Intelligent classification of electrocardiogram (ECG) signal using extended Kalman Filter (EKF) based neuro fuzzy system

2006 ◽  
Vol 82 (2) ◽  
pp. 157-168 ◽  
Author(s):  
Yeong Pong Meau ◽  
Fatimah Ibrahim ◽  
Selvanathan A.L. Narainasamy ◽  
Razali Omar
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Fuzzy System ◽  
Ecg Signal ◽  
Neuro Fuzzy ◽  
Electrocardiogram Ecg

scholarly journals ECG De-noising using Hybrid Linearization Method

2015 ◽  
Vol 15 (3) ◽  
pp. 504
Author(s):  
P Sri Lakshmi ◽  
V Lokesh Raju
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Linearization Method ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Original Signal ◽  
Non Invasive ◽  
And Performance ◽  
Noise Contamination ◽  
Electrocardiogram Ecg

<p>Electrocardiogram (ECG) is a non-invasive tool that monitors the electrical activity of the heart. An ECG signal is highly prone to the disturbances such as noise contamination, artifacts and other signals interference. So, an ECG signal has to be de-noised so that the distortions can be eliminated from the original signal for the perfect diagnosing of the condition and performance of the heart. Extended Kalman Filter (EKF) de-noises an ECG signal to some extent. This project proposes a method called Hybrid Linearization Method which is a combination of Extended Kalman Filter along with Discrete Wavelet Transform (DWT) resulting in an improved de-noised signal.</p>

scholarly journals Classification Of Wheat Grains Using Image Processing Techniques Based Neuro-Fuzzy System Model

2018 ◽  
pp. 57-61
Author(s):  
Ahmet Kayabasi ◽  
Kadir Sabanci ◽  
Abdurrahim Toktas
Keyword(s):  
Image Processing ◽  
Fuzzy System ◽  
Absolute Error ◽  
Data Set ◽  
Wheat Grains ◽  
Image Processing Techniques ◽  
Neuro Fuzzy ◽  
Length Width ◽  
Processing Techniques

In this study, an image processing techniques (IPTs) and a Sugeno-typed neuro-fuzzy system (NFS) model is presented for classifying the wheat grains into bread and durum. Images of 200 wheat grains are taken by a high resolution camera in order to generate the data set for training and testing processes of the NFS model. The features of 5 dimensions which are length, width, area, perimeter and fullness are acquired through using IPT. Then NFS model input with the dimension parameters are trained through 180 wheat grain data and their accuracies are tested via 20 data. The proposed NFS model numerically calculate the outputs with mean absolute error (MAE) of 0.0312 and classify the grains with accuracy of 100% for the testing process. These results show that the IPT based NFS model can be successfully applied to classification of wheat grains.

A hybrid neuro-fuzzy system for ECG classification of myocardial infarction

2002 ◽  
Author(s):  
P. Bozzola ◽  
G. Bortolan ◽  
C. Combi ◽  
F. Pinciroli ◽  
C. BroHet
Keyword(s):  
Myocardial Infarction ◽  
Fuzzy System ◽  
Neuro Fuzzy

scholarly journals Electrocardiogram (ECG) based stress recognition integrated with different classification of age and gender

2019 ◽  
Vol 15 (1) ◽  
pp. 199
Author(s):  
N. S. Nor Shahrudin ◽  
K. A. Sidek ◽  
A. Z. Jusoh
Keyword(s):  
Data Acquisition ◽  
Stress Level ◽  
Ecg Signal ◽  
Alternative Mechanism ◽  
Age And Gender ◽  
Stress States ◽  
And Gender ◽  
Electrocardiogram Ecg ◽  
Ecg Data

<p class="Abstract"><em><span>Good mental health is important in our daily life. A person commonly finds stress as a barrier to enhance an individual’s performance. Be reminded that not all people have the same level of stress because different people have dissimilar problems in their life. In addition, different level of age and gender will affect unequal amount of stress. Electrocardiogram (ECG) signal is an electrical indicator of the heart that can detect changes of human response which relates to our emotions and reactions. Thus, this research proposed a non-intrusive detector to identify stress level for both gender and different classification of age using the ECG. A total of 30 healthy subjects were involved during the data acquisition stage. Data acquisition which initialize ECG data were divided into two conditions; which are normal and stress states. ECG data for normal state only need the participant to breath in and out normally. In other hand, the participants also need to undergo Stroop Colour word test as a stress inducer to represent ECG in stress state. Then, Sgolay filter was selected in the pre-processing stage to remove artifacts in the signal. The process was followed by feature extraction of the ECG signal and finally classified using RR Interval (RRI), different amplitudes of R peaks and Cardioid graph were used to evaluate the performance of the proposed technique. As a result, Class 5 (age range between 50-59 years old) marks the highest changes of stress level rather than other classes, while women are more affected by stress rather than men by showing tremendous percentage changes between normal and stress level over the proposed classifiers. The result proves that ECG signals can be used as an alternative mechanism to recognize stress more efficiently with the integration of gender and age variabilities.</span></em></p>

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.

scholarly journals A SOLUTION FOR IMPROVEMENT OF ECG ARRHYTHMIA RECOGNITION USING RESPIRATION INFORMATION

2018 ◽  
Vol 56 (3) ◽  
pp. 335
Author(s):  
Tran Hoai Linh
Keyword(s):  
Signal Classification ◽  
Ecg Signal ◽  
Wavelet Filter ◽  
Ecg Signals ◽  
Additional Information ◽  
Neuro Fuzzy ◽  
Ecg Arrhythmia ◽  
Nonlinear Classifier ◽  
Electrocardiogram Ecg ◽  
Source Of Information

Electrocardiogram (ECG) and respiration signals are two basic and important and valuable biomedical signals as source of information used to determine a person's health status. However, ECG signals are usually of small amplitude and are susceptible to various noises such as: the 50Hz grid noise, poor electrodes’ contacts with the patient's skin, the patient's emotional variations, the respiration and movement of the patient... The idea in this paper by  filtering out the effect of the respiration in the ECG signal or by incorporating the information of breathing stage into the ECG signal classification the we can improve the reliability and accuracy of the arrythmia classification. This paper proposes a solution, which uses wavelet filter to reduce the effect of respiration in the ECG signals and will use additional information from the breathing rhythm (when available) to help better classifying the arrythmias. As the main nonlinear classifier we use the classical neuro-fuzzy TSK network. The proposed solution will be tested with data from the MIT-BIH and the MGH/MF databases.

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