Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Computers in Biology and Medicine
Artificial Intelligence in Medicine
Design of 100 μW Wireless Sensor Nodes for Biomedical Monitoring
Journal of Signal Processing Systems
IEEE Transactions on Information Technology in Biomedicine
Heartbeat time series classification with support vector machines
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Automatic extraction of HRV sequences from noisy ECG data for reliable analysis and telediagnosis
Telehealth '07 The Third IASTED International Conference on Telehealth
Rapid and brief communication: Evolutionary extreme learning machine
Pattern Recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A Computer Aided Diagnosis System for Thyroid Disease Using Extreme Learning Machine
Journal of Medical Systems
Computer Methods and Programs in Biomedicine
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The ability to predict patient outcomes is important for clinical triage, which is the process of assessing severity and assigning appropriate priority of treatment for large numbers of patients. In this study, we present an automatic prognosis system for patient outcome prediction with heart rate variability (HRV) and traditional vital signs. Support vector machine (SVM) and extreme learning machine (ELM) are employed as predictors, and SVM with linear kernel is reported to perform the best in general. In the experiments, the combination of HRV measures and vital signs is found to be more closely associated with patient outcome than either HRV or vital signs. Moreover, two new segment based methods are proposed to improve the predictive accuracy, where several sets of HRV measures are calculated from non-overlapped segments for each patient and final decision is made through the majority voting rule. The results reveal that the segment based methods are able to enhance the prediction performance significantly.