Arrhythmias Prediction Using an Hybrid Model Based on Convolutional Neural Network and Nonlinear Regression

2020 ◽  
Vol 19 (03) ◽  
pp. 2050024
Author(s):  
Abdoul-Dalibou Abdou ◽  
Ndeye Fatou Ngom ◽  
Oumar Niang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Nonlinear Regression ◽  
Cardiac Arrhythmias ◽  
Heart Diseases ◽  
Heart Rhythm ◽  
Relevant Information ◽  
Biomedical Signal Processing ◽  
Ecg Signal ◽  
Extraction Step

In biomedical signal processing, artificial intelligence techniques are used for identifying and extracting relevant information. However, it lacks effective solutions based on machine learning for the prediction of cardiac arrhythmias. The heart diseases diagnosis rests essentially on the analysis of various properties of ECG signal. The arrhythmia is one of the most common heart diseases. A cardiac arrhythmia is a disturbance of the heart rhythm. It occurs when the heart beats too slowly, too fast or anarchically, with no apparent cause. The diagnosis of cardiac arrhythmias is based on the analysis of the ECG properties, especially, the durations (P, QRS, T), the amplitudes (P, Q, R, S, T), the intervals (PQ, QT, RR), the cardiac frequency and the rhythm. In this paper we propose a system of arrhythmias diagnosis assistance based on the analysis of the temporal and frequential properties of the ECG signal. After the features extraction step, the ECG properties are then used as input for a convolutional neural network to detect and classify the arrhythmias. Finally, the classification results are used to perform a prediction of arrhythmias with nonlinear regression model. The method is illustrated using the MIT-BIH database.

scholarly journals Intellectual heartbeats classification model for diagnosis of heart disease from ECG signal using hybrid convolutional neural network with GOA

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Ankita Tyagi ◽  
Ritika Mehra
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Heart Diseases ◽  
Classification Model ◽  
Ecg Signal ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Selection Of

AbstractAutomatic heart disease detection from human heartbeats is a challenging and intellectual assignment in signal processing because periodically monitoring of the heart beat arrhythmia for patient is an essential task to reduce the death rate due to cardiovascular disease (CVD). In this paper, the focus of research is to design hybrid Convolutional Neural Network (CNN) architecture by making use of Grasshopper Optimization Algorithm (GOA) to classify different types of heart diseases from the ECG signal or human heartbeats. Convolutional Neural Network (CNN) as an artificial intelligence approach is widely used in computer vision-based medical data analysis. However, the traditional CNN cannot be used for classification of heart diseases from the ECG signal because lots of noise or irrelevant data is mixed with signal. So this study utilizes the pre-processing and selection of feature for proper heart diseases classification, where Discrete Wavelet Transform (DWT) is used for the noise reduction as well as segmentation of ECG signal and Grasshopper Optimization Algorithm (GOA) is used for selection of R-peaks features from the extracted feature sets in terms of R-peaks and R-R intervals that help to attain better classification accuracy. For training as well as testing of projected Heartbeats Classification Model (HCM), the Standard MIT-BIH arrhythmia database is utilized with hybrid Convolutional Neural Network (CNN) architecture. The assortment of proper R-peaks and R-R intervals is a major factor and because of the deficiency of apposite pre-processing phases like noise removal, signal decomposition, smoothing and filtering, the uniqueness of extracted feature is less. The experimental outcomes show that the planned HCM is effective for detecting irregular human heartbeats via R-peaks and R-R intervals. When the proposed Heartbeats Classification Model (HCM) was verified on the database, model achieved higher efficiency than other state-of-the-art techniques for 16 heartbeat disease categories and the average classification accuracy is 99.58% with fast and robust responses where the correctly classified heartbeats are 86,005 and misclassified beats is only 108 with 0.42% error rate.

Evolutionary Algorithm-based Convolutional Neural Network for Predicting Heart Diseases

2021 ◽  
pp. 107651
Author(s):  
Ali A. Samir ◽  
Abdullah R. Rashwan ◽  
Karam M. Sallam ◽  
Ripon K. Chakrabortty ◽  
Michael J. Ryan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Evolutionary Algorithm ◽  
Heart Diseases

scholarly journals End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2085 ◽  
Author(s):  
Rami M. Jomaa ◽  
Hassan Mathkour ◽  
Yakoub Bazi ◽  
Md Saiful Islam
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Attack Detection ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Biometric Systems ◽  
Fingerprint Biometrics ◽  
End To End ◽  
The Impact

Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the robustness against a PA. We propose an alternative approach based on the combination of fingerprint and electrocardiogram (ECG) signals. An ECG signal has advantageous characteristics that prevent the replication. Combining a fingerprint with an ECG signal is a potentially interesting solution to reduce the impact of PAs in biometric systems. We also propose a novel end-to-end deep learning-based fusion neural architecture between a fingerprint and an ECG signal to improve PA detection in fingerprint biometrics. Our model uses state-of-the-art EfficientNets for generating a fingerprint feature representation. For the ECG, we investigate three different architectures based on fully-connected layers (FC), a 1D-convolutional neural network (1D-CNN), and a 2D-convolutional neural network (2D-CNN). The 2D-CNN converts the ECG signals into an image and uses inverted Mobilenet-v2 layers for feature generation. We evaluated the method on a multimodal dataset, that is, a customized fusion of the LivDet 2015 fingerprint dataset and ECG data from real subjects. Experimental results reveal that this architecture yields a better average classification accuracy compared to a single fingerprint modality.

scholarly journals Predicting Surgical Phases using CNN-NARX Neural Network

2019 ◽  
Vol 5 (1) ◽  
pp. 405-407
Author(s):  
Nour Aldeen Jalal ◽  
Tamer Abdulbaki Alshirbaji ◽  
Knut Möller
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Relevant Information ◽  
Surgical Tools ◽  
Use Of Data ◽  
Input Signals ◽  
Phase Prediction ◽  
Context Aware System ◽  
Laparoscopic Videos ◽  
Narx Neural Network

AbstractOnline recognition of surgical phases is essential to develop systems able to effectively conceive the workflow and provide relevant information to surgical staff during surgical procedures. These systems, known as context-aware system (CAS), are designed to assist surgeons, improve scheduling efficiency of operating rooms (ORs) and surgical team and promote a comprehensive perception and awareness of the OR. State-of-the-art studies for recognizing surgical phases have made use of data from different sources such as videos or binary usage signals from surgical tools. In this work, we propose a deep learning pipeline, namely a convolutional neural network (CNN) and a nonlinear autoregressive network with exogenous inputs (NARX), designed to predict surgical phases from laparoscopic videos. A convolutional neural network (CNN) is used to perform the tool classification task by automatically learning visual features from laparoscopic videos. The output of the CNN, which represents binary usage signals of surgical tools, is provided to a NARX neural network that performs a multistep-ahead predictions of surgical phases. Surgical phase prediction performance of the proposed pipeline was evaluated on a dataset of 80 cholecystectomy videos (Cholec80 dataset). Results show that the NARX model provides a good modelling of the temporal dependencies between surgical phases. However, more input signals are needed to improve the recognition accuracy.

scholarly journals Identification of Natural Selection in Genomic Data with Deep Convolutional Neural Network

2021 ◽  
Author(s):  
Arnaud Nguembang Fadja ◽  
Fabrizio Riguzzi ◽  
Giorgio Bertorelle ◽  
Emiliano Trucchi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Natural Selection ◽  
Convolutional Neural Network ◽  
Simulated Data ◽  
Genomic Data ◽  
Relevant Information ◽  
Supervised Machine Learning ◽  
Selection Processes ◽  
Adaptive Processes

Abstract Background: With the increase in the size of genomic datasets describing variability in populations, extracting relevant information becomes increasingly useful as well as complex. Recently, computational methodologies such as Supervised Machine Learning and specifically Convolutional Neural Networks have been proposed to order to make inferences on demographic and adaptive processes using genomic data, Even though it was already shown to be powerful and efficient in different fields of investigation, Supervised Machine Learning has still to be explored as to unfold its enormous potential in evolutionary genomics. Results: The paper proposes a method based on Supervised Machine Learning for classifying genomic data, represented as windows of genomic sequences from a sample of individuals belonging to the same population. A Convolutional Neural Network is used to test whether a genomic window shows the signature of natural selection. Experiments performed on simulated data show that the proposed model can accurately predict neutral and selection processes on genomic data with more than 99% accuracy.

scholarly journals Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7731 ◽  
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Guohao Shen ◽  
Feng Hong
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Single Channel ◽  
Feature Representation ◽  
Training Data ◽  
Ecg Signal ◽  
Automatic Feature Extraction ◽  
Adjacent Segments ◽  
The Impact

Sleep apnea (SA) is the most common respiratory sleep disorder, leading to some serious neurological and cardiovascular diseases if left untreated. The diagnosis of SA is traditionally made using Polysomnography (PSG). However, this method requires many electrodes and wires, as well as an expert to monitor the test. Several researchers have proposed instead using a single channel signal for SA diagnosis. Among these options, the ECG signal is one of the most physiologically relevant signals of SA occurrence, and one that can be easily recorded using a wearable device. However, existing ECG signal-based methods mainly use features (i.e. frequency domain, time domain, and other nonlinear features) acquired from ECG and its derived signals in order to construct the model. This requires researchers to have rich experience in ECG, which is not common. A convolutional neural network (CNN) is a kind of deep neural network that can automatically learn effective feature representation from training data and has been successfully applied in many fields. Meanwhile, most studies have not considered the impact of adjacent segments on SA detection. Therefore, in this study, we propose a modified LeNet-5 convolutional neural network with adjacent segments for SA detection. Our experimental results show that our proposed method is useful for SA detection, and achieves better or comparable results when compared with traditional machine learning methods.

scholarly journals P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images

2020 ◽  
Vol 10 (3) ◽  
pp. 976
Author(s):  
Rana N. Costandy ◽  
Safa M. Gasser ◽  
Mohamed S. El-Mahallawy ◽  
Mohamed W. Fakhr ◽  
Samir Y. Marzouk
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Convolutional Neural Network ◽  
P Wave ◽  
Ecg Signal ◽  
P Waves ◽  
Ecg Signals ◽  
Wave Detection ◽  
Automated Algorithm ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) signal analysis is a critical task in diagnosing the presence of any cardiac disorder. There are limited studies on detecting P-waves in various atrial arrhythmias, such as atrial fibrillation (AFIB), atrial flutter, junctional rhythm, and other arrhythmias due to P-wave variability and absence in various cases. Thus, there is a growing need to develop an efficient automated algorithm that annotates a 2D printed version of P-waves in the well-known ECG signal databases for validation purposes. To our knowledge, no one has annotated P-waves in the MIT-BIH atrial fibrillation database. Therefore, it is a challenge to manually annotate P-waves in the MIT-BIH AF database and to develop an automated algorithm to detect the absence and presence of different shapes of P-waves. In this paper, we present the manual annotation of P-waves in the well-known MIT-BIH AF database with the aid of a cardiologist. In addition, we provide an automatic P-wave segmentation for the same database using a fully convolutional neural network model (U-Net). This algorithm works on 2D imagery of printed ECG signals, as this type of imagery is the most commonly used in developing countries. The proposed automatic P-wave detection method obtained an accuracy and sensitivity of 98.56% and 98.78%, respectively, over the first 5 min of the second lead of the MIT-BIH AF database (a total of 8280 beats). Moreover, the proposed method is validated using the well-known automatically and manually annotated QT database (a total of 11,201 and 3194 automatically and manually annotated beats, respectively). This results in accuracies of 98.98 and 98.9%, and sensitivities of 98.97 and 97.24% for the automatically and manually annotated QT databases, respectively. Thus, these results indicate that the proposed automatic method can be used for analyzing long-printed ECG signals on mobile battery-driven devices using only images of the ECG signals, without the need for a cardiologist.

scholarly journals Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 170
Author(s):  
Muhammad Wasimuddin ◽  
Khaled Elleithy ◽  
Abdelshakour Abuzneid ◽  
Miad Faezipour ◽  
Omar Abuzaghleh
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Signal Analysis ◽  
Neural Network Modeling ◽  
Ecg Signal ◽  
Analysis Model ◽  
Learning Methods ◽  
Machine Learning Methods

Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of main interest among researchers, as its early diagnosis can prevent life threatening cardiac conditions and potentially save human lives. Analyzing the Electrocardiogram (ECG) can provide valuable diagnostic information to detect different types of cardiac arrhythmia. Real-time ECG monitoring systems with advanced machine learning methods provide information about the health status in real-time and have improved user’s experience. However, advanced machine learning methods have put a burden on portable and wearable devices due to their high computing requirements. We present an improved, less complex Convolutional Neural Network (CNN)-based classifier model that identifies multiple arrhythmia types using the two-dimensional image of the ECG wave in real-time. The proposed model is presented as a three-layer ECG signal analysis model that can potentially be adopted in real-time portable and wearable monitoring devices. We have designed, implemented, and simulated the proposed CNN network using Matlab. We also present the hardware implementation of the proposed method to validate its adaptability in real-time wearable systems. The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and achieved an accuracy of 99.23%, outperforming most existing solutions.

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