Machine Learning Methods for Real-Time Blood Pressure Measurement Based on Photoplethysmography

Xie, Qingsong; Wang, Guoxing; Peng, Zhengchun; Lian, Yong

doi:10.1109/icdsp.2018.8631690

Cited by 21 publications

(20 citation statements)

References 5 publications

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“…In Xie et al [ 15 ], a very small subset of the Queensland Vital sign dataset was used. The work in Carek et al [ 13 ] and Shimazaki et al [ 18 ] prepared their own dataset which is not publicly available.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…And on the other hand, our model obtained comparatively better MAE and STD than Esmaelpoor et al [ 47 ]. In Xie et al [ 15 ] and Simjanoska et al [ 46 ], the total amount of data was also very small and the result was also not satisfactory. Moreover, the work of Li et al [ 12 ], Wang et al [ 14 ], Xie et al [ 15 ], Shimazaki et al [ 18 ] and Esmaelpoor et al [ 47 ] needed beat segmentation to detect single PPG cycles.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Reviewing the difficulties of using a combination of signals, and as PPG is the most convenient signal to be used in wearable devices, researchers are trying to measure BP using only the PPG signal [ 14 , 15 ]. In [ 14 ], a total of 22 spectral and morphological features were extracted from PPG signals to characterize the signal to determine SBP and DBP output.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 14 ], a total of 22 spectral and morphological features were extracted from PPG signals to characterize the signal to determine SBP and DBP output. The study [ 15 ] also used 8 features of pre-processed PPG signals as the input for different machine learning methods and the output was SBP and DBP. But different feature selection problem also persists in these studies.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

Athaya

Choi

2021

Sensors

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Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson’s correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of 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 method is an efficient process to estimate ABP waveform directly using fingertip PPG.

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Section: Performance Evaluationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

Athaya

Choi

2021

Sensors

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show abstract

“…Moreover, another direction in measuring BP from PPG is to extract the influential features of PPG through signal processing to produce a vector of features, then apply machine learning to infer the function in terms of the blood pressure variables: Systolic blood pressure (SBP) and diastolic blood pressure (DBP) [27]. Many works in the literature used the conventional machine-learning algorithms to measure BP from PPG [28][29][30][31]. However, conventional machine learning techniques suffer from different issues.…”

Section: Introductionmentioning

confidence: 99%

A Continuous Cuffless Blood Pressure Estimation Using Tree-Based Pipeline Optimization Tool

Fati

Muneer

Akbar³

et al. 2021

Symmetry

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High blood pressure (BP) may lead to further health complications if not monitored and controlled, especially for critically ill patients. Particularly, there are two types of blood pressure monitoring, invasive measurement, whereby a central line is inserted into the patient’s body, which is associated with infection risks. The second measurement is cuff-based that monitors BP by detecting the blood volume change at the skin surface using a pulse oximeter or wearable devices such as a smartwatch. This paper aims to estimate the blood pressure using machine learning from photoplethysmogram (PPG) signals, which is obtained from cuff-based monitoring. To avoid the issues associated with machine learning such as improperly choosing the classifiers and/or not selecting the best features, this paper utilized the tree-based pipeline optimization tool (TPOT) to automate the machine learning pipeline to select the best regression models for estimating both systolic BP (SBP) and diastolic BP (DBP) separately. As a pre-processing stage, notch filter, band-pass filter, and zero phase filtering were applied by TPOT to eliminate any potential noise inherent in the signal. Then, the automated feature selection was performed to select the best features to estimate the BP, including SBP and DBP features, which are extracted using random forest (RF) and k-nearest neighbors (KNN), respectively. To train and test the model, the PhysioNet global dataset was used, which contains 32.061 million samples for 1000 subjects. Finally, the proposed approach was evaluated and validated using the mean absolute error (MAE). The results obtained were 6.52 mmHg for SBS and 4.19 mmHg for DBP, which show the superiority of the proposed model over the related works.

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A New Blood Pressure Estimation Approach Using PPG Sensors: Subject Specific Evaluation over a Long-term Period

Mouney

Tiplica

Fasquel

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Machine Learning Methods for Real-Time Blood Pressure Measurement Based on Photoplethysmography

Cited by 21 publications

References 5 publications

An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

A Continuous Cuffless Blood Pressure Estimation Using Tree-Based Pipeline Optimization Tool

A New Blood Pressure Estimation Approach Using PPG Sensors: Subject Specific Evaluation over a Long-term Period

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