Estimating Blood Pressure via Artificial Neural Networks Based on Measured Photoplethysmography Waveforms

K, Priyanka; Chao, Paul C.-P.; Tu, Tse-Yi; Kao, Yu-Hsiang; Yeh, Ming–Kung; Pandey, Rajeev; Eka, Fitrah P.

doi:10.1109/icsens.2018.8589796

Cited by 13 publications

(4 citation statements)

References 16 publications

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“…The average of each feature over the duration of the signal was then used as input to an artificial neural network with two hidden layers. This study concluded that proposed method provided high accuracy in estimating both SBP and DBP (Priyanka et al, 2018).…”

Section: Cuffless Methodsmentioning

confidence: 64%

Cuffless Blood Pressure Estimation Using Cardiovascular Dynamics

Samimi

Dajani

2022

2022 International Conference on Electrical, Computer and Energy Technologies (ICECET)

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Blood pressure (BP) monitoring is an important tool for management of hypertension, which is a significant risk for cardiovascular disease and premature death. Since cuff-based BP measurement can be uncomfortable and does not provide continuous readings, several cuffless methods that are typically based on within-beat information or on the pulse transit time (PTT) have recently been investigated. This work proposes a novel cuffless BP estimation approach that mainly uses the information from cardiovascular dynamics of photoplethysmogram (PPG) waveforms.This work is divided into three parts. The first part proposes a calibration-free approach that uses dynamic changes in the pulse waveform. Results from 200 patients showed that the method achieved grade B, in terms of accuracy, for diastolic blood pressure (DBP) based on the British Hypertension Society (BHS) standard and complied with the accuracy requirements of the Association for Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (AAMI/ESH/ISO) standard. The second part presents a method based on calibrated cardiovascular dynamics, achieved through a mathematical model that relates reflective PTT (R-PTT) to BP. Results from 30 patients showed a mean error (ME) of 0.58 mmHg, standard deviation of the error (SDE) of 8.13 mmHg, and a mean absolute error (MAE) of 4.93 mmHg for DBP and an ME of 2.52 mmHg, SDE of 12.28 mmHg, and an MAE of 8.82 mmHg for systolic blood pressure (SBP).The third part proposes a calibration-free method that combines morphology features and dynamic changes of the pulse waveform over short intervals. In this method a neural network was trained on 200 patients and tested on never-seen data from 25 other patients and provided an ME of -0.31 mmHg, SDE of 4.89 mmHg, and MAE of 3.32 mmHg for DBP and an ME of -4.02 mmHg, SDE of 10.40 mmHg, and MAE of 7.41 mmHg for SBP. Overall, the results show that cardiovascular dynamics may contribute useful information for cuffless estimation of BP.

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Section: Cuffless Methodsmentioning

confidence: 64%

Cuffless Blood Pressure Estimation Using Cardiovascular Dynamics

Samimi

Dajani

2022

2022 International Conference on Electrical, Computer and Energy Technologies (ICECET)

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“…Several studies agree on considering pulse rate variability assessed from PPG as a surrogate of heart rate variability [23,79,80] and recent studies have also investigated the use of PPG time series shorter than 300-samples as an alternative for the standard ST analysis [33,78]. Moreover, analyzing PPG signals allow to extract information also on blood pressure [81,82], and this would permit to achieve an insight into both heart rate and blood pressure variability which, as seen from our results, often yield complementary results.…”

Section: Discussionmentioning

confidence: 99%

Feasibility of Ultra-Short Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and During Stress via Time-domain and Entropy-based Measures

Volpes

Barà

Busacca

et al. 2022

Preprint

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Heart Rate Variability (HRV) and Blood Pressure Variability (BPV) are widely employed tools for characterizing the complex behavior of cardiovascular dynamics. Usually, HRV and BPV analyses are carried out through short-term (ST) measurements, which exploit ~5 minute-long recordings. Recent research efforts are focused on reducing the time series length, assessing whether and to what extent Ultra-Short Term (UST) analysis is capable of extracting information about cardiovascular variability from very short recordings. In this work, we compare ST and UST measures computed on electrocardiographic R-R intervals and systolic arterial pressure time series obtained at rest and during both postural and mental stress. Standard time-domain indices are computed, together with entropy-based measures able to assess regularity and complexity of cardiovascular dynamics, on time series lasting up to 60 samples, employing either a faster linear parametric estimator or a more reliable but time-consuming model-free method based on nearest neighbor estimates. Our results evidence that shorter time series up to 120 samples still exhibit an acceptable agreement with the ST reference, and can be exploited to dis-criminate between stress and rest as well. Moreover, although neglecting nonlinearities inherent to short-term cardiovascular dynamics, the faster linear estimator is still capable of detecting differences among the conditions, thus resulting suitable to be implemented on wearable devices.

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“…Wrist PPG and ECG signals were captured from smart watches in [ 12 , 13 ]. PTT was computed in [ 12 ] while reflective PTT, systolic period, and diastolic period were utilized in [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Wrist PPG and ECG signals were captured from smart watches in [ 12 , 13 ]. PTT was computed in [ 12 ] while reflective PTT, systolic period, and diastolic period were utilized in [ 13 ]. Chest PPG was measured in [ 14 , 15 ] and pulse arrival time (PAT) was acquired for the estimation.…”

Section: Introductionmentioning

confidence: 99%

Coherence between Decomposed Components of Wrist and Finger PPG Signals by Imputing Missing Features and Resolving Ambiguous Features

Tsai

Huang

Guo

et al. 2021

Sensors

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Background: Feature extraction from photoplethysmography (PPG) signals is an essential step to analyze vascular and hemodynamic information. Different morphologies of PPG waveforms from different measurement sites appear. Various phenomena of missing or ambiguous features exist, which limit subsequent signal processing. Methods: The reasons that cause missing or ambiguous features of finger and wrist PPG pulses are analyzed based on the concept of component waves from pulse decomposition. Then, a systematic approach for missing-feature imputation and ambiguous-feature resolution is proposed. Results: From the experimental results, with the imputation and ambiguity resolution technique, features from 35,036 (98.7%) of 35,502 finger PPG cycles and 36307 (99.1%) of 36,652 wrist PPG cycles can be successfully identified. The extracted features became more stable and the standard deviations of their distributions were reduced. Furthermore, significant correlations up to 0.92 were shown between the finger and wrist PPG waveforms regarding the positions and widths of the third to fifth component waves. Conclusion: The proposed missing-feature imputation and ambiguous-feature resolution solve the problems encountered during PPG feature extraction and expand the feature availability for further processing. More intrinsic properties of finger and wrist PPG are revealed. The coherence between the finger and wrist PPG waveforms enhances the applicability of the wrist PPG.

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Estimating Blood Pressure via Artificial Neural Networks Based on Measured Photoplethysmography Waveforms

Cited by 13 publications

References 16 publications

Cuffless Blood Pressure Estimation Using Cardiovascular Dynamics

Cuffless Blood Pressure Estimation Using Cardiovascular Dynamics

Feasibility of Ultra-Short Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and During Stress via Time-domain and Entropy-based Measures

Coherence between Decomposed Components of Wrist and Finger PPG Signals by Imputing Missing Features and Resolving Ambiguous Features

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