The nature of statistical learning theory
The nature of statistical learning theory
Artificial Neural Networks: A Tutorial
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Introduction to the special section on computationalintelligence in medical systems
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Comparison studies of LS_SVM and SVM on modeling for fermentation processes
ICNC'09 Proceedings of the 5th international conference on Natural computation
GA-SVM based framework for time series forecasting
ICNC'09 Proceedings of the 5th international conference on Natural computation
ICNC'09 Proceedings of the 5th international conference on Natural computation
Multi-class classification for Wuhan area's TM image based on support vector machine
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Fuzzy SVM controller for robotic manipulator based on GA and LS algorithm
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 6
Relevance analysis of stochastic biosignals for identification of pathologies
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
Patient Outcome Prediction with Heart Rate Variability and Vital Signs
Journal of Signal Processing Systems
Expert Systems with Applications: An International Journal
Detection of obstructive sleep apnoea using dynamic filter-banked features
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
An efficient multiple-kernel learning for pattern classification
Expert Systems with Applications: An International Journal
A vector-valued support vector machine model for multiclass problem
Information Sciences: an International Journal
A MapReduce-based distributed SVM ensemble for scalable image classification and annotation
Computers & Mathematics with Applications
Building a Cepstrum-HMM kernel for Apnea identification
Neurocomputing
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Obstructive sleep apnea syndrome (OSAS) is associated with cardiovascular morbidity as well as excessive daytime sleepiness and poor quality of life. In this study, we apply a machine learning technique [support vector machines (SVMs)] for automated recognition of OSAS types from their nocturnal ECG recordings. A total of 125 sets of nocturnal ECG recordings acquired from normal subjects (OSAS-) and subjects with OSAS (OSAS+), each of approximately 8 h in duration, were analyzed. Features extracted from successive wavelet coefficient levels after wavelet decomposition of signals due to heart rate variability (HRV) from RR intervals and ECG-derived respiration (EDR) from R waves of QRS amplitudes were used as inputs to the SVMs to recognize OSAS+/- subjects. Using leave-one-out technique, the maximum accuracy of classification for 83 training sets was found to be 100% for SVMs using a subset of selected combination of HRV and EDR features. Independent test results on 42 subjects showed that it correctly recognized 24 out of 26 OSAS+ subjects and 15 out of 16 OSAS- subjects (accuracy = 92.85%; Cohen's κ value of 0.85). For estimating the relative severity of OSAS, the posterior probabilities of SVM outputs were calculated and compared with respective apnea/hypopnea index. These results suggest superior performance of SVMs in OSAS recognition supported by wavelet-based features of ECG. The results demonstrate considerable potential in applying SVMs in an ECG-based screening device that can aid a sleep specialist in the initial assessment of patients with suspected OSAS.