Abstract
Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susceptible to noise signals. We focus on estimating the BP waveform rather than discrete BP values. We propose a model based on a generalized regression neural network to estimate the BP waveform, SBP and DBP. This model takes the raw PPG signal as input and BP waveform as output. The SBP and DBP are extracted from the estimated BP waveform. In addition, the model contains encoders and decoders, and their role is to be responsible for the conversion between the time domain and frequency domain of the waveform. The prediction results of our model show that the mean absolute error is 3.96 ± 5.36 mmHg for SBP and 2.39 ± 3.28 mmHg for DBP, the root mean square error is 5.54 for SBP and 3.45 for DBP. These results fulfill the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed model can effectively estimate the BP waveform only using the raw PPG signal.
Highlights
Blood pressure (BP) is an important physiological index for diagnosing diseases, observing changes in the condition and judging the effect of treatment
The results show that the proposed model can effectively estimate the BP waveform only using the raw PPG signal
We propose a model based on a generalized regression neural network (GRNN) to estimate the BP waveform from the raw PPG signal and extract
Summary
Blood pressure (BP) is an important physiological index for diagnosing diseases, observing changes in the condition and judging the effect of treatment. Sphygmomanometers are currently widely used BP measuring instruments These instruments measure BP via an inflatable cuff across the arm of a patient and BP is determined at the height of the mercury column [6]. This approach is uncomfortable and prohibits continuous BP measuring due to physical constraints. Continuous blood pressure measurement can be achieved in an invasive (intra-arterial) way. It is an expensive and invasive procedure and carries an increased risk of complications [7]. BP estimation methods based on PPG have been widely studied. This method is noninvasive, simple and easy to implement [8]
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