Removing the effect of respiration on the heart rate variability and quantifying the medical impact of the new uncoupled parameters when estimating risk of cardiac death

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    The respiratory sinus arrhythmia (RSA) is a physiological phenomenon in wich the respiration cycle modulates the heart rate. In this event the heart beats faster during inspirations and slows down when expiring. On the other hand, the heart variability (HRT) is an accurate way of measuring the great amount of physiological and pathological processes that also modulate the normal rhythm of the heart. It has been shown that patients presenting pathological states including diabetes, renal failure, myocardial infarction and cardiac arrhythmias among others tend to have a modified HRV. However, this measurement could be corrupted by a strong RSA and an accurate HRV parameter estimation might not be possible.

    Therefore, it can be postulated that removing the effect of respiration on the HRV could deliver further insights on the pure physiological state of the heart. The uncoupled HRV parameters might have a stronger prediction when estimating risk of cardiac death.

    In this project, an algorithm to remove the effect of respiration on HRV should be developed. For this purpose, the existing algorithm based on notch filtering should be extended to adapt in time and include more spectral information.