scholarly journals Automatic ECG Diagnosis Using Convolutional Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 951 ◽  
Author(s):  
Roberta Avanzato ◽  
Francesco Beritelli
Keyword(s):  
Confusion Matrix ◽  
Premature Ventricular Contraction ◽  
Disease Diagnosis ◽  
Ecg Signals ◽  
Testing Dataset ◽  
Life Threatening ◽  
Causes Of Mortality ◽  
Atrial Premature Beat ◽  
Heart Disease Diagnosis ◽  
Electrocardiogram Ecg

Cardiovascular disease (CVD) is the most common class of chronic and life-threatening diseases and, therefore, considered to be one of the main causes of mortality. The proposed new neural architecture based on the recent popularity of convolutional neural networks (CNN) was a solution for the development of automatic heart disease diagnosis systems using electrocardiogram (ECG) signals. More specifically, ECG signals were passed directly to a properly trained CNN network. The database consisted of more than 4000 ECG signal instances extracted from outpatient ECG examinations obtained from 47 subjects: 25 males and 22 females. The confusion matrix derived from the testing dataset indicated 99% accuracy for the “normal” class. For the “atrial premature beat” class, ECG segments were correctly classified 100% of the time. Finally, for the “premature ventricular contraction” class, ECG segments were correctly classified 96% of the time. In total, there was an average classification accuracy of 98.33%. The sensitivity (SNS) and the specificity (SPC) were, respectively, 98.33% and 98.35%. The new approach based on deep learning and, in particular, on a CNN network guaranteed excellent performance in automatic recognition and, therefore, prevention of cardiovascular diseases.

FRACTAL ANALYSIS OF THE ELECTROCARDIOGRAM SIGNAL

2014 ◽  
Vol 14 (04) ◽  
pp. 1450055 ◽  
Author(s):  
IBTICEME SEDJELMACI ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Premature Ventricular Contraction ◽  
Multifractal Spectrum ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Linear Approach ◽  
Atrial Premature Beat ◽  
The Subject ◽  
Electrocardiogram Signal ◽  
Electrocardiogram Ecg

In this paper, the analysis of the electrocardiogram (ECG) signal is carried out according a non-linear approach. This concerns the eventual fractal behavior of such signal and the correlation of such behavior with normal and pathological ECG signals. The analysis is carried out on different ECG signals taken from the MIT-BIH arrhythmia database. In fact these signals are those of six subjects with different ages and presenting both normal and abnormal arrhythmias situations. The abnormal situations are atrial premature beat (APB), premature ventricular contraction (PVC), right bundle branch block (RBBB) and left bundle branch block (LBBB). The fractal behavior of these signals is analyzed according to the determination of the multifractal spectrum and the fractal dimension variations and looking for eventually a fractal signature of each heart disease and age of the subject. The obtained results show a fractal signature according to the age and the pathologies for the studied cases. However further investigations are required on larger databases to confirm such results.

A NOVEL TWO-LEAD ARRHYTHMIA CLASSIFICATION SYSTEM BASED ON CNN AND LSTM

2019 ◽  
Vol 19 (03) ◽  
pp. 1950004 ◽  
Author(s):  
JINGHUI CHU ◽  
HONG WANG ◽  
WEI LU
Keyword(s):  
Disease Diagnosis ◽  
Medical Instrumentation ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Ecg Signals ◽  
Deep Learning Features ◽  
Heart Disease Diagnosis ◽  
Electrocardiogram Ecg ◽  
Multi Classification

Arrhythmia classification is useful during heart disease diagnosis. Although well-established for intra-patient diagnoses, inter-patient arrhythmia classification remains difficult. Most previous work has focused on the intra-patient condition and has not followed the Association for the Advancement of Medical Instrumentation (AAMI) standards. Here, we propose a novel system for arrhythmia classification based on multi-lead electrocardiogram (ECG) signals. The core of the design is that we fuse two types of deep learning features with some common traditional features and select discriminating features using a binary particle swarm optimization algorithm (BPSO). Then, the feature vector is classified using a weighted support vector machine (SVM) classifier. For a better generalization of the model and to draw fair comparisons, we carried out inter-patient experiments and followed the AAMI standards. We found that, when using common metrics aimed at multi-classification either macro- or micro-averaging, our system outperforms most other state-of-the-art methods.

AUTOMATED CHARACTERIZATION OF CARDIOVASCULAR DISEASES USING WAVELET TRANSFORM FEATURES EXTRACTED FROM ECG SIGNALS

2019 ◽  
Vol 19 (01) ◽  
pp. 1940009 ◽  
Author(s):  
AHMAD MOHSIN ◽  
OLIVER FAUST
Keyword(s):  
Wavelet Transform ◽  
Computer Program ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Disease Diagnosis ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Training Set ◽  
Ecg Signals

Cardiovascular disease has been the leading cause of death worldwide. Electrocardiogram (ECG)-based heart disease diagnosis is simple, fast, cost effective and non-invasive. However, interpreting ECG waveforms can be taxing for a clinician who has to deal with hundreds of patients during a day. We propose computing machinery to reduce the workload of clinicians and to streamline the clinical work processes. Replacing human labor with machine work can lead to cost savings. Furthermore, it is possible to improve the diagnosis quality by reducing inter- and intra-observer variability. To support that claim, we created a computer program that recognizes normal, Dilated Cardiomyopathy (DCM), Hypertrophic Cardiomyopathy (HCM) or Myocardial Infarction (MI) ECG signals. The computer program combined Discrete Wavelet Transform (DWT) based feature extraction and K-Nearest Neighbor (K-NN) classification for discriminating the signal classes. The system was verified with tenfold cross validation based on labeled data from the PTB diagnostic ECG database. During the validation, we adjusted the number of neighbors [Formula: see text] for the machine learning algorithm. For [Formula: see text], training set has an accuracy and cross validation of 98.33% and 95%, respectively. However, when [Formula: see text], it showed constant for training set but dropped drastically to 80% for cross-validation. Hence, training set [Formula: see text] prevails. Furthermore, a confusion matrix proved that normal data was identified with 96.7% accuracy, 99.6% sensitivity and 99.4% specificity. This means an error of 3.3% will occur. For every 30 normal signals, the classifier will mislabel only 1 of the them as HCM. With these results, we are confident that the proposed system can improve the speed and accuracy with which normal and diseased subjects are identified. Diseased subjects can be treated earlier which improves their probability of survival.

scholarly journals Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

2020 ◽  
Vol 12 (10) ◽  
pp. 1685 ◽  
Author(s):  
Amin Ullah ◽  
Syed Muhammad Anwar ◽  
Muhammad Bilal ◽  
Raja Majid Mehmood
Keyword(s):  
Premature Ventricular Contraction ◽  
Careful Study ◽  
Spectral Image ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
The One ◽  
Short Time ◽  
Electrocardiogram Ecg ◽  
Better Than

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.

scholarly journals Please pay attention to the competency of Chinese primary clinical medical workers in electrocardiogrophic interpretation skills

2019 ◽  
Author(s):  
Yu-lei Gao ◽  
Yan-hua Li ◽  
Jie Li ◽  
Chun-xue Wang ◽  
Yan-cun Liu ◽  
...  
Keyword(s):  
Disease Diagnosis ◽  
Effective Rate ◽  
Diagnosis And Treatment ◽  
The Internet ◽  
Cross Sectional ◽  
Professional Qualification ◽  
Treatment Services ◽  
Life Threatening ◽  
Electrocardiogram Ecg ◽  
Overall Reliability

Abstract Background: Improving the capacity of health care, disease diagnosis and treatment of primary medical workers was the key to hierarchical diagnosis and treatment services. Electrocardiogram (ECG) played an important role in the diagnosis of cardiac diseases and should be regarded as the most basic tool for primary clinical medical workers in China. Objectives: To investigate the competency of Chinese medical workers in electrocardiographic interpretation skills, especially in the primary care hospitals, to explore the insufficiency of electrocardiographic interpretation skills and the improvement methods. Methods: A cross-sectional questionnaire study was conducted via the internet from March to October 2019. The questionnaire consists of 6 parts: basic information, equipping with an electrocardiograph, operating electrocardiograph, receiving electrocardiographic theory, testing of electrocardiographic interpretation, and improving electrocardiographic interpretation skills. Results: The effective quantity of this questionnaire was 2307, with an effective rate of 96.57%. The overall reliability was α=0.895. There were no significant differences among primary, private and class-Ⅲ hospitals in the aspects of equipping with an electrocardiograph (χ2=3.794, 3.104, P＞0.05), operating the electrocardiograph (χ2=1.857, P＞0.05) and receiving electrocardiographic theoretical study (χ2=6.701, P＞0.05). Medical workers in private and class-Ⅲ hospitals had a stronger interpretation competency of ECG, including common or life-threatening ECGs (P＜0.01). The development of talent echelon in primary hospitals affected the electrocardiographic interpretation skills of medical workers (P＜0.01). In primary hospitals, the age was mainly ≥ 40 years (79.0%), the education background was mainly bachelor and below degree (80.9%), the professional qualification was mainly physician assistant (58.6%) and primary physician (31.4%). The interpretation competency of ECG of medical workers in private or class-Ⅲ hospitals was higher than that in primary hospitals (P＜0.05 or 0.01). Conclusions: In China, the examination of electrocardiograph had been popularized. Due to the unreasonable development of talent echelon in primary hospitals, the electrocardiographic interpretation skills of primary medical workers were worrying. We should improve electrocardiographic teaching methods with the help of the internet, to enhance the electrocardiographic interpretation skills of primary medical workers.

VT/VF Detection Method Based on ECG Signal Quality Assessment

2018 ◽  
Vol 27 (11) ◽  
pp. 1850169 ◽  
Author(s):  
Borisav Jovanović ◽  
Srdan Milenković ◽  
Milan Pavlović
Keyword(s):  
Cardiac Arrhythmias ◽  
Signal Analysis ◽  
Detection Method ◽  
Signal Quality ◽  
Ecg Signals ◽  
Battery Charge ◽  
Time Domain Signal ◽  
Signal Parameters ◽  
Life Threatening ◽  
Electrocardiogram Ecg

Artefacts which are present in electrocardiogram (ECG) recordings distort detection of life-threatening arrhythmias such as ventricular tachycardia and ventricular fibrillation. The method examines single ECG lead and exploits time domain signal parameters for real-time detection of severe cardiac arrhythmias. The method is dedicated to implementation in mobile ECG telemetry systems, which are designed by using low-power microcontrollers, operating more than a week on a single battery charge. The method has been validated on publicly available databases and the results are presented. We verified our method on ECG signals obtained without pre-selection meaning that the noisy intervals were not omitted from signal analysis.

From fabric to smart T-shirt: fine tuning an improved robust system to detect arrhythmia

2021 ◽  
pp. 004051752110608
Author(s):  
Abdel Salam Malek ◽  
Ashraf Elnahrawy ◽  
Hamed Anwar ◽  
Mohamed Naeem
Keyword(s):  
Research Work ◽  
Outer Part ◽  
Premature Ventricular Contraction ◽  
Fine Tuning ◽  
Quality Data ◽  
High Quality Data ◽  
Ecg Signals ◽  
Weft Knitted Fabric ◽  
Smart Garments ◽  
Electrocardiogram Ecg

Wearable electrocardiogram (ECG) systems should be comfortable, non-stigmatizing, and capable of producing high-quality data. Many different designs of wearable textile ECG systems have recently emerged. Some of them are not considered to be smart garments, whereas most of the others present only the electronic side of the system. Our research work introduces a comprehensive study for an improved single-lead ECG smart shirt to identify automatically premature ventricular contraction as a common form of arrhythmia. For artifact-free results, Marvelous Designer is implemented to design our optimized relaxed slim fit shirt. In addition, a weft-knitted fabric of 80% nylon–20% spandex is used to manufacture the outer part of the shirt. Moreover, lightweight and small size electronic components are integrated to the outer part via low-resistance dry textile electrodes and 100% cotton fabric as an inner layer for easy transmission of weak ECG signals.

scholarly journals Heart Disease Diagnosis Using Electrocardiography (ECG) Signals

2022 ◽  
Vol 32 (1) ◽  
pp. 31-43
Author(s):  
V. R. Vimal ◽  
P. Anandan ◽  
N. Kumaratharan
Keyword(s):  
Heart Disease ◽  
Disease Diagnosis ◽  
Ecg Signals ◽  
Heart Disease Diagnosis

PVC arrhythmia classification based on fractional order system modeling

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Imen Assadi ◽  
Abdelfatah Charef ◽  
Tahar Bensouici
Keyword(s):  
Fractional Order ◽  
System Modeling ◽  
Premature Ventricular Contraction ◽  
Order System ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Fractal Structures ◽  
Life Threatening ◽  
Electrocardiogram Ecg

Abstract It is well known that many physiological phenomena are modeled accurately and effectively using fractional operators and systems. This type of modeling is due mainly to the dynamical link between fractional-order systems and the fractal structures of the physiological systems. The automatic characterization of the premature ventricular contraction (PVC) is very important for early diagnosis of patients with different life-threatening cardiac diseases. In this paper, a classification scheme of normal and PVC beats of the electrocardiogram (ECG) signal is proposed. The clustering features used for normal and PVC beats discrimination are the parameters of the commensurate order linear fractional model of the frequency content of the QRS complex of the ECG signal. A series of tests and comparisons have been performed to evaluate and validate the efficiency of the proposed PVC classification algorithm using the MIT-BIH arrhythmia database. The proposed PVC classification method has achieved an overall accuracy of 94.745%, a specificity of 95.178% and a sensitivity of 90.021% using all the 48 records of the database.

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