ECG Diagnostics by Fuzzy Decision Making

1998 ◽  
Vol 06 (02) ◽  
pp. 201-210 ◽  
Author(s):  
Adolf Grauel ◽  
Lars A. Ludwig ◽  
Georg Klene
Keyword(s):  
Fuzzy Logic ◽  
Medical Knowledge ◽  
Practical Experience ◽  
Fuzzy Logic System ◽  
Fuzzy Decision Making ◽  
Medical Expert ◽  
Rule Based ◽  
Rule Based System ◽  
Cardiac Diagnosis ◽  
Logic System

The analysis of electrocardiograms (ECGs) helps physicians make their cardiac diagnosis. Therefore a large store of medical knowledge and practical experience is required. In this paper we report on our investigations of a rule-based fuzzy logic system that processes ECG data using the knowledge of a medical expert. The aim is to give support to the physician for his diagnosis. In this first consideration we discuss single modules of the rule-based system proposed and moreover we present the used input and output variables of the rulebases. The performance of the implemented rule-based fuzzy logic system is tested using ECGs with abnormalities in the P and T wave as well as in the QRS complex. The system's output corresponds to the analysis of these ECGs by a medical expert.

An Efficient System for Heart Disease Prediction Using Hybrid OFBAT with Rule-Based Fuzzy Logic Model

2016 ◽  
Vol 26 (04) ◽  
pp. 1750061 ◽  
Author(s):  
G. Thippa Reddy ◽  
Neelu Khare
Keyword(s):  
Fuzzy Logic ◽  
Heart Disease ◽  
Fuzzy System ◽  
Disease Diagnosis ◽  
Fuzzy Logic System ◽  
Classification Model ◽  
Rule Based ◽  
Diagnosis System ◽  
Heart Disease Diagnosis ◽  
Logic System

The objective of the work is to predict heart disease using computing techniques like an oppositional firefly with BAT and rule-based fuzzy logic (RBFL). The system would help the doctors to automate heart disease diagnosis and to enhance the medical care. In this paper, a hybrid OFBAT-RBFL heart disease diagnosis system is designed. Here, at first, the relevant features are selected from the dataset using locality preserving projection (LPP) algorithm which helps the diagnosis system to develop a classification model using the fuzzy logic system. After that, the rules for the fuzzy system are created from the sample data. Among the entire rules, the important and relevant group of rules are selected using OFBAT algorithm. Here, the opposition based learning (OBL) is hybrid to the firefly with BAT algorithm to improve the performance of the FAT algorithm while optimizing the rules of the fuzzy logic system. Next, the fuzzy system is designed with the help of designed fuzzy rules and membership functions so that classification can be carried out within the fuzzy system designed. At last, the experimentation is performed by means of publicly available UCI datasets, i.e., Cleveland, Hungarian and Switzerland datasets. The experimentation result proves that the RBFL prediction algorithm outperformed the existing approach by attaining the accuracy of 78%.

A fuzzy logic system for expert systems

1988 ◽  
Vol 2 (3) ◽  
pp. 183-193 ◽  
Author(s):  
Te-Chuan Chang ◽  
C. William Ibbs ◽  
Keith C. Crandall
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Knowledge Base ◽  
Fuzzy Logic System ◽  
Future Research ◽  
Rule Based ◽  
Reasoning System ◽  
Fuzzy Production Rules ◽  
Future Research Directions ◽  
Logic System

Using the theory of fuzzy sets, this paper develops a fuzzy logic reasoning system as an augmentation to a rule-based expert system to deal with fuzzy information. First, fuzzy set theorems and fuzzy logic principles are briefly reviewed and organized to form a basis for the proposed fuzzy logic system. These theorems and principles are then extended for reasoning based on knowledge base with fuzzy production rules. When an expert system is augmented with the fuzzy logic system, the inference capability of the expert system is greatly expanded; and the establishment of a rule-based knowledge base becomes much easier and more economical. Interpretations of the system’s power and possible future research directions conclude the paper.

APPLICATION OF RULE-BASED FUZZY LOGIC FOR MAIN SUBURBAN ROAD EXTRACTION IN IKONOS IMAGERY

2006 ◽  
Vol 03 (03) ◽  
pp. 171-180
Author(s):  
LILI YUN ◽  
KEIICHI UCHIMURA ◽  
ZHENCHENG HU
Keyword(s):  
Fuzzy Logic ◽  
Satellite Imagery ◽  
Fuzzy Reasoning ◽  
Fuzzy Logic System ◽  
Road Extraction ◽  
Rule Based ◽  
The Road ◽  
Straight Line ◽  
Road Segments ◽  
Logic System

In aerial and satellite imagery, geometric and radiometric properties are two important properties for feature extraction and recognition. This paper presents a semi-automated approach, based on a fuzzy logic system, to extract the main suburban roads in IKONOS images, and shows how to implement structure extraction algorithms based on fuzzy reasoning approaches. First, the method detects segments that are similar to the road in their radiometric properties. Then, it recognizes potential geometric shapes of the road using the straight-line Hough transform. Only the road segments are extracted by means of fuzzy logic concepts, with subsequent image processing and analysis being able to exploit the corresponding fuzzy reasoning to yield improved results. The proposed approach is validated by analysis of high-resolution Ortho-satellite imagery.

Rule-Based Fuzzy Logic System for Diagnosing Migraine

2008 ◽  
pp. 383-388 ◽  
Author(s):  
Svetlana Simić ◽  
Dragan Simić ◽  
Petar Slankamenac ◽  
Milana Simić-Ivkov
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic System ◽  
Rule Based ◽  
Logic System

A New Hardware Architecture for Fuzzy Logic System Acceleration

2016 ◽  
Vol 12 (2) ◽  
pp. 188-197
Author(s):  
A yahoo.com ◽  
Aumalhuda Gani Abood aumalhuda ◽  
A comp ◽  
Dr. Mohammed A. Jodha ◽  
Dr. Majid A. Alwan
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic System ◽  
Hardware Architecture ◽  
Logic System

Penerapan Logika Samar dalam Peramalan Data Runtun Waktu

2011 ◽  
Vol 3 (2) ◽  
pp. 11-15
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Analysis ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Time Series Data ◽  
Fuzzy Logic System ◽  
Series Data ◽  
Series Analysis ◽  
Logic System

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis

IMPROVING EIGENSPACE-BASED FUZZY LOGIC SYSTEM USING A LINEAR INTERPOLATION SCHEME FOR SPEECH PATTERN RECOGNITION

2013 ◽  
Vol 37 (3) ◽  
pp. 611-620
Author(s):  
Ing-Jr Ding ◽  
Chih-Ta Yen
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Recognition Performance ◽  
Linear Interpolation ◽  
Fuzzy Logic System ◽  
Interpolation Scheme ◽  
Speech Pattern ◽  
Eigen Value ◽  
Target Speaker ◽  
Logic System

The Eigen-FLS approach using an eigenspace-based scheme for fast fuzzy logic system (FLS) establishments has been attempted successfully in speech pattern recognition. However, speech pattern recognition by Eigen-FLS will still encounter a dissatisfactory recognition performance when the collected data for eigen value calculations of the FLS eigenspace is scarce. To tackle this issue, this paper proposes two improved-versioned Eigen-FLS methods, incremental MLED Eigen-FLS and EigenMLLR-like Eigen-FLS, both of which use a linear interpolation scheme for properly adjusting the target speaker’s Eigen-FLS model derived from an FLS eigenspace. Developed incremental MLED Eigen-FLS and EigenMLLR-like Eigen-FLS are superior to conventional Eigen-FLS especially in the situation of insufficient data from the target speaker.

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