Mathematical Model Based on the Shape of Pulse Waves Measured at a Single Spot for the Non-Invasive Prediction of Blood Pressure

Abstract

Background: Continuous non-invasive blood pressure (BP) measurement is a desired virtue in clinical practice. Unfortunately, current systems do not allow one for continuous, reliable BP measurement for more than a few hours per day, and they often require a complicated set of sensors to provide the necessary biosignals. Therefore we investigated the possibility of proposing a computational model that would predict the BP from pulse waves recorded in a single spot. Methods: Two experimental circuits were created. One containing a simple plastic tube for model development and a second with a silicone molded patient-specific arterial tree model. The first model served for the measuring of pulse waves under various BP (70–270 mmHg) and heart rate (60–190 beats per minute) values. Four different computational models were used to estimate the BP values from the diastolic time. The most accurate model was further validated using data from the latter experimental circuit containing a molded patient-specific silicone arterial tree. The measured data were averaged over a window of one, three, and five cycles. Two models based on pulse arrival time (PAT) were also analyzed for comparison. Results: The most accurate model exhibits a correlation coefficient of r = 0.967. The Bland–Altman plot revealed standard deviations (SD) between the model predictions and measurement of 10, 8.3, and 7.5 mmHg for the systolic BP and 8.7, 7 and 6.3 mmHg for the diastolic BP (both pressures calculated for the averaging windows of one, three, and five cycles, respectively). The best of the used PAT based model exhibited a SD of 17, 16, and 15 mmHg for the systolic BP and 14, 13, and 12 mmHg for the diastolic BP for the same averaging windows. Discussion: The proposed model showed its capability to predict BP accurately from the shape of the pulse wave measured at a single spot. Its SD was about 50% lower compared to the PAT based models which met the requirements of the Association for the Advancement of Medical Instrumentation.

A signal processing tool for extracting features from arterial blood pressure and photoplethysmography waveforms

“…From Table 1, it can be observed that the proposed tool had, on average, a sensitivity (SE) of 100%, a positive predictive value (PPV) greater than 99 %, and an F1 score greater than 99 % for the detection of all key points within a cardiac cycle in both ABP and PPG waveforms. The tool utilizes a single signal (the non-stationary component of the IEM method) to identify the temporal location of all key points within a cardiac cycle, in contrast to the two different signals employed in the literature (1 st and 2 nd derivatives) [15]. Moreover, in terms of computational cost, the IEM method requires only O (LN) operations for number of iterations L and signal length of N. The graphical user interface we designed to help researchers and clinicians use our proposed tool is shown in Fig.…”

“…These medical applications of PPG and ABP features clearly emphasize the importance of accurately detecting all five key points within a cardiac cycle in these waveforms, as they form the foundation for feature extraction and subsequent clinical applications. The use of derivatives (1 st and 2 nd ) of ABP and PPG waveforms is a common method for detecting key points within a cardiac cycle in these waveforms [15]. However, the sensitivity of signal derivatives to noise presents a significant challenge for accurate detection of these key points, potentially resulting in the extraction of inaccurate features.…”

An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method

“…Many different algorithms have been developed to detect the DN [6,7,25,26,27,28,29,30]. However, either these methods were not thoroughly tested for noise robustness, or they were not examined for their ability to identify the DN in signals lacking a prominent notch (DN-less signals).…”

“…However, either these methods were not thoroughly tested for noise robustness, or they were not examined for their ability to identify the DN in signals lacking a prominent notch (DN-less signals). Among these methods the 2 nd derivative method is the most widely used method for DN detection in the literature [28,31,32]. This approach employs the e-peak, the third peak within the 2 nd derivative of the PPG cardiac cycle, to estimate the DN within the cardiac cycle [31].…”

“…These are referred to as 'DN-less signals'. It is important to note that 'detecting the DN' means determining its temporal location within the cardiac cycle.Many different algorithms have been developed to detect the DN[6,7,25,26,27,28,29,30]. However, either these methods were not thoroughly tested for noise robustness, or they were not examined for their ability to identify the DN in signals lacking a prominent notch (DN-less signals).…”