Hidden Markov Models and Boosting for Robust Time Series Prediction

Abstract

This study explores the use of Hidden Markov Models (HMM) combined with a hybrid ensemble method using boosting techniques for the classification of different types of electrocardiogram (ECG) signals, including Normal, Ventricular Tachycardia (VTach), Ventricular Fibrillation (VFib), and Bradycardia. The analysis focuses on leveraging Auto Correlation and Partial Autocorrelation (PACF) for feature extraction and enhancing the classification performance through a hybrid approach integrating HMMs and boosting. First, Wavelet-based filtering was applied to remove noise from the ECG signals, providing cleaner data for subsequent feature extraction. Both Autocorrelation (AC) and Partial Autocorrelation (PACF) were computed for the filtered signals. While AC provided general periodicity information, PACF offered a more precise analysis by isolating direct correlations at each lag, which was especially useful for differentiating irregular rhythms like VTach and VFib. We then implemented a hybrid ensemble method combining Hidden Markov Models (HMMs) with boosting techniques, such as AdaBoost and Gradient Boosting, to improve classification accuracy. The HMMs were used to model the sequential dependencies in the ECG signals, while the boosting algorithms were applied to optimize the performance of the ensemble by weighting and improving weaker classifiers iteratively. The study demonstrates that the combination of HMMs, boosting, and PACF provides a powerful and efficient method for automatic arrhythmia detection, achieving high precision and robustness across different ECG signal types.