scholarly journals Diagnosing Abnormal Electrocardiogram (ECG) via Deep Learning

2020 ◽  
Author(s):  
Xin Gao
Keyword(s):  
Deep Learning ◽  
Electrocardiogram Ecg

scholarly journals BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets

2021 ◽  
Vol 11 (13) ◽  
pp. 5880
Author(s):  
Paloma Tirado-Martin ◽  
Raul Sanchez-Reillo
Keyword(s):  
Deep Learning ◽  
Temporal Variations ◽  
Physical Activities ◽  
Learning Tools ◽  
Biometric Recognition ◽  
Learning Network ◽  
Deep Learning Network ◽  
Electrocardiogram Ecg

Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases that can represent these conditions. Nonetheless, the restriction in database publications obstructs further research on this topic. This work was developed with the help of a database that represents potential scenarios in biometric recognition as data was acquired in different days, physical activities and positions. The classification was implemented with a Deep Learning network, BioECG, avoiding complex and time-consuming signal transformations. An exhaustive tuning was completed including variations in enrollment length, improving ECG verification for more complex and realistic biometric conditions. Finally, this work studied one-day and two-days enrollments and their effects. Two-days enrollments resulted in huge general improvements even when verification was accomplished with more unstable signals. EER was improved in 63% when including a change of position, up to almost 99% when visits were in a different day and up to 91% if the user experienced a heartbeat increase after exercise.

scholarly journals Reliable Deep Learning–Based Detection of Misplaced Chest Electrodes During Electrocardiogram Recording: Algorithm Development and Validation

10.2196/25347 ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. e25347
Author(s):  
Khaled Rjoob ◽  
Raymond Bond ◽  
Dewar Finlay ◽  
Victoria McGilligan ◽  
Stephen J Leslie ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ventricular Hypertrophy ◽  
Left Ventricular ◽  
Intercostal Space ◽  
The Past ◽  
Body Surface Potential Maps ◽  
Body Surface Potential ◽  
Using Data ◽  
Development And Validation ◽  
Electrocardiogram Ecg

Background A 12-lead electrocardiogram (ECG) is the most commonly used method to diagnose patients with cardiovascular diseases. However, there are a number of possible misinterpretations of the ECG that can be caused by several different factors, such as the misplacement of chest electrodes. Objective The aim of this study is to build advanced algorithms to detect precordial (chest) electrode misplacement. Methods In this study, we used traditional machine learning (ML) and deep learning (DL) to autodetect the misplacement of electrodes V1 and V2 using features from the resultant ECG. The algorithms were trained using data extracted from high-resolution body surface potential maps of patients who were diagnosed with myocardial infarction, diagnosed with left ventricular hypertrophy, or a normal ECG. Results DL achieved the highest accuracy in this study for detecting V1 and V2 electrode misplacement, with an accuracy of 93.0% (95% CI 91.46-94.53) for misplacement in the second intercostal space. The performance of DL in the second intercostal space was benchmarked with physicians (n=11 and age 47.3 years, SD 15.5) who were experienced in reading ECGs (mean number of ECGs read in the past year 436.54, SD 397.9). Physicians were poor at recognizing chest electrode misplacement on the ECG and achieved a mean accuracy of 60% (95% CI 56.09-63.90), which was significantly poorer than that of DL (P<.001). Conclusions DL provides the best performance for detecting chest electrode misplacement when compared with the ability of experienced physicians. DL and ML could be used to help flag ECGs that have been incorrectly recorded and flag that the data may be flawed, which could reduce the number of erroneous diagnoses.

scholarly journals Classification of cardiac arrhythmias using deep learning

2018 ◽  
Vol 7 (3.3) ◽  
pp. 401
Author(s):  
Jeong Hwan Kim ◽  
Jeong Whan Lee ◽  
Kyeong Seop Kim
Keyword(s):  
Deep Learning ◽  
Normal Sinus Rhythm ◽  
Premature Ventricular Contraction ◽  
Heart Rhythm ◽  
Future Research ◽  
Normal Sinus ◽  
Input Layer ◽  
Hidden Layer ◽  
Electrocardiogram Ecg

Background/Objectives: The main objective of this research is to design Deep Learning (DL) architecture to classify an electrocardiogram (ECG) signal into normal sinus rhythm (NSR), premature ventricular contraction (PVC), atrial premature contraction (APC) or right/left bundle branch block (RBBB/LBBB) arrhythmia by empirically optimizing the numbers of hidden layers, the number of neurons in each hidden layer and the number of neurons in input layer in DL model.Methods/Statistical analysis: For our experimental simulations, PhysioBank-MIT/BIH annotated ECG database was considered to classify heart beats into abnormal rhythms (PVC, APC, RBBB, LBBB) or normal sinus. The performance of classifying ECG beats by the proposed DL architecture was evaluated by computing the overall accuracy of classifying NSR or four different arrhythmias.Findings: Base on testing MIT/BIH arrhythmia database, the proposed DL model can classify the heart rhythm into one of NSR, PVC, APC, RBBB or LBBB beat with the mean accuracy of 95.5% by implementing DL architecture with 200 neurons in input layer, 100 neurons in the first and second hidden layer, respectively and 80 neurons in the 3rd hidden layer.Improvements/Applications: Our experimental results show that the proposed DL model might not be quite accurate for detecting APC beats due to its morphological resemblance of NSR. Therefore, we might need to design more sophisticated DL architecture by including more temporal characteristics of APC to increase the classification accuracy of APC arrhythmia in the future research efforts. 

scholarly journals Hyperglycemia Identification Using ECG in Deep Learning Era

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6263
Author(s):  
Renato Cordeiro ◽  
Nima Karimian ◽  
Younghee Park
Keyword(s):  
Deep Learning ◽  
Blood Glucose Concentration ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Physiological Signals ◽  
Ecg Signals ◽  
Non Invasive ◽  
Learning Classifier ◽  
Electrocardiogram Ecg ◽  
Smart Wearable

A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over the years, researchers have proposed different techniques to detect hyperglycemia using ECG. In this paper, we propose a novel deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals. In addition, we introduce a new fiducial feature extraction technique that improves the performance of the deep learning classifier. We evaluate the proposed method with ECG data from 1119 different subjects to assess the efficiency of hyperglycemia detection of the proposed work. The result indicates that the proposed algorithm is effective in detecting hyperglycemia with a 94.53% area under the curve (AUC), 87.57% sensitivity, and 85.04% specificity. That performance represents an relative improvement of 53% versus the best model found in the literature. The high sensitivity and specificity achieved by the 10-layer deep neural network proposed in this work provide an excellent indication that ECG possesses intrinsic information that can indicate the level of blood glucose concentration.

scholarly journals Robustness of convolutional neural networks to physiological electrocardiogram noise

2021 ◽  
Vol 379 (2212) ◽  
Author(s):  
J. Venton ◽  
P. M. Harris ◽  
A. Sundar ◽  
N. A. S. Smith ◽  
P. J. Aston
Keyword(s):  
Deep Learning ◽  
Careful Consideration ◽  
Training Data ◽  
Learning Methods ◽  
Ecg Signals ◽  
Open Questions ◽  
Image Transforms ◽  
Challenges And Opportunities ◽  
Electrocardiogram Ecg ◽  
Image Transformations

The electrocardiogram (ECG) is a widespread diagnostic tool in healthcare and supports the diagnosis of cardiovascular disorders. Deep learning methods are a successful and popular technique to detect indications of disorders from an ECG signal. However, there are open questions around the robustness of these methods to various factors, including physiological ECG noise. In this study, we generate clean and noisy versions of an ECG dataset before applying symmetric projection attractor reconstruction (SPAR) and scalogram image transformations. A convolutional neural network is used to classify these image transforms. For the clean ECG dataset, F1 scores for SPAR attractor and scalogram transforms were 0.70 and 0.79, respectively. Scores decreased by less than 0.05 for the noisy ECG datasets. Notably, when the network trained on clean data was used to classify the noisy datasets, performance decreases of up to 0.18 in F1 scores were seen. However, when the network trained on the noisy data was used to classify the clean dataset, the decrease was less than 0.05. We conclude that physiological ECG noise impacts classification using deep learning methods and careful consideration should be given to the inclusion of noisy ECG signals in the training data when developing supervised networks for ECG classification. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

scholarly journals Arrhythmia Classification Using One Dimensional Conventional Neural Network

2021 ◽  
Vol 13 (3) ◽  
pp. 43-58
Author(s):  
Sarah kamil ◽  
Lamia Muhammed
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Deep Learning ◽  
Disease Classification ◽  
Visual Interpretation ◽  
Deep Convolutional Neural Networks ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Short Period ◽  
Electrocardiogram Ecg

Arrhythmia is a heart condition that occurs due to abnormalities in the heartbeat, which means that the heart's electrical signals do not work properly, resulting in an irregular heartbeat or rhythm and thus defeating the pumping of blood. Some cases of arrhythmia are not considered serious, while others are very dangerous, life-threatening, and cause death in a short period of time. In the clinical routine, cardiac arrhythmia detection is performed by electrocardiogram (ECG) signals. The ECG is a significant diagnosis tool that is used to record the electrical activity of the heart, and its signals can reveal abnormal heart activity. However, because of their small amplitude and duration, visual interpretation of ECG signals is difficult. As a result, we present a significant approach for identifying arrhythmias using ECG signals. In this study, we proposed an approach based on Deep Learning (DL) technology that is a framework of nine-layer one-dimension Conventional Neural Network (1D CNN) for classifying automatically ECG signals into four cardiac conditions named: normal (N), Atrial Premature Beat (APB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The practical test of this work was executed with the benchmark MIT-BIH database. We achieved an average accuracy of 99%, precision of 98%, recall of 96.5%, specificity of 99.08%, and an F1-score of 95.75%. The obtained results were compared with some relevant models, and they showed that the proposed framework outperformed those models in some measures. The new approach’s performance indicates its success. Also, it has been shown that deep convolutional neural networks can be used efficiently in automated detection and, therefore, cardiovascular disease protection as well as help cardiologists in medical practice by saving time and effort. Keywords: 1-D CNN, Arrhythmia, Cardiovascular Disease, Classification, Deep learning, Electrocardiogram(ECG), MIT-BIH arrhythmia database.

scholarly journals A Low-cost, Low-energy Wearable ECG System with Cloud-Based Arrhythmia Detection

2020 ◽  
Author(s):  
Nurul Huda ◽  
Sadia Khan ◽  
Ragib Abid ◽  
Samiul Based Shuvo ◽  
Mir Maheen Labib ◽  
...  
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Health Condition ◽  
Smart Device ◽  
Ecg Monitoring ◽  
Arrhythmia Detection ◽  
Analog Front End ◽  
Cloud Server ◽  
Electrocardiogram Ecg ◽  
Monitoring Devices

Continuously monitoring the Electrocardiogram (ECG) is an essential tool for Cardiovascular Disease (CVD) patients. In low-resource countries, the hospitals and health centers do not have adequate ECG systems, and this unavailability exacerbates the patients' health condition. Lack of skilled physicians, limited availability of continuous ECG monitoring devices, and their high prices, all lead to a higher CVD burden in the developing countries. To address these challenges, we present a low-cost, low-power, and wireless ECG monitoring system with deep learning-based automatic arrhythmia detection. Flexible fabric-based design and the wearable nature of the device enhances the patient's comfort while facilitating continuous monitoring. An AD8232 chip is used for the ECG Analog Front-End (AFE) with two 450 mi-Ah Li-ion batteries for powering the device. The acquired ECG signal can be transmitted to a smart-device over Bluetooth and subsequently sent to a cloud server for analysis. A 1-D Convolutional Neural Network (CNN) based deep learning model is developed that provides an accuracy of 94.03% in classifying abnormal cardiac rhythm on the MIT-BIH Arrhythmia Database.

Arrhythmia Classification Using Deep Learning: A Review

2021 ◽  
Vol 18 ◽  
pp. 96-105
Author(s):  
Deepali Koppad
Keyword(s):  
Deep Learning ◽  
Human Error ◽  
Traditional Approach ◽  
Learning Technology ◽  
Ecg Signals ◽  
The Past ◽  
Medical Disorders ◽  
Medical Reports ◽  
Diagnosis Method ◽  
Electrocardiogram Ecg

In most hospitals, the diagnosis of medical disorders involves the traditional approach of doctors manually analyzing the medical reports of the patient. This method is not only time consuming and strenuous, but is also highly prone to human error. With the advent of deep learning technology, an efficient autonomous diagnosis method holds the possibility of replacing the existing tedious approach. This in turn results in the reduction of human error which is of major concern in the medical industry today. Through this paper, we aim to put forth an articulate review of the different deep learning methodologies, observed in the past four years, to classify arrhythmia using electrocardiogram (ECG) signals.

Sign in / Sign up

Export Citation Format

Share Document