Single Electrocardiogram Research Articles

Artificial intelligence age prediction using electrocardiogram data: Exploring biological age differences

BackgroundBiological age can be predicted using artificial intelligence (AI) trained on electrocardiograms (ECGs), which is prognostic for mortality and cardiovascular events. ObjectiveWe developed an AI model to predict age from an ECG and compared baseline characteristics to identify determinants of advanced biological age. MethodsAn AI model was trained on ECGs from cardiology inpatients aged 20–90 years. AI analysis used a convolutional neural network with data divided in an 80:20 ratio (development/internal validation), with external validation undertaken using data from the UK Biobank. Performance and subgroup comparison measures included correlation, difference, and mean absolute difference. ResultsA total of 63,246 patients with 353,704 total ECGs were included. In internal validation, the correlation coefficient was 0.72, with a mean absolute difference between chronological age and AI-predicted age of 9.1 years. The same model performed similarly in external validation. In patients aged 20–29 years, AI-ECG biological age was greater than chronological age by a mean of 14.3 ± 0.2 years. In patients aged 80–89 years, biological age was lower by a mean of 10.5 ± 0.1 years. Women were biologically younger than men by a mean of 10.7 months (P = .023), and patients with a single ECG were biologically 1.0 years younger than those with multiple ECGs (P < .0001). ConclusionThere are significant between-group differences in AI-ECG biological age for patient subgroups. Biological age was greater than chronological age in young hospitalized patients and lower than chronological age in older hospitalized patients. Women and patients with a single ECG recorded were biologically younger than men and patients with multiple recorded ECGs.

Differentiated knowledge distillation: Patient-specific single-sample personalization for electrocardiogram diagnostic models

To achieve optimal performance in practical applications, the electrocardiogram (ECG) diagnosis models have to be personalized using the ECG data of specific patients. Most current research on personalized ECG diagnosis centres around large-sample transfer learning aimed at specific populations or collection conditions. However, the time and economic costs of collecting such a large amount of data are unacceptable for specific individuals. Moreover, the characteristics of ECG signals mean that most data augmentation methods may not produce positive effects. To address these issues, we propose a novel differentiated knowledge distillation (DKD) method, which purposefully transfers specific knowledge in knowledge distillation by inputting differentiated data to the teacher and student models. Specifically, this method constructs differences between the input data of student and teacher models using one single ECG record of a specific patient, so as to transfer individual knowledge of the patient into the student model while maintaining the diagnostic ability of the baseline teacher model. For other ECG data of the target patient, the model personalized by this method shows a significant improvement of up to 10% in accuracy, micro-F1-score, and micro-sensitivity compared to the baseline model. Additionally, this method demonstrates excellent efficacy for various existing networks in the field of ECG diagnosis.

A Vehicle Passive Entry Passive Start System with the Intelligent Internet of Things

With the development of sensor and communication technologies, the Internet of Things (IoT) subsystem is gradually becoming a crucial part in vehicles. It can effectively enhance functionalities of vehicles. However, new attack types are also emerging. For example, a driver with the smart key in their pocket can push the start button to start a car. At the same time, security issues in the push-to-start scenario are pervasive, such as smart key forgery. In this study, we propose a vehicle Passive Entry Passive Start (PEPS) system that adopts deep learning algorithms to recognize the driver using the electrocardiogram (ECG) signals measured on the driver’s smart watch. ECG signals are used for personal identification. Smart watches, serving as new smart keys of the PEPS system, can improve convenience and security. In the experiment, we consider commercial smart watches capable of sensing ECG signals. The sample rate and precision are typically lower than those of a 12-lead ECG used in hospitals. The experimental results show that Long Short-Term Memory (LSTM) models achieve the best accuracy score for identity recognition (91%) when a single ECG cycle is used. However, it takes at least 30 min for training. The training of a personalized Auto Encoder model takes only 5 min for each subject. When 15 continuous ECG cycles are sensed and used, this can achieve 100% identity accuracy. As the personalized Auto Encoder model is an unsupervised learning one-class recognizer, it can be trained using only the driver’s ECG signal. This will simplify the management of ECG recordings extremely, as well as the integration of the proposed technology into PEPS vehicles. A FIDO (Fast Identify Online)-like environment for the proposed PEPS system is discussed. Public key cryptography is adopted for communication between the smart watch and the PEPS car. The driver is first verified on the smart watch via local ECG biometric authentication, and then identified by the PEPS car. Phishing attacks, MITM (man in the middle) attacks, and replay attacks can be effectively prevented.

The Clinical Relevance of ECG Parameters in the Prediction of Cardiac Mortality: A Comprehensive Review

About half of all heart disease deaths are caused by cardiac arrest, making it one of the major causes of mortality in prosperous countries. When confronted with potentially fatal arrhythmias, implanted preventive cardioverter defibrillators significantly improve survival chances. However, this is only possible if high-risk patients who are prone to spontaneous cardiac arrest are identified beforehand. The current analysis examines the most recent findings regarding the use of surface electrocardiogram (ECG) data to predict sudden cardiac arrest. Here, we provide a comprehensive overview of the literature on non-invasive ECG techniques for predicting these kinds of cardiovascular crises. Several electrocardiographic risk stratification methods, including T-wave alternans, signal-averaged ECG, T-peak-to-end variation, early repolarization, an extension of the QT interval, QRS duration, QRS cluster patterns, and Holter monitoring, have been reviewed and analysed. These ECG results have shown to be useful as first screening instruments. Nonetheless, no single ECG measure has shown to be an effective technique for classifying individuals based on their risk of sudden cardiac arrest to date. Nevertheless, one or more of these prospective SEM metrics might potentially be important in intricate risk categorization schemes.

Characterization of Heart Diseases per Single Lead Using ECG Images and CNN-2D.

Cardiopathy has become one of the predominant global causes of death. The timely identification of different types of heart diseases significantly diminishes mortality risk and enhances the efficacy of treatment. However, fast and efficient recognition necessitates continuous monitoring, encompassing not only specific clinical conditions but also diverse lifestyles. Consequently, an increasing number of studies are striving to automate and progress in the identification of different cardiopathies. Notably, the assessment of electrocardiograms (ECGs) is crucial, given that it serves as the initial diagnostic test for patients, proving to be both the simplest and the most cost-effective tool. This research employs a customized architecture of Convolutional Neural Network (CNN) to forecast heart diseases by analyzing the images of both three bands of electrodes and of each single electrode signal of the ECG derived from four distinct patient categories, representing three heart-related conditions as well as a spectrum of healthy controls. The analyses are conducted on a real dataset, providing noteworthy performance (recall greater than 80% for the majority of the considered diseases and sometimes even equal to 100%) as well as a certain degree of interpretability thanks to the understanding of the importance a band of electrodes or even a single ECG electrode can have in detecting a specific heart-related pathology.

Electrocardiography-based Artificial Intelligence Algorithms Aid in Prediction of Long-term Mortality After Kidney Transplantation.

Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative electrocardiograms (ECGs) in forecasting long-term mortality following KT. We analyzed preoperative ECGs from KT recipients at three Mayo Clinic sites (Minnesota, Florida, and Arizona) between January 1, 2006, and July 30, 2021. The study involved 6 validated AI algorithms, each trained to predict future development of atrial fibrillation, aortic stenosis, low ejection fraction, hypertrophic cardiomyopathy, amyloid heart disease, and biological age. These algorithms' outputs based on a single preoperative ECG were correlated with patient mortality data. Among 6504 KT recipients included in the study, 1764 (27.1%) died within a median follow-up of 5.7 y (interquartile range: 3.00-9.29 y). All AI-ECG algorithms were independently associated with long-term all-cause mortality ( P < 0.001). Notably, few patients had a clinical cardiac diagnosis at the time of transplant, indicating that AI-ECG scores were predictive even in asymptomatic patients. When adjusted for multiple clinical factors such as recipient age, diabetes, and pretransplant dialysis, AI algorithms for atrial fibrillation and aortic stenosis remained independently associated with long-term mortality. These algorithms also improved the C-statistic for predicting overall (C = 0.74) and cardiac-related deaths (C = 0.751). The findings suggest that AI-enabled preoperative ECG analysis can be a valuable tool in predicting long-term mortality following KT and could aid in identifying patients who may benefit from enhanced cardiac monitoring because of increased risk.

Usefulness of the four-variable formula on serial electrocardiograms in detecting subtle anterior myocardial infarction

ObjectiveTo perform serial electrocardiogram (ECG) analyses in patients with subtle ECG changes in the anterior leads and evaluate the performance of the four-variable formula in detecting left anterior descending (LAD) coronary artery occlusion. MethodsThis prospective study included patients admitted to the emergency department with acute chest pain between April 2021 and January 2023, whose initial ECG was not diagnostic but indicated suspicion of myocardial infarction (MI) and who underwent percutaneous coronary intervention in their follow-up. The control group consisted of patients who were diagnosed with benign variant ST-segment elevation (BV-STE) due to ST-segment elevation (STE) of at least 1 mm in the anterior lead, who had normal cardiac troponin levels, and who presented with non-cardiac chest pain. Following admission, six ECGs were taken at 10-min intervals. The scores of all patients were calculated with the four-variable formula on serial ECGs and compared between the groups. ResultsA total of 232 patients, including 116 with anterior MI and 116 with BV-STE, were included in the study. When the cut-off value for the four-variable formula was taken as ≥18.2, the sensitivity, specificity, and diagnostic accuracy of the first ECG were determined to be 82.7%, 85.3%, and 83.6%, respectively. We found that the four-variable formula had the highest sensitivity, specificity, and diagnostic accuracy in detecting LAD occlusion for the ECG taken at the 20th minute (83.6%, 89.6%, and 86.2%, respectively). ConclusionThe four-variable formula was found to be a valid method for the differentiation of STEMI and BV-STE in patients with subtle ECG changes. While managing this patient group, using serial ECGs rather than a single ECG to evaluate the clinical status of patients can help clinicians make more accurate decisions.

Explainable Deep Learning-Based Approach for Multilabel Classification of Electrocardiogram

Recently computer-aided diagnosis methods have been widely adopted to aid doctors in disease diagnosis making their decisions more reliable and error-free. Electrocardiogram (ECG) is the most commonly used, noninvasive diagnostic tool for investigating various cardiovascular diseases. In real life, patients suffer from more than one heart disease at a time. So any practical automated heart disease diagnosis system should identify multiple heart diseases present in a single ECG signal. In this article, we propose a novel deep learning-based method for the multilabel classification of ECG signals. The proposed method can accurately identify up to two labels of an ECG signal pertaining to eight rhythm or morphological abnormalities of the heart and also the normal heart condition. Also, the black-box nature of deep learning models prevents them from being applied to high-risk decisions like the automated heart disease diagnosis. So in this article, we also establish an explainable artificial intelligence (XAI) framework for ECG classification using class activation maps obtained from the Grad-CAM technique. In the proposed method, we train a convolutional neural network (CNN) with constructed ECG matrices. With the experiments conducted, we establish that training the CNN by taking only one label for each ECG signal data point is enough for the network to learn the features of an ECG point with multilabel information in it (multiple heart diseases at the same time). During classification, we apply thresholding on the output probabilities of the softmax layer of our CNN, to obtain the multilabel classification of ECG signals.We trained the model with 6311 ECG records and tested the model with 280 ECG records. During testing, the model achieved a subset accuracy of 96.2&#x0025; and a hamming loss of 0.037 and a precision of 0.986 and a recall of 0.949 and an F1-score of 0.967. Considering the fact that the model has performed very well in all the metrics of multilabel classification, the model can be directly used as a practical tool for automated heart disease diagnosis.

Heartbeat classification based on single lead-II ECG using deep learning

The analysis and processing of electrocardiogram (ECG) signals is a vital step in the diagnosis of cardiovascular disease. ECG offers a non-invasive and risk-free method for monitoring the electrical activity of the heart that can assist in predicting and diagnosing heart diseases. The manual interpretation of the ECG signals, however, can be challenging and time-consuming even for experts. Machine learning techniques are increasingly being utilized to support the research and development of automatic ECG classification, which has emerged as a prominent area of study. In this paper, we propose a deep neural network model with residual blocks (DNN-RB) to classify cardiac cycles into six ECG beat classes. The MIT-BIH dataset was used to validate the model resulting in a test accuracy of 99.51%, average sensitivity of 99.7%, and average specificity of 98.2%. The DNN-RB method has achieved higher accuracy than other state-of-the-art algorithms tested on the same dataset. The proposed method is effective in the automatic classification of ECG signals and can be used for both clinical and out-of-hospital monitoring and classification combined with a single-lead mobile ECG device. The method has also been integrated into a web application designed to accept digital ECG beats as input for analyses and to display diagnostic results.

Risk Factors and Prognosis of Cardiovascular Damage in Hypereosinophilia

To investigate the risk factors and prognosis of cardiovascular damage in hypereosinophilia (HE). The clinical data of 62 patients with HE in Gansu Provincial Hospital from January 2015 to December 2020 were retrospectively analyzed, including clinical characteristics and laboratory indicators, and the influencing factors of survival and prognosis were also analyzed. In this study, there were 34 males and 28 females, with a median age of 53.5 (20-79) years, 35 patients without cardiovascular damage, 27 patients with cardiovascular damage, including 22 patients with abnormal electrocardiogram (ECG) (81.5%), 18 patients with abnormal echocardiography (ECHO) (66.7%), 9 patients with single ECG abnormality, 5 patients with single ECHO abnormality, and other 13 patients with multiple abnormalities. In cardiovascular damage group, peripheral white blood cell count, absolute value of eosinophils, troponin T (TNT), N-terminal pro-B-type natriuretic peptide (NT-proBNP), interleukin (IL)-4 and IL-5 levels at initial diagnosis were significantly higher than those in the non-cardiovascular damage group (P <0.01), while hemoglobin, IL-2 and interferon-γ levels were significantly lower (P <0.01). There were no significant differences in age, sex, course of disease, etiological classification, platelet count, serum creatine kinase, serum creatine kinase isoenzyme and lactate dehydrogenase between the two groups (P >0.05). The 5-year overal survival rate of patients with cardiovascular damage was 88.9%, and that of patients without cardiovascular damage was 100%, the difference was statistically significant (P =0.012). The 5-year event-free survival (EFS) rate of patients with cardiovascular damage was 59.3%, and the median time was 37 (21-52) months, while that of patients without cardiovascular damage was 80%, and the median time was 63 (51-74) months (P =0.002). Age (>60 years old), course of disease (>24 months), NT-proBNP (>3 000 pg/ml), TNT (>100 ng/L), elevated IL-4 and IL-5 were associated with EFS shortening in patients with cardiovascular damage, which were independent risk factors for EFS. The EFS rate in HE patients without cardiovascular damage is significantly higher than patients with cardiovascular damage. Age, course of disease, NT-proBNP, TNT, IL-4 and IL-5 are independent risk factors affecting EFS of patients with cardiovascular damage.

Prevalence and diagnostic significance of de-novo 12-lead electrocardiogram patterns following covid-19 infection in elite soccer players

Abstract Funding Acknowledgements Type of funding sources: None. Background Considering that myocarditis is implicated in 5-15% of all exercise related sudden cardiac deaths in athletes, following a novel viral pandemic, the identification and risk-stratification of affected athletes remains an important priority. Given its widespread availability, the inclusion of a 12-lead electrocardiogram (ECG) is common to all consensus-based protocols from North America and Europe. However, a single cross-sectional ECG is problematic because several physiological repolarisation changes are also common manifestations in individuals with myopericarditis. Purpose The aim of our study was to report on the prevalence and diagnostic significance of de-novo ECG patterns following COVID-19 infection in a well-defined cohort of elite soccer players in the English, Dutch and Brazilian leagues. Methods In this multicentre observational study, between March 2020–May 2022, we evaluated consecutive athletes with COVID-19 infection. ECGs were reported in accordance with the International recommendations for ECG interpretation in athletes. The following patterns were also considered abnormal if they were not detected on the pre-COVID-19 infection ECG: PR-segment depression, new J-point and ST segment elevation, low QRS voltages, QRS fragmentation, new ST-segment depression, new T-wave inversion, biphasic T-waves and a reduction in the T-wave amplitude by 50% or T-wave flattening. Athletes exhibiting de-novo ECG changes underwent cardiovascular magnetic resonance (CMR) scans. One club mandated CMR scans for all players (n=30) following COVID-19 infection, despite the absence of cardiac symptoms or de-novo ECG changes. Results 511 soccer players (median age 21-years, IQR:18-26-years) were included, of which 88% were male and 58% white. 17 (3%) athletes demonstrated de-novo ECG changes, which included, a reduction in T-wave amplitude in the inferior and lateral leads (n=5), inferior leads (n=4) and lateral leads (n=4); inferior T-wave inversion (n=7), and ST-segment depression (n=2). 15 (88%) athletes with de-novo ECG changes revealed CMR evidence of inflammatory cardiac sequalae. All athletes who underwent a mandatory CMR irrespective of illness severity or ECG findings, had normal findings. Athletes revealing de-novo ECG changes, had a higher prevalence of cardiac symptoms (71% v 12%; p&amp;lt;0.0001) and longer median symptom duration (5-days, IQR:3-10) compared with athletes without de-novo ECG changes (2-days, IQR:1-3; p&amp;lt;0.001). Among athletes without cardiac symptoms, the additional yield of de-novo ECG changes to detect cardiac inflammation was 17%. Conclusions 3% of athletes demonstrated de-novo ECG changes post COVID-19 infection, of which 88% were diagnosed with inflammatory cardiac sequalae. Most affected athletes exhibited cardiac symptoms; however de-novo ECG changes contributed to a diagnosis of cardiac inflammation in 17% of athletes without cardiac symptoms.

PO-04-151 A NEW SEQUENTIAL ARTIFICIAL INTELLIGENCE ALGORITHM FOR PREDICTING ATRIAL FIBRILLATION USING SERIAL 12-LEAD ELECTROCARDIOGRAMS

Because the early stage of atrial fibrillation (AF) is difficult to detect, a significant number of patients are exposed to the risk of stroke without forewarning. Artificial intelligence (AI) algorithms using 12-lead electrocardiograms (ECGs) are being developed to detect AF early. Unlike previous studies in which a single normal sinus rhythm (NSR) ECG was used, we hypothesized that the AI model could predict new-onset AF more accurately by learning the change between two NSR ECGs in an individual. In the present study, standard 12-lead ECGs of patients labeled by cardiologists from January 2010, to December 2021, at Samsung Medical Center were used for AI model development. All patients included in the study were classified into an NSR or AF group based on AF diagnosis. Two AI models (single ECG- and serial ECG-based) were developed using a light gradient boosting machine learning algorithm. The performance of AI models was evaluated based on the area under the receiver operating characteristic curve (AUROC) and precision-recall curve with sensitivity, specificity, accuracy, and F1 score. The AUROC results of the single ECG and serial ECG models were compared to evaluate the hypothesis. We trained 102,018 ECGs from 54,128 patients for the single ECG model and 126,106 ECGs from 106,722 patients for the serial ECG model. When testing the two AI models using test data sets, the performance for predicting new-onset AF was significantly higher with the serial AI model compared with the single AI model (single vs. serial AI model; sensitivity 0.68 vs. 0.86; specificity 0.95 vs. 0.98; accuracy 0.89 vs. 0.9; F1 score 0.74 vs. 0.90; AUROC 0.92 vs. 0.95; p<0.001). In addition, the contribution of various ECG parameters was scored using AI model developed with F-score ANOVA. In the serial ECG model, difference of P-wave amplitude between two ECGs was the leading variable for predicting AF. Comparing serial ECGs rather than a single ECG in an individual could predict future AF more accurately.Tabled 1Table 1. Baseline characteristicsSingle ECG modelOverall (n = 127,036)NSR group (n = 97,514)AF group (n = 29,522)p-valueAge, years64.3 ± 13.762.8 ± 13.570.1 ± 12.8< 0.001Male, n (%)62,136 (53.7)46,370 (50.7)15,766 (65.3)< 0.001Number of ECGs per patient1.9 ± 1.91.7 ± 1.43.3 ± 3.7< 0.001Serial ECG modelOverall (n = 157,440)NSR group (n = 129,721)AF group (n = 27,719)p-valueAge, years69.2 ± 24.367.9 ± 24.676.3 ± 20.8< 0.001Male, n (%)70,008 (50.6)56,448 (48.2)13,560 (63.4)< 0.001Number of ECGs per patient1.2 ± 1.517.5 ± 6.7< 0.001ECG=electrocardiography, NSR=normal sinus rhythm, AF=atrial fibrillation Open table in a new tab

Deep Learning-Based Signal Quality Assessment for Wearable ECGs

Nowadays, use of the dynamic electrocardiogram (ECG) has developed rapidly because of the wide application of wearable devices [1]–[3]. Most ECG-based diagnostic algorithms require that the ECG signal have a clear waveform and accurate feature points. However, the collected wearable ECG signal usually contains a certain amount of noise and causes many false alarms in the ECG analysis system [4], [5]. Thus, signal quality assessment (SQA) plays a prominent role in ruling out the ECG segments with poor signal quality [6]. Compared with traditional static ECG signals, dynamic wearable ECGs contain more noise, which brings greater challenges to disease detection algorithms [7]–[9]. These artifacts and noises in dynamic ECG signals can seriously affect the R-peaks detection, ECG beat extraction, ECG morphological feature extraction and the detection of noise peaks, resulting in frequent false alarms [10]. In 2008, Li et al. [11] proposed the bSQI signal quality indexes: comparison of two beat detectors on a single ECG lead. Liu et al. [12] generalized the two QRS wave complex (QRS) detectors-based bSQI to multiple QRS detectors-based bSQI (GbSQI) to improve the SQA performance. Liu et al. [8] proposed an efficient real-time SQA method for healthy subjects.

LTH-ECG: Lottery Ticket Hypothesis-based Deep Learning Model Compression for Atrial Fibrillation Detection from Single Lead ECG On Wearable and Implantable Devices.

Atrial Fibrillation (AF) is a kind of arrhythmia, which is a major morbidity factor, and AF can lead to stroke, heart failure and other cardiovascular complications. Electrocardiogram (ECG) is the basic marker to test the condition of heart and it can effectively detect AF condition. Single lead ECG has the practical advantage for being small form factor and it is easy to deploy. With the sophistication of the current deep learning (DL) models, researchers have been able to construct cardiologist-level models to detect different arrhythmias including AF condition detection from single lead short-time ECG signals. However, such models are computationally expensive and require huge memory size for deployment (more than 100 MB to deploy state-of-the-art 34-layer convolutional neural network-based ECG classification model). Such models need to be significantly trimmed with insignificantly loss of its classification performance for deployment in practical applications like single lead ECG classification in wearable and implantable devices. We have found that classical deep learning model compression techniques like pruning, quantization are not capable of substantial model size reduction without compromising on the model performance. In this paper, we propose LTH-ECG, which is our novel goal-driven winning lottery ticket discovery method, where lottery ticket hypothesis (LTH)-based iterative model pruning is used with the aim of over-pruning avoidance. LTH-ECG reduces the model size by 142x times with insignificant loss of classification performance (less than 1 % test F1-score penalty). Clinical Relevance- LTH-ECG will enable practical deployment for remote screening of AF condition using single lead short-time ECG recordings such that patients can on-demand monitor AF condition remotely through wearable ECG sensing devices and report cardiological abnormality to the concerned physician. LTH-ECG acts as an early warning system for effective AF condition screening.

Antipsychotic drugs and QT interval prolongation

Prolongation of myocardial repolarization, i.e. lengthening of the QTc interval on surface electrocardiogram (ECG), with increased risk of cardiac arrhythmias, has been recognized as a side effect of many drugs. Most first and second generation antipsychotic drugs can cause dose-related prolongation of QTc, although there are important differences in the potency of individual agents. ECGs in 46 patients suffering from schizophrenic disorder and treated with different antipsychotic drugs one week after the admission to the psychiatric hospital were analyzed in this prospective observational study. Hearth rate, QT interval and QTc interval were assessed. The average daily dose of prescribed antipsychotic drugs was calculated. Average QTc interval was 417 msec, only 3 out of 46 (6,5%) patients had QTc over 450 msec and only one patient (2,2%) had QTc over 500 msec. No differences in QTc interval between patients who were treated either with first or second generation antipsychotic drug or with combination of both were found. Female participants had significantly longer OTc interval than male participants (p &lt; 0,05). Results raise the question of the clinical relevance of a single ECG for diagnostics of cardiac complications in schizophrenia patients and suggest the need to conduct ECG monitoring in patients with high risk for cardiac complications during antipsychotic treatment.

The Identification of ECG Signals Using Wavelet Transform and WOA-PNN.

Electrocardiogram (ECG) signal identification technology is rapidly replacing traditional fingerprint, face, iris and other recognition technologies, avoiding the vulnerability of traditional recognition technologies. This paper proposes an ECG signal identification method based on the wavelet transform algorithm and the probabilistic neural network by whale optimization algorithm (WOA-PNN). Firstly, Q, R and S waves are detected by wavelet transform, and the P and T waves are detected by local windowed wavelet transform. The characteristic values are constructed by the detected time points, and the ECG data dimension is smaller than that of the non-reference detection. Secondly, combined with the probabilistic neural network, the mean impact value algorithm is used to screen the characteristic values, the characteristic values with low influence are eliminated, and the input and complexity of the model are simplified. Finally, a WOA-PNN combined classification method is proposed to intelligently optimize the hyper parameters in the probabilistic neural network algorithm to improve the model accuracy. According to the simulation verification on three databases, ECG-ID, MIT-BIH Normal Sinus Rhythm and MIT-BIH Arrhythmia, the identification accuracy of a single ECG cycle is 96.97%, and the identification accuracy of three ECG cycles is 99.43%.

Driver Identification System Using 2D ECG and EMG Based on Multistream CNN for Intelligent Vehicle

As autonomous driving technologies develop, biometrics technology using driver's bio-signals provides various driver-customized infotainment services. Many studies on driver identification are underway to improve the feature extraction and classification steps to identify drivers with high accuracy. The existing identification systems have used a driver's single biological signal to identify the driver without considering the driver's driving state. The identification error rate was high because electromyography (EMG) is included according to motion artifacts while driving in the electrocardiogram (ECG) acquired by the driver's behavioral characteristics. In this letter, EMG acquired by the driver's motion and ECG acquired by the behavioral characteristics are used simultaneously to improve the identification accuracy. The proposed identification system converts ECG and EMG into 2D constant Q transform (CQT) images and classifies drivers by multistream convolutional neural network (CNN). Our extensive experiments show that a single ECG can achieve 98.1% of the identification accuracy, a single EMG can achieve 84.4% of the identification accuracy, and a multisignal can achieve 98.9% of the identification accuracy with 2D CQT.

Machine Learning Techniques Applied for Exploring Heart Disease and Classifying Stages through ECG Signal

Electrocardiogram (ECG) has given crucial data concerning various heart attack criterion of the human heart this examination can be the key goal of the research for the detection and prevention of cardiac circumstances frighten. The proposed method using machine learning techniques for classifying and analyzing ECG signal processing and this research mainly developed for early discovery of heart diseases and also the prediction of stages level. The dataset was utilized as a person ECG signal of Heart Database which was taken from the UCI repository of Machine learning dataset vault. In this paper, a simple algorithm is presented for to discover R-peaks automatically from a single lead digital ECG data. The proposed method detecting the time interval of the ECG signal from the R-peaks level next level with the double squared difference signal is used to localize the region of QRS which is the time interval between the binary data. this method consists of different stages of sorting from the raw data for reducing nosier signal, threshold a difference signal of ECG by analyzing the time interval of QRS, and finally a comparison of relative magnitude to detect the region of interval processing to analyze accuracy result. The proposed research novel machine learning techniques of the multi-module neural network system (MMNNS) is used to analyze the imbalance problem form the ECG signal classification if the wave was abnormal then the user of dataset patients will be affected by heart diseases. Using the time interval varies from the range of QRS then it analyzes the abnormal of ECG by MMNNS algorithm to define the classification result and finally analyze the stages. If the patients were presented an abnormal ECG signal compared with a normal ECG signal wave graph then they were affected by heart diseases if whether they patients were affected then finally classify the stages of heart diseases whether they are predictable/unpredictable stages. The examination result is completed among two strategies on the better accuracy premise and effective training time of the process.

Heart rate variability based physical exertion monitoring for manual material handling tasks

Physical exertion monitoring has been strongly emphasized to avert the ill-effects of physically demanding nature of many industries such as construction. Recently, several sensors-based approaches have been suggested as an alternative to traditional subjective feedback-based methods. Although the proposed sensor-based approaches have laid the foundation for automated physical exertion monitoring, they require multiple on-body and/or off-body sensors to collect psychological, physiological, acceleration/posture or weather-related data. As such, multiple on-body sensors may instigate irritation and discomfort whereas other off-body sensors require additional resources for handling and managing them. To address these limitations, taking a minimalistic approach, this study explored the use of heart rate variability (HRV) metrics which could be computed from a single electrocardiogram or optical sensor (often found in fitness wrist bands and smart watches). For this purpose, manual material handling experiments were conducted while state-of-the-art HRV features were used to perform physical exertion monitoring with ensemble classifiers and artificial neural network (ANN) based regression analysis. The results indicate that ensemble classifiers achieved accuracies from 64.2% to 81.2%, depending on the number of levels in which physical exertion data was divided, whereas ANN regression achieved the least root mean square error of 1.651. Given the wide availability of HRV sensors in fitness bands and wrist watches, this study highlights the usability and limitations of HRV based physical exertion monitoring which could help make informed decisions related to its adoption in physically demanding industries such as construction.

Physician Training for Electrocardiogram Interpretation: A Systematic Review and Meta-Analysis.

Using electrocardiogram (ECG) interpretation as an example of a widely taught diagnostic skill, the authors conducted a systematic review and meta-analysis to demonstrate how research evidence on instruction in diagnosis can be synthesized to facilitate improvement of educational activities (instructional modalities, instructional methods, and interpretation approaches), guide the content and specificity of such activities, and provide direction for research. The authors searched PubMed/MEDLINE, Embase, Cochrane CENTRAL, PsycInfo, CINAHL, ERIC, and Web of Science databases through February 21, 2020, for empirical investigations of ECG interpretation training enrolling medical students, residents, or practicing physicians. They appraised study quality with the Medical Education Research Study Quality Instrument and pooled standardized mean differences (SMDs) using random effects meta-analysis. Of 1,002 articles identified, 59 were included (enrolling 17,251 participants). Among 10 studies comparing instructional modalities, 8 compared computer-assisted and face-to-face instruction, with pooled SMD 0.23 (95% CI, 0.09, 0.36) indicating a small, statistically significant difference favoring computer-assisted instruction. Among 19 studies comparing instructional methods, 5 evaluated individual versus group training (pooled SMD -0.35 favoring group study [95% CI, -0.06, -0.63]), 4 evaluated peer-led versus faculty-led instruction (pooled SMD 0.38 favoring peer instruction [95% CI, 0.01, 0.74]), and 4 evaluated contrasting ECG features (e.g., QRS width) from 2 or more diagnostic categories versus routine examination of features within a single ECG or diagnosis (pooled SMD 0.23 not significantly favoring contrasting features [95% CI, -0.30, 0.76]). Eight studies compared ECG interpretation approaches, with pooled SMD 0.92 (95% CI, 0.48, 1.37) indicating a large, statistically significant effect favoring more systematic interpretation approaches. Some instructional interventions appear to improve learning in ECG interpretation; however, many evidence-based instructional strategies are insufficiently investigated. The findings may have implications for future research and design of training to improve skills in ECG interpretation and other types of visual diagnosis.