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A rough-set-based inference engine for ECG classification
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DOI：10.1109/TIM.2006.884279.png)
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摘要
En 中文
In this paper, a rule-based rough-set decision system for the development of a disease inference engine is described. For this purpose, an offline-data-acquisition system of paper electrocardiogram (ECG) records is developed using image-processing techniques. The ECG signals may be corrupted with six types of noise. Therefore, at first, the extracted signals are fed for noise removal. A QRS detector is also developed for the detection of R-R interval of ECG waves. After the detection of this R-R interval, the P and T waves are detected based on a syntactic approach. The isoelectric-level detection and base-line correction are also implemented for accurate computation of different attributes of P, QRS, and T waves. A knowledge base is developed from different medical books and feedbacks of reputed cardiologists regarding ECG interpretation and essential time-domain features of the ECG signal. Finally, a rule-based rough-set decision system is generated for the development of an inference engine for disease identification from these time-domain features.
Keyword:
decision system
electrocardiogram (ECG)
feature extraction
inference engine
knowledge base
rough set
rule based
time domain
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期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
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