307 research outputs found
Detection of Obstructive Sleep Apnea from ECG Signal using SVM based Grid Search
Obstructive Sleep Apnea is one common form of sleep apnea and is now tested by means of a process called Polysomnography which is time-consuming, expensive and also requires a human observer throughout the study of the subject which makes it inconvenient and new detection techniques are now being developed to overcome these difficulties. Heart rate variability has proven to be related to sleep apnea episodes and thus the features from the ECG signal can be used in the detection of sleep apnea. The proposed detection technique uses Support Vector Machines using Grid search algorithm and the classifier is trained using features based on heart rate variability derived from the ECG signal. The developed system is tested using the dataset and the results show that this classification system can recognize the disorder with an accuracy rate of 89%. Further, the use of the grid search algorithm has made this system a reliable and an accurate means for the classification of sleep apnea and can serve as a basis for the future development of its screening
Atrial fibrillation classification based on MLP networks by extracting Jitter and Shimmer parameters
Atrial fibrillation (AF) is the most common cardiac anomaly and one that potentially threatens human life. Due to its relation to a
variation in cardiac rhythm during indeterminate periods, long-term observations are necessary for its diagnosis. With the increase
in data volume, fatigue and the complexity of long-term features make analysis an increasingly impractical process. Most medical
diagnostic aid systems based on machine learning, are designed to automatically detect, classify or predict certain behaviors. In
this work, using the PhysioNet MIT-BIH Atrial Fibrillation database, a system based on MLP artificial neural network is proposed
to differentiate, between AF and non-AF, segments and ECGβs features, obtaining average accuracy of 80.67% in test set, for the
10-fold cross-validation method. As a highlight, the extraction of jitter and shimmer parameters from ECG windows is presented
to compose the network input sets, indicating a slight improvement in the model's performance. Added to these, Shannon's and
logarithmic energy entropies are determined, also indicating an improvement in performance related to the use of fewer features.This work has been supported by FCT β Fundação para a CiΓͺncia e Tecnologia within the Project Scope: UIDB/05757/2020.info:eu-repo/semantics/publishedVersio
Application of artificial intelligence techniques for automated detection of myocardial infarction: A review
Myocardial infarction (MI) results in heart muscle injury due to receiving
insufficient blood flow. MI is the most common cause of mortality in
middle-aged and elderly individuals around the world. To diagnose MI,
clinicians need to interpret electrocardiography (ECG) signals, which requires
expertise and is subject to observer bias. Artificial intelligence-based
methods can be utilized to screen for or diagnose MI automatically using ECG
signals. In this work, we conducted a comprehensive assessment of artificial
intelligence-based approaches for MI detection based on ECG as well as other
biophysical signals, including machine learning (ML) and deep learning (DL)
models. The performance of traditional ML methods relies on handcrafted
features and manual selection of ECG signals, whereas DL models can automate
these tasks. The review observed that deep convolutional neural networks
(DCNNs) yielded excellent classification performance for MI diagnosis, which
explains why they have become prevalent in recent years. To our knowledge, this
is the first comprehensive survey of artificial intelligence techniques
employed for MI diagnosis using ECG and other biophysical signals.Comment: 16 pages, 8 figure
COMPUTER AIDED DIAGNOSIS OF VENTRICULAR ARRHYTHMIAS FROM ELECTROCARDIOGRAM LEAD II SIGNALS
In this work, we use computer aided diagnosis (CADx) to extract features from ECG signals and detect different types of cardiac ventricular arrhythmias including Ventricular Tachycardia (VT),Ventricular Fibrillation (VF), Ventricular Couplet (VC), and Ventricular Bigeminy (VB).Our methodology is unique in computing features of lower and higher order statistical parameters from six different data domains: time domain, Fourier domain, and four Wavelet domains (Daubechies, Coiflet, Symlet, and Meyer). These features proved to give superior classification performance, in general, regardless of the type of classifier used as compared with previous studies. However, Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers got better performance than other classifiers tried including KNN and NaΓ―ve Bayes classifiers. Our unique features enabled classifiers to perform better in comparison with previous studies: for VT, 100% accuracy while best previous work got 95.8%, for VF, 100% accuracy while best previous work got 97.5%, for VC, 100% sensitivity while best previous work got 71.8%, and for VB, 100% sensitivity while best previous work got 84.6%
Electrocardiogram Recognization Based on Variational AutoEncoder
Subtle distortions on electrocardiogram (ECG) can help doctors to diagnose some serious larvaceous heart sickness on their patients. However, it is difficult to find them manually because of disturbing factors such as baseline wander and high-frequency noise. In this chapter, we propose a method based on variational autoencoder to distinguish these distortions automatically and efficiently. We test our method on three ECG datasets from Physionet by adding some tiny artificial distortions. Comparing with other approaches adopting autoencoders [e.g., contractive autoencoder, denoising autoencoder (DAE)], the results of our experiment show that our method improves the performance of publically available on ECG analysis on the distortions
ΠΠ±Π·ΠΎΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎΠΉ Π°ΡΠΈΡΠΌΠΈΠΈ Π΄Π»Ρ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΎ Π½Π΅ΠΎΠ±Ρ ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΠΈ
Ventricular fibrillation is considered the most common cause of sudden cardiac arrest. The fibrillation, and ventricular tachycardia often preceding it, are cardiac rhythms that may respond to emergency electroshock therapy and return to normal sinus rhythm when diagnosed early after cardiac arrest with the restoration of adequate cardiac pumping function. However, manually checking ECG signals on the existence of a pattern of such arrhythmias is a risky and time-consuming task in stressful situations and practically impossible in the absence of a qualified medical specialist. Therefore, systems of the computer classification of arrhythmias with the function of making a decision on the necessity of electric cardioversion with the parameters of a high-voltage pulse calculated adaptively for each patient are widely used for the automatic diagnosis of such conditions. This paper discusses methods of analyzing the electrocardiographic signal taken from the electrodes of an external automatic or semi-automatic defibrillator in order to make a decision on the necessity for defibrillation, which are applicable in the embedded software of automatic and semiautomatic external defibrillators. The paper includes an overview of applicable filtering techniques as well as subsequent algorithms for extracting, classifying and compressing features for the ECG signal.Β Β Lipchak D. A., Chupov A. A. Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review. Ural Radio Engineering Journal. 2021;5(4):380β409. (In Russ.) DOI: 10.15826/ urej.2021.5.4.004.Β Π€ΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΡ ΠΆΠ΅Π»ΡΠ΄ΠΎΡΠΊΠΎΠ² ΡΠ΅ΡΠ΄ΡΠ° ΡΡΠΈΡΠ°Π΅ΡΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΡΠΎ Π²ΡΡΡΠ΅ΡΠ°ΡΡΠ΅ΠΉΡΡ ΠΏΡΠΈΡΠΈΠ½ΠΎΠΉ Π²Π½Π΅Π·Π°ΠΏΠ½ΠΎΠΉ ΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ ΡΠ΅ΡΠ΄ΡΠ°. Π’Π°ΠΊΠ°Ρ ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΡ ΠΈ ΡΠ°ΡΡΠΎ ΠΏΡΠ΅Π΄ΡΠ΅ΡΡΠ²ΡΡΡΠ°Ρ Π΅ΠΉ ΠΆΠ΅Π»ΡΠ΄ΠΎΡΠΊΠΎΠ²Π°Ρ ΡΠ°Ρ
ΠΈΠΊΠ°ΡΠ΄ΠΈΡ β ΡΡΠΎ ΡΠΈΡΠΌΡ ΡΠ΅ΡΠ΄ΡΠ°, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠΎΠ³ΡΡ ΡΠ΅Π°Π³ΠΈΡΠΎΠ²Π°ΡΡ Π½Π° ΡΠΊΡΡΡΠ΅Π½Π½ΡΡ ΡΠ»Π΅ΠΊΡΡΠΎΡΠΎΠΊΠΎΠ²ΡΡ ΡΠ΅ΡΠ°ΠΏΠΈΡ ΠΈ Π²Π΅ΡΠ½ΡΡΡΡΡ ΠΊ Π½ΠΎΡΠΌΠ°Π»ΡΠ½ΠΎΠΌΡ ΡΠΈΠ½ΡΡΠΎΠ²ΠΎΠΌΡ ΡΠΈΡΠΌΡ ΠΏΡΠΈ ΡΠ°Π½Π½Π΅ΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ ΠΏΠΎΡΠ»Π΅ ΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ ΡΠ΅ΡΠ΄ΡΠ° Ρ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ΠΌ Π°Π΄Π΅ΠΊΠ²Π°ΡΠ½ΠΎΠΉ Π½Π°ΡΠΎΡΠ½ΠΎΠΉ ΡΡΠ½ΠΊΡΠΈΠΈ ΡΠ΅ΡΠ΄ΡΠ°. ΠΠ΄Π½Π°ΠΊΠΎ ΡΡΡΠ½Π°Ρ ΠΏΡΠΎΠ²Π΅ΡΠΊΠ° ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΠΠ Π½Π° Π½Π°Π»ΠΈΡΠΈΠ΅ ΠΏΠ°ΡΡΠ΅ΡΠ½Π° ΡΠ°ΠΊΠΎΠΉ Π°ΡΠΈΡΠΌΠΈΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ»ΠΎΠΆΠ½ΠΎΠΉ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π·Π°Π΄Π°ΡΠ΅ΠΉ, ΡΡΠ΅Π±ΡΡΡΠ΅ΠΉ Π½Π΅ΠΌΠ΅Π΄Π»Π΅Π½Π½ΠΎΠ³ΠΎ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π² ΡΡΡΠ΅ΡΡΠΎΠ²ΠΎΠΉ ΡΠΈΡΡΠ°ΡΠΈΠΈ, ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈ Π½Π΅Π²ΡΠΏΠΎΠ»Π½ΠΈΠΌΠΎΠΉ Π² ΠΎΡΡΡΡΡΡΠ²ΠΈΠ΅ ΠΊΠ²Π°Π»ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΎΠ³ΠΎ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠ°. ΠΠΎΡΡΠΎΠΌΡ Π΄Π»Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΎΡΡΡΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ ΡΠΈΡΠΎΠΊΠΎΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΏΠΎΠ»ΡΡΠΈΠ»ΠΈ ΡΠΈΡΡΠ΅ΠΌΡ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π°ΡΠΈΡΠΌΠΈΠΉ Ρ ΡΡΠ½ΠΊΡΠΈΠ΅ΠΉ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΎ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΠΊΠ°ΡΠ΄ΠΈΠΎΡΠ΅ΡΠ°ΠΏΠΈΠΈ Ρ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ Π²ΡΡΠΎΠΊΠΎΠ²ΠΎΠ»ΡΡΠ½ΠΎΠ³ΠΎ ΠΈΠΌΠΏΡΠ»ΡΡΠ°, Π²ΡΡΠΈΡΠ»Π΅Π½Π½ΠΎΠ³ΠΎ Π°Π΄Π°ΠΏΡΠΈΠ²Π½ΠΎ Π΄Π»Ρ ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ°. Π Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΠΌΠ΅ΡΠΎΠ΄Ρ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ»Π΅ΠΊΡΡΠΎΠΊΠ°ΡΠ΄ΠΈΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Π°, ΡΠ½ΠΈΠΌΠ°Π΅ΠΌΠΎΠ³ΠΎ Ρ ΡΠ»Π΅ΠΊΡΡΠΎΠ΄ΠΎΠ² Π½Π°ΡΡΠΆΠ½ΠΎΠ³ΠΎ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈΠ»ΠΈ ΠΏΠΎΠ»ΡΠ°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΎΡΠ°, Ρ ΡΠ΅Π»ΡΡ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΎ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΎΠΊΠ°Π·Π°Π½ΠΈΡ Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΠΈ, ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠΌΡΠ΅ Π²ΠΎ Π²ΡΡΡΠΎΠ΅Π½Π½ΠΎΠΌ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠΌ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠΈ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΠΏΠΎΠ»ΡΠ°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π²Π½Π΅ΡΠ½ΠΈΡ
Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΎΡΠΎΠ². Π Π°Π±ΠΎΡΠ° Π²ΠΊΠ»ΡΡΠ°Π΅Ρ ΠΎΠ±Π·ΠΎΡ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠΌΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΠΈΠ»ΡΡΡΠ°ΡΠΈΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠΈΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΡ, ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΈ ΡΠΆΠ°ΡΠΈΡ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ½ΡΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π΄Π»Ρ ΡΠΈΠ³Π½Π°Π»Π° ΠΠΠ.Β Β ΠΠΈΠΏΡΠ°ΠΊ Π. Π., Π§ΡΠΏΠΎΠ² Π. Π. ΠΠ±Π·ΠΎΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎΠΉ Π°ΡΠΈΡΠΌΠΈΠΈ Π΄Π»Ρ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΎ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π΄Π΅ΡΠΈΠ±ΡΠΈΠ»Π»ΡΡΠΈΠΈ. Ural Radio Engineering Journal. 2021;5(4):380β409. DOI: 10.15826/urej.2021.5.4.004.
A Survey Study of the Current Challenges and Opportunities of Deploying the ECG Biometric Authentication Method in IoT and 5G Environments
The environment prototype of the Internet of Things (IoT) has opened the horizon for researchers to utilize such environments in deploying useful new techniques and methods in different fields and areas. The deployment process takes place when numerous IoT devices are utilized in the implementation phase for new techniques and methods. With the wide use of IoT devices in our daily lives in many fields, personal identification is becoming increasingly important for our society. This survey aims to demonstrate various aspects related to the implementation of biometric authentication in healthcare monitoring systems based on acquiring vital ECG signals via designated wearable devices that are compatible with 5G technology. The nature of ECG signals and current ongoing research related to ECG authentication are investigated in this survey along with the factors that may affect the signal acquisition process. In addition, the survey addresses the psycho-physiological factors that pose a challenge to the usage of ECG signals as a biometric trait in biometric authentication systems along with other challenges that must be addressed and resolved in any future related research.
Multi-Class Heart Abnormalities Detection Based on ECG Graph Using Transfer Learning Method
The heart is one of the vital organs in the circulatory system. Regular checkups are very important to prevent heart disease. The most basic examination is blood pressure then further examination is related to the evaluation of the electrical activity of the heart using an electrocardiogram (ECG). The ECG carries important information regarding various abnormalities of heart function. Several automated classification techniques have been proposed to facilitate diagnosis. However, not all digital ECG devices provide raw data for analysis. ECG classification method based on images can be an alternative in classification. Therefore, in this study, it is proposed to classify ECG based on signal images. The proposed classification method uses transfer learning with VGG, AlexNet, and DenseNet architectures. The method used for the classification of multi-class ECG consists of normal, PVC, Atrial Fibrilation, AFL, Bigeminy, LBBB, and APB. The simulation results generate the best accuracy of 92% and F1-score of 92%. Best performance is achieved using DenseNet architecture at 60 epochs. This study is expected to be a new reference technique in the classification of ECG signals
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