BME 2023

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10:00
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Accurate pulse transit time estimation in low sampling frequency environments using parametric normalization
Roel Montree, Elisabetta Peri, Reinder Haakma, Rik Vullings

Session: Poster session 2 (Odd numbers)
Session starts: Friday 27 January, 10:00
Presentation starts: 10:00

Roel Montree (University of Technology Eindhoven)
Elisabetta Peri (University of Technology Eindhoven)
Reinder Haakma (Philips Research)
Rik Vullings (University of Technology Eindhoven)

Abstract:
Over the last years, Pulse Transit Time (PTT) has garnered a lot of interest to continuously and non-invasively determine blood pressure without using a cuff. The advancements in wearable technology have increased the accessibility to outpatient monitoring. However, because of computational limitations and a limited battery life, a reduction in the sampling frequency is desired. This comes at the cost of the accuracy of the measurements. To obtain relevant clinical information for cardiovascular health, accurate estimation of pulse parameters is vital. A new method has been developed to estimate the location of the foot of the pulse more accurately in a signal with a lower sampling frequency. This is achieved by matching with a template. The matching is done by reducing the calculated error between the pulse and the template, dependent on three parameters, while allowing for subsample matching. The method is kept low in complexity in consideration of the computational limits and battery consumption. This method is tested on a dataset recording the subject for the PTT between the heart as recorded by electrography (ECG) and plethysmography (PPG) recorded at the tip of the non-dominant hand index finger. Both are acquired at a sampling frequency of 500 Hertz. A comparison is made between the results at 500 Hertz and when the PPG signal is reduced to as low as 25 Hertz, both with and without the newly introduced method. Enabling more accurate measurement in low frequency environments allows the increase of data gathering outside of controlled environments, as well as continuous monitoring. Furthermore, large databases currently exist, recorded in low frequency, that can retroactively be used to characterize vital markers able to be found in theory and high frequency environments.