PCA-Based Multi-Wavelength Photoplethysmography Algorithm for Cuffless Blood Pressure Measurement on Elderly Subjects

Liu, Jing; Qiu, Shirong; Luo, Ningqi; Lau, Sze-Kei; Yu, Hui; Zhang, Yuan-Ting; Zhao, Ni

doi:10.1109/jbhi.2020.3004032

Cited by 36 publications

(26 citation statements)

References 14 publications

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“…There are two kinds of approaches for estimating BP based on PPG, using either the PPG signal only or PPG signal along with other signals (e.g., electrocardiogram) [11]. In [12][13][14][15], Pulse Transit Time (PTT)-based methods are carried out. PTT is the time interval between the R-peak of an electrocardiogram (ECG) and the point with maximum gradient on the rising edge of the PPG [16].…”

Section: Introductionmentioning

confidence: 99%

A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model

2021

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model

2021

Sensors

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“…From these principle components, Fourier transforms are used to extract features such as phase shift which indicates arteriolar PTT and HR with heart period and pulse decay time being computed separately. These features are used to compute mean blood pressure and pulse pressure which are then transformed into SBP and DBP estimates yielding errors of 1.44 ± 6.89mmHg for SBP and -1.00 ± 6.71mmHg for DBP [118].…”

Section: B Blood Pressurementioning

confidence: 99%

“…Utilizing multiple wavelengths for BP estimations showed improved error rates compared to single-wavelength estimations [73], [112], [118] especially when taking advantage of the differing interactions of wavelengths with skin and blood [118]. Methods that do not require an ECG device [73], [114], [118] have the advantage of not requiring multiple devices.…”

Section: B Blood Pressurementioning

confidence: 99%

A Review of Wearable Multi-Wavelength Photoplethysmography

Ray

Collins

Woolley

et al. 2023

IEEE Rev. Biomed. Eng.

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“…Through principal component analysis, signal noise and motion artifacts were removed to obtain more satisfactory features, and the least squares method was adopted for blood pressure estimation. The mean absolute error and standard deviation for SBP and DBP were 5.51 ± 6.89 and 5.57 ± 6.71 mmHg, respectively [5]. Kyriacou et al applied a bidirectional recurrent neural network and attention mechanism to PPG signals, which were in turn used as signals in the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II Databases to estimate blood pressure.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Continuous Blood Pressure Measurement by Dual Photoplethysmography Signals

Yen¹,

Chang²,

Liao³

et al. 2022

Computers, Materials &Amp; Continua

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This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography (PPG) sensors and a deep learning (DL) that can be used for continuous and rapid measurement of blood pressure and analysis of cardiovascular-related indicators. The proposed platform measured the signal changes in PPG and converted them into physiological indicators, such as pulse transit time (PTT), pulse wave velocity (PWV), perfusion index (PI) and heart rate (HR); these indicators were then fed into the DL to calculate blood pressure. The hardware of the experiment comprised 2 PPG components (i.e., Raspberry Pi 3 Model B and analog-todigital converter [MCP3008]), which were connected using a serial peripheral interface. The DL algorithm converted the stable dual PPG signals acquired from the strictly standardized experimental process into various physiological indicators as input parameters and finally obtained the systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial pressure (MAP). To increase the robustness of the DL model, this study input data of 100 Asian participants into the training database, including those with and without cardiovascular disease, each with a proportion of approximately 50%. The experimental results revealed that the mean absolute error and standard deviation of SBP was 0.17 ± 0.46 mmHg. The mean absolute error and standard deviation of DBP was 0.27 ± 0.52 mmHg. The mean absolute error and standard deviation of MAP was 0.16 ± 0.40 mmHg.

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PCA-Based Multi-Wavelength Photoplethysmography Algorithm for Cuffless Blood Pressure Measurement on Elderly Subjects

Cited by 36 publications

References 14 publications

A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model

A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model

A Review of Wearable Multi-Wavelength Photoplethysmography

A Deep Learning-Based Continuous Blood Pressure Measurement by Dual Photoplethysmography Signals

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