-change-in-baseline (SCB) types of noises are considered to be the toughest among others to filter out from the ECG signals as the bandwidths of these two types of noises overlap with that of the ECG. Only one feature is extracted from each of the QRS-complex-intervals, and the normalised values of this feature are arranged in the form of a gray-scale image. Then, a saliency detection-based technique is applied iteratively on the gray-scale image to detect those regions of the ECG signals, which are highly corrupted with MA and (or) SCB noises. Next, three unique geometric-features are extracted from the rest of the QRS-complexes, which are not corrupted with MA or SCB noises, and the normalised values of these three features are arranged in the form of an Red-Green-Blue (RGB) image. Again, the saliency detection-based technique is applied to identify the abnormal QRS-complexes from the RGB image. ConclusionsThe technique is tested on long-term ECG signals; totaling a duration of 17.54 days, and its performance is evaluated through both quantitative and qualitative measures. The applicability, scope of implement in real-time scenarios, advantage of the proposed technique over the existing ones are discussed with a group of clinicians and cardiologists, and very affirmative and encouraging responses are received from them.

A Novel Deep Learning based Gated Recurrent Unit with Extreme Learning Machine for Electrocardiogram (ECG) Signal Recognition

Cardiovascular disease (CVD) becomes a significant risk to human lives. Electrocardiogram (ECG) is a commonly employed non-invasive physiological signal used to screen and diagnose CVD. During recent years, remarkable advances in automated ECG interpretation techniques have been witnessed. In particular, deep learning (DL) models find useful to increase the disease diagnostic performance of CVD using the ECG signals. To explore and achieve effective ECG recognition, this paper presents a Class Imbalance handing with DL based Gated Recurrent Unit (GRU) and Extreme Learning Machine (ELM) for ECG Signal Recognition, named as CIGRU-ELM model. The presented CIGRU-ELM model involves preprocessing, data sampling, feature extraction, and classification. Primarily, data preprocessing takes place to convert the ECG report to valuable data and transform it into a compatible format for further processing. In addition, Adaptive Synthetic (ADASYN) based data sampling process takes place to handle the class imbalance problem. Besides, GRU based feature extractor is applied to extract a useful set of feature vectors. Finally, ELM model based classification model is employed to determine the appropriate class label of the test ECG signal. An extensive experimental analysis is performed on the benchmark PTB-XL dataset. A detailed comparative results analysis of the proposed CIGRU-ELM model takes place to highlight the superiority of the proposed model interms of accuracy, sensitivity, specificity, kappa, Mathew correlation coefficient, and Hamming loss.

A novel method for automated congestive heart failure and coronary artery disease recognition using THC-Net

Coronary artery disease (CAD) and congestive heart failure (CHF) lead to many deaths worldwide. Generally, an electrocardiogram (ECG) is employed as the diagnostic tool for CAD/CHF recognition. However, since ECG changes are sometimes subtle, visually distinguishing long-term ECG abnormalities is time consuming and laborious. To address these issues, we proposed a novel two-channel hybrid convolutional network (THC-Net) for automatic ECG recognition. THC-Net contains a canonical correlation analysis (CCA)-principal component analysis (PCA) convolutional network, an independent component analysis (ICA)-PCA convolutional network, and a Dempster-Shafer (D-S) theory-based linear support vector machine (SVM). The CCA-PCA and ICA-PCA convolutional networks are developed to extract deep features containing the lead-correlation and lead-specific information, respectively, from ECGs. Compared to common convolutional neural networks (CNNs), their kernels can be directly extracted by CCA, ICA, and PCA with a faster training time. Then, the D-S theory-based linear SVM, which can process multi-channel uncertainty information, is employed as the classification model. In this work, an accuracy of 95.54% was obtained for classifying normal, CHF and CAD patients based on leave-one-out cross-validation. Additionally, experiments on multi-level noisy and imbalanced data yielded remarkable results. Hence, the proposed method has the potential to diagnose CAD and CHF in clinical settings.

A New Method for Refined Recognition for Heart Disease Diagnosis Based on Deep Learning

The proper evaluation of heart health requires professional medical experience. Therefore, in clinical diagnosis practice, the development direction is to reduce the high dependence of the diagnosis process on medical experience and to more effectively improve the diagnosis efficiency and accuracy. Deep learning has made remarkable achievements in intelligent image analysis technology involved in the medical process. From the aspect of cardiac diagnosis, image analysis can extract more profound and abundant information than sequential electrocardiogram (ECG) signals. Therefore, a new region recognition and diagnosis method model of a two-dimensional ECG (2D-ECG) signal based on an image format is proposed. This method can identify and diagnose each refined waveform in the cardiac conduction cycle reflected in the image format ECG signal, so as to realize the rapid and accurate positioning and visualization of the target recognition area and finally get the analysis results of specific diseases. The test results show that compared with the results obtained by a one-dimensional sequential ECG signal, the proposed model has higher average diagnostic accuracy (98.94%) and can assist doctors in disease diagnosis with better visualization effect.

Arrhythmia Classification with Single Beat ECG Evaluation and Support Vector Machine

Abnormal electrical activity of the human heart indicates cardiac dysfunction. The Electrocardiogram (ECG) is one of the non-invasive diagnostic techniques to detect cardiac abnormalities. Irregularity and non-stationarity in the ECG signal impose difficulties to clinicians for accurate diagnosis of heart diseases only by visual inspection. Automatic recognition of abnormal ECG beats aids in early detection of heart diseases. This paper explores the ECG single beat analysis to identify the cardiac abnormality. In this work, seven classes of arrhythmia are considered as recommended by AAMI(Association for the Advancement of Medical Instrumentation) standard. Beat feature database is generated from 44 recordings of the MIT-BIH arrhythmia database to support the arrhythmia classification. Classification is implemented with Multiclass Support Vector Machine (SVM) for non-linearly separable data effectively. Classification accuracy up to 93% is achieved for the selected input feature sets. This work assesses the suitability of the ECG input features for multi-class classification of arrhythmia.

Detection and diagnosis of myocarditis in young patients using ECG analysis based on artificial neural networks

Myocarditis is a common disease of the pediatric circulatory system, and its clinical manifestations are diversified. Especially in older patients, chest tightness, shortness of breath and palpitations may occur. However, some patients may have no distinct clinical symptoms and may occasionally experience symptoms during the diagnosis and treatment of respiratory infections, such as arrhythmias in infants and young patients. Because the clinical manifestations of myocarditis are not typical, the specificity and sensitivity of serum myocardial enzyme detection are not very high, which hinders a definitive diagnosis of certain cases by clinicians. However, degeneration, necrosis and inflammatory diseases of cardiomyocytes can affect myocardial conductivity and stress. In patients with myocarditis, an electrocardiogram (ECG) signal changes abnormally; thus, the ECG remains an indispensable means of diagnosing myocarditis. Biomedical signal detection and signal processing techniques can facilitate the study of biological and physiological systems and assist physicians in the diagnosis and treatment of patients. However, traditional manual analysis of ECG methods has some drawbacks. Due to the extensive variety of electrocardiograms, variations in various waveform patterns are subtle and complex. Clinicians often need a solid theoretical foundation and a wealth of clinical experience to make a correct assessment using an ECG. If the medical expert is engaged in a large number of ECG recognition studies for a long time, fatigue may develop and cause misdiagnosis. In addition, the manual analysis of an ECG is part of the postmortem analysis, achieving a real-time diagnosis is difficult, and the resulting delay is likely to cause critical patients to miss the best treatment opportunity. To solve this problem, we have developed a dynamic electrocardiogram medical-assisted diagnosis system to assist medical experts in the diagnosis and treatment of patients with myocarditis. Our system can process the collected ECG signals, obtain waveform parameters, and automatically perform an analysis according to an expert’s clinical experience. These parameters provide diagnostic recommendations.