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A knowledge-based approach to ECG interpretation using fuzzy logic
M Kundu1, M Nasipuri, D K Basu
1Dept. of Comput. Sci. & Eng., Jadavpur Univ., Calcutta.
Summary
This study introduces a fuzzy logic-based expert system for diagnosing heart rhythm disorders using electrocardiogram (ECG) patterns. The system effectively classifies abnormalities by employing generalized modus ponens (GMP) for flexible pattern interpretation.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomedical Engineering
Background:
- Accurate diagnosis of heart rhythm disorders is crucial for patient outcomes.
- Electrocardiogram (ECG) interpretation can be complex due to variations in bioelectric signals.
- Existing rule-based systems may struggle with subtle variations in ECG patterns.
Purpose of the Study:
- To develop a rule-based expert system for classifying heart rhythm abnormalities.
- To leverage fuzzy logic and generalized modus ponens (GMP) for enhanced ECG pattern interpretation.
- To improve the sensitivity of expert systems to fine variations in ECG signals without increasing rulebase size.
Main Methods:
- Implementation of a rule-based expert system utilizing fuzzy logic.
- Application of generalized modus ponens (GMP) as the primary rule of inference.
- Definition of possibility distributions for cardiological domain variables.
Main Results:
- The system successfully classifies abnormalities in human heart rhythm disorders via ECG interpretation.
- GMP allows for the diagnosis of a wide range of ECG pattern variations, even those deviating from rule preconditions.
- Fuzzy logic enhances system sensitivity to subtle ECG variations common in bioelectric signals.
Conclusions:
- The developed fuzzy logic expert system provides a robust method for diagnosing heart rhythm disorders.
- Generalized modus ponens (GMP) offers flexibility in interpreting diverse ECG patterns.
- Fuzzy logic is effective in creating sensitive diagnostic systems for bioelectric signal analysis.
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Parts of an ECG
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