Neural networks for heart rate time series analysis
Jyväskylän yliopisto, ammattikorkeakoulu ja elinkeinoelämä ovat viimevuosina investoineet voimakkaasti hyvinvointiteknologian kehittämiseen Jyvässeudun alueella. Tavoitteena on ollut kehittää alan yritystoimintaa ja verkostoitumista, sekä koulutusta ja tutkimusta. Tuoreena esimerkkinä on Viveca-talon valmistuminen 2003, jossa hyvinvointiteknologian yritys ja tutkimustoimintaa on integroitu yhteisiin toimitiloihin. Sami Saalastin väitöskirjatyön tutkimustulokset ovat syntyneet pääasiassa kahdessa hyvinvointiteknologiaa edistävässä Tekes -projektissa Kilpa- ja Huippu-urheilun Tutkimuskeskuksessa, sekä Firstbeat Technologies Oy:ssä vuosina 2000-2003. Tutkimusta on ollut rahoittamassa myös Comas -tutkijakoulu. 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.
...
Publisher
Jyväskylän yliopistoISBN
951-39-1707-XISSN Search the Publication Forum
1456-5390Metadata
Show full item recordCollections
- Väitöskirjat [3589]
License
Related items
Showing items with similar title or keywords.
-
Estimating intra- and inter-subject oxygen consumption in outdoor human gait using multiple neural network approaches
Müller, Philipp; Pham-Dinh, Khoa; Trinh, Huy; Rauhameri, Anton; Cronin, Neil J. (Public Library of Science, 2024)Oxygen consumption (VO2) is an important measure for exercise test, such as walking and running, that can be measured outdoors using portable spirometers or metabolic analyzers. However, these devices are not feasible for ... -
The potential of convolutional neural network in the evaluation of tumor-stroma ratio from colorectal cancer histopathological images
Petäinen, Liisa (2022)Tässä Pro gradu-työssä tutkitaan konvoluutioneuroverkkojen käyttömahdollisuuksia histopatologisista kuvista tehtävässä kasvain-strooma suhdeluvun arvioinnissa. Tarkoituksena on selvittää, mikä on siirto-opettamisen vaikutus, ... -
Using deep neural networks for kinematic analysis : challenges and opportunities
Cronin, Neil J. (Elsevier BV, 2021)Kinematic analysis is often performed in a lab using optical cameras combined with reflective markers. With the advent of artificial intelligence techniques such as deep neural networks, it is now possible to perform such ... -
Process‐Informed Neural Networks : A Hybrid Modelling Approach to Improve Predictive Performance and Inference of Neural Networks in Ecology and Beyond
Wesselkamp, Marieke; Moser, Niklas; Kalweit, Maria; Boedecker, Joschka; Dormann, Carsten F. (Wiley, 2024)Despite deep learning being state of the art for data-driven model predictions, its application in ecology is currently subject to two important constraints: (i) deep-learning methods are powerful in data-rich regimes, but ... -
Surrogate Modelling for Oxygen Uptake Prediction Using LSTM Neural Network
Davidson, Pavel; Trinh, Huy; Vekki, Sakari; Müller, Philipp (MDPI AG, 2023)Oxygen uptake (V̇O2) is an important metric in any exercise test including walking and running. It can be measured using portable spirometers or metabolic analyzers. Those devices are, however, not suitable for constant ...