Syntactic Pattern Recognition of the ECG
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A Multi-HMM Approach to ECG Segmentation
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Feature compensation in the cepstral domain employing model combination
Speech Communication
Video-based signer-independent Arabic sign language recognition using hidden Markov models
Applied Soft Computing
Hybrid robust support vector machines for regression with outliers
Applied Soft Computing
GMM based SPECT image classification for the diagnosis of Alzheimer's disease
Applied Soft Computing
ECG analysis using nonlinear PCA neural networks for ischemiadetection
IEEE Transactions on Signal Processing
IEEE Transactions on Information Technology in Biomedicine
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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This study presented a new diagnosis system for myocardial infarction classification by converting multi-lead ECG data into a density model for increasing accuracy and flexibility of diseases detection. In contrast to the traditional approaches, a hybrid system with HMMs and GMMs was employed for data classification. A hybrid approach using multi-leads, i.e., lead-V1, V2, V3 and V4 for myocardial infarction were developed and HMMs were used not only to find the ECG segmentations but also to calculate the log-likelihood value which was treated as statistical feature data of each heartbeat's ECG complex. The 4-dimension feature vector extracted by HMMs was clustered by GMMs with different numbers of distribution (disease and normal data). SVMs classifier was also examined for comparison with our system in experimental result. There were total 1129 samples of heartbeats from clinical data, including 582 data with myocardial infarction and 547 normal data. The sensitivity of this diagnosis system achieved 85.71%, specificity achieved 79.82% and accuracy achieved 82.50% statistically.