R peak detection and feature extraction for the diagnosis of heart diseases

2017 ◽  
Author(s):  
O. N. Swathi ◽  
M. Ganesan ◽  
R. Lavanya
Keyword(s):  
Feature Extraction ◽  
Heart Diseases ◽  
Peak Detection

scholarly journals Detection and Classification of R-Peak Using Naïve Bayes Classifier

2018 ◽  
Vol 7 (3.27) ◽  
pp. 397 ◽  
Author(s):  
S Celin ◽  
K Vasanth
Keyword(s):  
Feature Extraction ◽  
Performance Measures ◽  
Heart Diseases ◽  
Processing Technique ◽  
Challenging Problem ◽  
Bayes Classifier ◽  
Extraction Techniques ◽  
And Performance ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) in classification of signals plays a major role in the diagnoses of heart diseases. The main challenging problem is the classification of accurate ECG. Here in this paper the ECG is classified into arrhythmia types. It is very important that detecting the heart disease and finding the treatment for the patient at the earliest must be done accurately. In the ECG classification different classifiers are available. The best accuracy value of 99.7% is produced by using the Bayes classifiers in this paper. ECG databases, classifiers, feature extraction techniques and performance measures are presented in the pre-processing technique. And also the classification of ECG, analysis of input beat selection and the output of classifiers are also discussed in this paper.  

HEART SOUND CLASSIFICATION AND RECOGNITION BASED ON EEMD AND CORRELATION DIMENSION

2014 ◽  
Vol 14 (04) ◽  
pp. 1450046 ◽  
Author(s):  
WENYING ZHANG ◽  
XINGMING GUO ◽  
ZHIHUI YUAN ◽  
XINGHUA ZHU
Keyword(s):  
Feature Extraction ◽  
Heart Sound ◽  
Linear Time ◽  
Heart Diseases ◽  
Recognition Rate ◽  
Heart Sounds ◽  
Support Vector ◽  
Svm Classifier ◽  
Intrinsic Mode Functions ◽  
Sound Classification

Analysis of heart sound is of great importance to the diagnosis of heart diseases. Most of the feature extraction methods about heart sound have focused on linear time-variant or time-invariant models. While heart sound is a kind of highly nonstationary and nonlinear vibration signal, traditional methods cannot fully reveal its essential properties. In this paper, a novel feature extraction approach is proposed for heart sound classification and recognition. The ensemble empirical mode decomposition (EEMD) method is used to decompose the heart sound into a finite number of intrinsic mode functions (IMFs), and the correlation dimensions of the main IMF components (IMF1~IMF4) are calculated as feature set. Then the classical Binary Tree Support Vector Machine (BT-SVM) classifier is employed to classify the heart sounds which include the normal heart sounds (NHSs) and three kinds of abnormal signals namely mitral stenosis (MT), ventricular septal defect (VSD) and aortic stenosis (AS). Finally, the performance of the new feature set is compared with the correlation dimensions of original signals and the main IMF components obtained by the EMD method. The results showed that, for NHSs, the feature set proposed in this paper performed the best with recognition rate of 98.67%. For the abnormal signals, the best recognition rate of 91.67% was obtained. Therefore, the proposed feature set is more superior to two comparative feature sets, which has potential application in the diagnosis of cardiovascular diseases.

scholarly journals A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification

2021 ◽  
Vol 38 (5) ◽  
pp. 1281-1291
Author(s):  
Yusra Obeidat ◽  
Ali Mohammad Alqudah
Keyword(s):  
Feature Extraction ◽  
Short Term Memory ◽  
Heart Diseases ◽  
Normal Sinus Rhythm ◽  
Efficient Manner ◽  
Bundle Branch Block ◽  
Deep Feature ◽  
Feature Extraction And Selection ◽  
Deep Feature Extraction ◽  
Time Required

In this paper we have utilized a hybrid lightweight 1D deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods for accurate, fast, and automated beat-wise ECG classification. The CNN and LSTM models were designed separately to compare with the hybrid CNN-LSTM model in terms of accuracy, number of parameters, and the time required for classification. The hybrid CNN-LSTM system provides an automated deep feature extraction and classification for six ECG beats classes including Normal Sinus Rhythm (NSR), atrial fibrillation (AFIB), atrial flutter (AFL), atrial premature beat (APB), left bundle branch block (LBBB), and right bundle branch block (RBBB). The hybrid model uses the CNN blocks for deep feature extraction and selection from the ECG beat. While the LSTM layer will learn how to extract contextual time information. The results show that the proposed hybrid CNN-LSTM model achieves high accuracy and sensitivity of 98.22% and 98.23% respectively. This model is light and fast in classifying ECG beats and superior to other previously used models which makes it very suitable for embedded systems designs that can be used in clinical applications for monitoring heart diseases in faster and more efficient manner.

Spectral feature extraction based on continuous wavelet transform and image segmentation for peak detection

2020 ◽  
Vol 12 (2) ◽  
pp. 169-178 ◽  
Author(s):  
Guofeng Yang ◽  
Jiacai Dai ◽  
Xiangjun Liu ◽  
Meng Chen ◽  
Xiaolong Wu
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Spectral Feature ◽  
Peak Detection ◽  
Continuous Wavelet ◽  
Crucial Step ◽  
Spectral Signal

Peak detection is a crucial step in spectral signal pre-processing.

scholarly journals Classification of Multi Heart Diseases With Android Based Monitoring System

2020 ◽  
pp. 14-22
Keyword(s):  
Feature Extraction ◽  
Heart Diseases ◽  
Confusion Matrix ◽  
Predictive Coding ◽  
Classification Performance ◽  
Support Vector ◽  
Ecg Signal ◽  
Linear Predictive Coding ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) examination via computer techniques that involve feature extraction, pre-processing and post-processing was implemented due to its significant advantages. Extracting ECG signal standard features that requires high processing operation level was the main focusing point for many studies. In this paper, up to 6 different ECG signal classes are accurately predicted in the absence of ECG feature extraction. The corner stone of the proposed technique in this paper is the Linear predictive coding (LPC) technique that regress and normalize the signal during the pre-processing phase. Prior to the feature extraction using Wavelet energy (WE), a direct Wavelet transform (DWT) is implemented that converted ECG signal to frequency domain. In addition, the dataset was divided into two parts , one for training and the other for testing purposes Which have been classified in this proposed algorithm using support vector machine (SVM). Moreover, using MIT AI2 Companion was developed by MIT Center for Mobile Learning, the classification result was shared to the patient mobile phone that can call the ambulance and send the location in case of serious emergency. Finally, the confusion matrix values are used to measure the proposed classification performance. For 6 different ECG classes, an accuracy ration of about 98.15% was recorded. This ratio became 100% for 3 ECG signal classes and decreases to 97.95% by increasing ECG signal to 7 classes.

Automatic Detection of Diabetic Retinopathy by Using Evolutionary Computation Algorithm Based on Feature Extraction

2014 ◽  
Vol 573 ◽  
pp. 819-824 ◽  
Author(s):  
K. Latha ◽  
S. Gowri Durga
Keyword(s):  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Heart Diseases ◽  
Optimization Techniques ◽  
Current Status ◽  
Uncontrolled Diabetes ◽  
Eye Damage ◽  
Computation Algorithm ◽  
After Cancer ◽  
Nerve Disease

According to current status Diabetes is the third leading cause of death after cancer and heart diseases. The serious complications of uncontrolled diabetes include kidney damage, eye damage, nerve disease and stroke. Diabetic retinopathy (DR) is a common retinal problem associated with diabetes. This paper focuses on Diabetic Retinopathy and finds the exudates parts in the eye by implementing combinations of global optimization techniques such as Particle Swarm Optimization (PSO) based on feature extraction. It can also be helpful in improving the performance by accuracy, sensitivity and specificity for detecting the Diabetic Retinopathy when compare to other traditional methods.

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