Name
Title | ||
|---|---|---|
A Universal Method To Control Over-Time Alterations Of Respiratory Sinus Arrhythmia In Short And Long Signals |
Abstract | ||
|---|---|---|
One of the ways allowing to control the performance of the Autonomic Nervous System (ANS) is monitoring the cardiorespiratory coupling. It shows how the two most important systems in the human body act to function efficiently. The effect that is believed to show these dependencies is Respiratory Sinus Arrhythmia (RSA), which causes accelerations of heart rate during inspiration and decelerates the rhythm during passive act of expiration. There are many methods to analyse the occurrence of RSA, from the basic peak-to-valley method, to more complicated ones as e.g. spectral coherence. But all of them have flaws like omitting samples or giving only a single value for the entire analysed period.In this paper, we present a simple, numeric method, which enables the analysis of the changes in the strength of RSA over the time. Our solution is called Cardiorespiratory Coupling Assessment (CCA) and calculates the value of RSA in consecutive, one-minute long windows as the median of the differences of RR intervals detected during expiration and inspiration in each window.The data necessary for the CCA method to work properly includes ECG and the respiratory signal from the chest belt measured simultaneously. In our research, we first analysed the performance of the method on synthetically generated data to verify its performance. Than we compared short 5 minute recordings obtained during spontaneous and controlled breathing and then analysed 4 hours long signals extracted from nighttime recordings.The results show that the CCA method allows to measure accurately the changes in strength of RSA over the time in both short and long signals. Moreover, the results indicate that the level of the RSA strength is larger in recordings from spontaneous breathing phase than from the controlled breathing. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.22489/CinC.2017.154-409 | 2017 COMPUTING IN CARDIOLOGY (CINC) |
Field | DocType | Volume |
Autonomic nervous system,Vagal tone,Internal medicine,Coherence (signal processing),Cardiology,Expiration,Breathing,Heart rate,Rhythm,Medicine,Cardiorespiratory fitness | Conference | 44 |
ISSN | Citations | PageRank |
2325-8861 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| iga grzegorczyk | 1 | 1 | 1.75 |
| jan gieraltowski | 2 | 5 | 2.88 |
| Pawel Krzesinski | 3 | 0 | 0.68 |
| j j zebrowski | 4 | 0 | 1.69 |