scholarly journals An Approach of Neural Network For Electrocardiogram Classification

2020 ◽  
Vol 1 (3) ◽  
pp. 119-127
Author(s):  
Mayank Kumar Gautam ◽  
Vinod Kumar Giri
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Graphical Representation ◽  
Vital Role ◽  
Electrical Signal ◽  
Ecg Signal ◽  
Poincaré Plot ◽  
Cardiac Muscles ◽  
Almost All

ECG is basically the graphical representation of the electrical activity of cardiac muscles duringcontraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to thisearly detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated bythe cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). It acts as a vital physiologicalparameter, which is being used exclusively to know the state of the cardiac patients. Feature extraction of ECG playsa vital role in the manual as well as automatic analysis of ECG. In this paper the study of the concept of patternrecognition of ECG is done. It refers to the classification of data patterns and characterizing them into classes ofpredefined set. The analysis ECG signal falls under the application of pattern recognition. The ECG signal generatedwaveform gives almost all information about activity of the heart. The ECG signal feature extraction parameters suchas spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includesartificial neural network as a classifier for identifying the abnormalities of heart disease.

scholarly journals An Approach of Neural Network For Electrocardiogram Classification

2016 ◽  
Vol 1 (3) ◽  
pp. 119-127 ◽  
Author(s):  
Mayank Kumar Gautam ◽  
Vinod Kumar Giri
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Graphical Representation ◽  
Vital Role ◽  
Electrical Signal ◽  
Physiological Parameter ◽  
Ecg Signal ◽  
Poincaré Plot ◽  
Cardiac Muscles

ECG is basically the graphical representation of the electrical activity of cardiac muscles during contraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to this early detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated by the cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). It acts as a vital physiological parameter, which is being used exclusively to know the state of the cardiac patients. Feature extraction of ECG plays a vital role in the manual as well as automatic analysis of ECG. In this paper the study of the concept of pattern recognition of ECG is done. It refers to the classification of data patterns and characterizing them into classes of predefined set. The analysis ECG signal falls under the application of pattern recognition. The ECG signal generated waveform gives almost all information about activity of the heart. The ECG signal feature extraction parameters such as spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includes artificial neural network as a classifier for identifying the abnormalities of heart disease.

scholarly journals FACE RECOGNITION IN EIGEN DOMAIN WITH NEURO-FUZZY CLASSIFIER AND EVOLUTIONARY OPTIMIZATION

2012 ◽  
pp. 129-133
Author(s):  
PRASANTH.R. S ◽  
SARITHA. R
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Extraction ◽  
Face Recognition ◽  
Optimization Technique ◽  
Recognition Rate ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy ◽  
Extraction Test ◽  
Almost All

Face Recognition is a nascent field of research with many challenges. The proposed system focuses on recognizing faces in a faster and more accurate way using eigenface approach and genetic algorithm by considering the entire problem as an optimization problem. It consists of two stages: Eigenface approach is used for feature extraction and genetic algorithm based feed forward Neuro-Fuzzy System is used for face recognition. Classification of face images to a particular class is done using an artificial neural network. The training of neural network is done using genetic algorithm, a machine learning approach which optimizes the weights used in the neural network. This is an efficient optimization technique and an evolutionary classification method. The algorithm has been tested on 200 images (20 classes). A recognition score for test lot is calculated by considering almost all the variants of feature extraction. Test results gave a recognition rate of 97.01%.

scholarly journals Fast and Accurate Algorithm for ECG Authentication Using Residual Depthwise Separable Convolutional Neural Networks

2020 ◽  
Vol 10 (9) ◽  
pp. 3304 ◽  
Author(s):  
Eko Ihsanto ◽  
Kalamullah Ramli ◽  
Dodi Sudiana ◽  
Teddy Surya Gunawan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Processing Speed ◽  
State Of The Art ◽  
Ecg Signal ◽  
Biometric Authentication ◽  
Beat Detection ◽  
Two Stages ◽  
Electrocardiogram Ecg

The electrocardiogram (ECG) is relatively easy to acquire and has been used for reliable biometric authentication. Despite growing interest in ECG authentication, there are still two main problems that need to be tackled, i.e., the accuracy and processing speed. Therefore, this paper proposed a fast and accurate ECG authentication utilizing only two stages, i.e., ECG beat detection and classification. By minimizing time-consuming ECG signal pre-processing and feature extraction, our proposed two-stage algorithm can authenticate the ECG signal around 660 μs. Hamilton’s method was used for ECG beat detection, while the Residual Depthwise Separable Convolutional Neural Network (RDSCNN) algorithm was used for classification. It was found that between six and eight ECG beats were required for authentication of different databases. Results showed that our proposed algorithm achieved 100% accuracy when evaluated with 48 patients in the MIT-BIH database and 90 people in the ECG ID database. These results showed that our proposed algorithm outperformed other state-of-the-art methods.

scholarly journals Robust Iris Classification Based on Deep Neural Network (DNN) and Stationary Wavelet Transform (SWT)

2019 ◽  
pp. 198-204
Author(s):  
Priyanka S ◽  
Pavithra V ◽  
Pavithra M ◽  
S. Bhuvana
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Major Part ◽  
Deep Neural Network ◽  
Local Binary Pattern ◽  
Vital Role ◽  
Stationary Wavelet Transform ◽  
Huge Amount

The eye is a vital part of our body. It consists of several layers like sclera, retina, tunica, and iris. Among these several layers, Iris plays a vital role in human visionary. There are various infections which affect the Iris functioning. The sign, symptoms, and diagnosis of this is still a challenge for doctors. To overcome this many techniques and technologies have been introduced. But still, the existing system has several drawbacks in recognition like a huge amount of dataset, classification, extraction, etc. To overcome this we propose a system where Deep Neural Network plays a major part. It classifies the iris disease in our eyes in a more clear and precise manner. In additional to Deep Neural Network several other algorithms have been used like Stationary Wavelet Transform, for image selection and recognition, Local Binary Pattern, for Feature extraction and at a final stage Deep Neural Network for classification of Iris images.

scholarly journals Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach

2021 ◽  
Vol 3 (1) ◽  
pp. 1-16
Author(s):  
Vijayakumar T ◽  
Vinothkanna R ◽  
Duraipandian M
Keyword(s):  
Feature Extraction ◽  
Research Work ◽  
Electrical Signal ◽  
The Body ◽  
Handheld Devices ◽  
Ecg Signal ◽  
Data Set ◽  
Raw Data ◽  
Feature Extraction Method

Our human heart is classified into four sections called the left side and right side of the atrium and ventricle accordingly. Monitoring and taking care of the heart of every human is the very essential part. Therefore, the early prediction is essential to save and give awareness to humans about diet plan, lifestyle schedule. Also, this is used to improve the clinical diagnosis and treatment of any patients. To predict or identifying any cardiovascular problems, Electro Cardio Gram (ECG) is used to record the electrical signal of the heart from the body surface of humans. The algorithm learns the dataset from before cluster is called supervised; The algorithm learns to train the data from the set of a dataset is called unsupervised. Then the classification of more amount of heartbeat for different category of normal, abnormal, irregular heartbeats to detect cardiovascular diseases. In this research article, a comparison of various methods to classify the dataset with a fusion-based feature extraction method. Besides, our research work consists of a de-noising filter to reconstruct the raw data from the original input. Our proposed framework performing preprocessing that consists of a filtering approach to remove noises from the raw data set. The signal is affected by thermal noise and instrumentation noise, calibration noise due to power line fluctuation. This interference is high in many handheld devices which can be eliminated by de-noising filters. The output of the de-noising filter is input for fusion-based feature extraction and prediction model construction. This workflow progress has given good results of classifier effectiveness and imbalance arrangement conditions. We achieved good accuracy 96.5% and minimum computation time for classification of ECG signal.

scholarly journals Classification of ECG Signal using Artificial Neural Network

2020 ◽  
Vol 8 (6) ◽  
pp. 1545-1548
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Work Load ◽  
Health Condition ◽  
Ecg Signal ◽  
Bundle Branch Block ◽  
Normal Sinus ◽  
Artificial Neural

Electrocardiogram (ECG) is one of the significant investigative tool used in determining the health condition of heart. The raise in number of heart patients has necessitated a technique for automatic determination of diverse abnormalities of heart for lessening the pressure on the specialists or sharing their work load. The work presented in this paper facilitates in generating a computer based system that assists in categorizing the ECG signals. Artificial Neural Network (ANN) is been used for the classification of the signal. The various steps used for the determination of type of ECG signal are preprocessing, Feature extraction & selection and classification. The considered neural network is used to classify the six categories of arrhythmias named Normal Sinus, Right Bundle Branch Block (RBBB), Atrial Premature Beat (APB), Left Bundle Branch Block (LBBB), Arterial fibrillation, PVC. The simulation is done in MATLAB. The obtained results shows that the proposed classifier shows the enhanced performance sensitivity 95%, Specificity99% and classification accuracy 98%. This work provides the comparative analysis of the performance of proposed classifier with KNN, ANFIS and Naive Bias. The results shows the performance of proposed technique is better than other techniques.

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