|M.Sc Student||Rosenberg Aviv|
|Subject||Non-Invasive In-Vivo Analysis of Intrinsic Clock-Like|
Pacemaker Mechanisms: Decoupling Neural Input
Using Heart Rate Variability
|Department||Department of Biomedical Engineering||Supervisor||Professor Yael Yaniv|
Heart diseases account for a quarter of all deaths each year in the US and are also an economic burden with an estimated expenditure for treatment of almost $100B annually in the US alone. Cardiovascular disease mortality rate is correlated with an increase in heart rate which is regulated by both the autonomic nervous system (ANS) and the sinoatrial node (SAN) cells in heart. The heart rate is highly variable and never reaches a steady state even at rest---a phenomenon known as heart rate variability (HRV). Many studies have shown that loss of this variability is strongly associated with morbidity and mortality. By using pharmacological denervation, a method of temporarily blocking the ANS and applying HRV analysis we aim to study the contribution of the SAN to the HRV.
We acquired canine ECG data containing both basal (n=27) and denervated segments (n=20). We applied an automated ECG segmentation algorithm to extract the segments from each record. We used a custom R-peak detector, rqrs, based on the PhysioNet's gqrs, to detect R-peaks in the data and produce an RR-interval time series. We excluded ectopic beats using an automated algorithm and proceeded to apply HRV analysis to the resulting intervals. We implemented all major HRV techniques which can be categorized into time domain, frequency domain (spectral) and nonlinear methods (which quantify physiological complexity). We used these methods to extract HRV features from both the basal and denervated data sets.
We implemented all signal processing and HRV analysis methods as an open source MATLAB toolbox, rhrv, and additionally provided a GUI, PhysioZoo, which enables HRV analysis in animal data and comes with an annotated animal database. We have shown that the rqrs peak detector provides accurate detections for annotated human ECG data (F1=93.4) and annotated ECG records from our canine dataset (F1=98.7). Moreover, we adapted HRV analysis techniques to the canine data where necessary and e.g. provide an automatic method of adapting the frequency bands for spectral HRV analysis. HRV analysis of basal vs.
denervated data shows that (1) Time domain HRV is significantly reduced after denervation; (2) SAN contributes spectral power mainly in the very-low frequency band; (3) The SA Node contributes most of the physiological complexity of the heart rate, specifically the long-term changes occurring over many beats; (4) The ANS influences mainly the short term, beat-to-beat variability of the heart rate; (5) The contribution of the ANS to the heart rate signal can be modeled as two sine waves at specific frequencies corresponding to periodic autonomic regulation embedded in white noise. Moreover, we suggest clinical indices for the state and function of the SAN directly from basal ECG data by measuring spectral power in the VLF band and multiscale entropy (MSE) values in the high scales.
We conclude that by applying HRV analysis to regular ECG data, SAN function can be observed even without pharmacological denervation. This has the potential to allow future non-invasive heart monitoring solutions that can be used e.g. for early detection of SA node dysfunction.