Methods for assessment of autonomic nervous system activity from cardiorespiratory signals

Thesis event information

Date and time of the thesis defence

Topic of the dissertation

Methods for assessment of autonomic nervous system activity from cardiorespiratory signals

Doctoral candidate

M.D., M.Sc. Suvi Tiinanen

Faculty and unit

University of Oulu Graduate School, Faculty of Medicine, Center for Machine Vision and Signal Analysis

Subject of study



Professor Jari Hyttinen, Tampere University of Technology


Professor Tapio Seppänen, University of Oulu

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New methods for assessment of autonomic nervous system activity from cardiorespiratory signals

A cardiorespiratory system is highly regulated via the autonomic nervous system (ANS), whose function can be quantified noninvasively by analyzing electrocardiogram (ECG), blood pressure (BP) and respiration signals. Several conditions and illnesses are linked with imbalance of the ANS.

This thesis aimed to develop methods for describing the ANS regulation of a cardiovascular system from short-term cardiorespiratory measurements. More specifically, the role of breathing rate and its effects on traditional frequency domain based cardiovascular indexes describing ANS control is addressed.

The main contributions are as follows: 1) an adaptive filtering based method to remove respiratory influences from cardiovascular signals and indexes was developed. The adaptive filter reduced the bias caused by low respiration rate, enabling the usage of spontaneous respiration measurement protocol over controlled respiration. 2) Methods to quantify respiratory sinus arrhythmia (RSA) index from cardiovascular signals were developed as well: two methods utilizes adaptive filtering and either the measured respiration signal or the ECG-derived respiration signal and one method uses independent component analysis. Developed RSA index methods allow varying respiration rates making them physiologically more accurate than traditional high frequency power with fixed respiration rate, often used as RSA index. 3) Tools for studying the power and the frequency of low frequency (LF) oscillations of cardiovascular signals were developed, including a time-frequency representation for analyzing varying data. An experimental study was conducted with patients of continuum of cardiovascular risks. According to results, aging decreased the frequency of LF oscillation, whereas coronary artery disease decreased it further. 4) Two new ECG-derived respiration (EDR) methods utilizing decomposition techniques were developed. The proposed methods yielded statistically significant improvements over previously developed EDR methods. EDR method enables to get respiratory information from ECG, which in its turn reduces needed modalities in ANS quantification.

This thesis provides methods to quantify indexes describing the ANS function more accurately by acknowledging the respiration effects. The results of this thesis may be utilized in various application areas, ranging from clinical to physiology research up to commercial health, wellness and sport products.
Last updated: 20.8.2019