Toward Intelligent Meeting Agents
Computer
Agent-Based Control for Networked Traffic Management Systems
IEEE Intelligent Systems
MASACAD: A Multiagent-Based Approach to Information Customization
IEEE Intelligent Systems
Automatic generation of spoken dialogue from medical plans and ontologies
Journal of Biomedical Informatics - Special issue: Dialog systems for health communications
Automated ontology construction for unstructured text documents
Data & Knowledge Engineering
Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
A fuzzy clustering neural network architecture for classification of ECG arrhythmias
Computers in Biology and Medicine
A multilayer perceptron-based medical decision support system for heart disease diagnosis
Expert Systems with Applications: An International Journal
An integrated and intelligent DSS for manufacturing systems
Expert Systems with Applications: An International Journal
A genetic fuzzy agent using ontology model for meeting scheduling system
Information Sciences: an International Journal
Pattern Recognition
A fuzzy ontology and its application to news summarization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An arrhythmia classification system based on the RR-interval signal
Artificial Intelligence in Medicine
Intelligent ontological multi-agent for healthy diet planning
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
A type-2 fuzzy ontology and its application to personal diabetic-diet recommendation
IEEE Transactions on Fuzzy Systems
Hi-index | 12.05 |
The electrocardiogram (ECG) signal is adopted extensively as a low-cost diagnostic procedure to provide information concerning the healthy status of the heart. However, the QRS complex must be calculated accurately before proceeding with the heart rate variability (HRV). In particular, the R peak needs to be detected reliably. This study presents an adaptive fuzzy detector to detect the R peak correctly. Additionally, an ontological fuzzy agent is presented to process the collection of ECG signals. The required knowledge is stored in the ontology, which comprises some personal ontologies and predefined by domain experts. The ontological fuzzy agent retrieves the ECG signals with R peaks marked for HRV analysis and ECG further applications. It contains a personal fuzzy filter, an HRV analysis mechanism, and a fuzzy normed inference engine. Moreover, the ECG fuzzy signal space and some important properties are presented to define the working environment of the agent. An experimental platform has been constructed to test the performance of the agent. The results indicate that the proposed method can work effectively.