Jyväskylä Studies in Computing 33 Neural Networks for Heart Rate Time Series Analysis Neural Networks for Heart Rate Time Series Analysis Jyväskylä Studies in Computing 33 Neural Networks for Heart Rate Time Series Analysis University of Jyväskylä

Saalasti, Sami Neural Networks for Heart Rate Time Series Analysis Jyväskylä: University of Jyväskylä, 2003, 192 p. (Jyväskylä Studies in Computing ISSN 1456-5390; 33) ISBN 951-39-1637-5 Finnish summary Diss. The dissertation introduces method and algorithm development for nonstationary, nonlinear and dynamic signals. Furthermore, the dissertation concentrates on applying neural networks for time series analysis. The presented methods are especially applicable for heart rate time series analysis. Some classical methods for time series analysis are introduced, including improvements and new aspects for existing data preprocessing and modeling procedures, e.g., time series segmentation, digital filtering, data-ranking, detrending, time-frequency and time-scale distributions. A new approach for the creation of hybrid models with a discrete decision plane and limited value range is illustrated. A time domain peak detection algorithm for signal decomposition, i.e., estimation of a signal’s instantaneous power and frequency, is presented. A concept for constructing reliability measures, and the utilization of reliability to improve model and signal quality with postprocessing are grounded. Also a new method for estimating the reliability of instantaneous frequency for time-frequency distributions is presented. Furthermore, error tolerant methods are introduced to improve the signal-to-noise ratio in the time series. Some new principles are grounded for the neural network theory. Optimization of a time-frequency plane with a neural network as an adaptive filter is introduced. The novelty of the method is the use of a neural network as an inner function inside an instantaneous frequency estimation function. This is an example of a new architecture called a transistor network that is introduced together with the general solution for its unknown parameters. Applicability of the dynamic neural networks and model selection using physiological constraints is demonstrated with a model estimating excess post-exercise oxygen consumption based on heart rate time series. Yet another application demonstrates the correlation between the training and testing error and usage of the neural network as a memory to repeat the different RR interval patterns.