Measurement of Arterial Blood Pressure Through Single-Site Acquisition of Photoplethysmograph Signal

Chakraborty, Ayan; Goswami, Diganta; Mukhopadhyay, Jayanta; Chakrabarti, Saswat

doi:10.1109/tim.2020.3011304

Cited by 29 publications

(11 citation statements)

References 27 publications

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“…Therefore, the proposed method achieved a good correlation between the estimated BP and its actual value. The main drawback of their algorithm is that the accuracy of the A. Chakraborty et al [7] introduced two distinct approaches for the estimation of SBP, MAP, and DBP respectively. The first method used Two-Pulse Synthesis (TPS) model based on initialization method while the second method used a learning based non-parametric regression technique for the measurement of BP.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Recent advances in sensor technology have made it possible to monitor physiological parameters unobtrusively anywhere, anytime [7,8,9]. In this regard, photoplethysmogram (PPG) signal is crucial for the assessment of vital health-related factors without a reference signal and clinical condition [10].…”

Section: Introductionmentioning

confidence: 99%

“…1. PPG's simplicity, portability, and low cost enable its integration into mobile and wearable devices, giving an alternative for ubiquitous monitoring [7,11]. However, despite these benefits, PPG technology's susceptible nature towards noise and motion artifacts caused by moving fingers or hands, which extend the space between the sensor and the user's skin and alters the signal quality [12].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Technique for Continuous Blood Pressure Estimation from Optimal Feature Set of PPG Signal Using Deep Learning Approach

Raju

Dipto

Hossain

et al. 2023

Preprint

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Continuous Blood Pressure (BP) provides essential information for monitoring one's health condition. However, BP is currently monitoring using uncomfortable cuff-based devices, which does not support continuous BP monitoring. This paper aims to introduce a blood pressure monitoring algorithm based on only photoplethysmography (PPG) signals using the deep neural network (DNN). The PPG signals are obtained from 219 subjects with 657 records and filtered using signal processing algorithms to reduce the effects of noise, such as baseline wandering, and motion artifacts. The proposed algorithm is based on pulse wave analysis of PPG signals, extracted various domain features from PPG signals, and mapped them to BP values. Four feature selection methods are applied and yielded four feature subsets. Therefore, an ensemble feature selection technique is proposed to obtain the optimal feature set based on major voting scores from four feature subsets. DNN models, along with the ensemble feature selection technique, outperformed in estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) compared to previously reported approaches that rely only on the PPG signal. The coefficient of determination (R2) and mean absolute error (MAE) of the proposed algorithm are 0.962 and 2.480 mmHg respectively for SBP, 0.955 and 1.499 mmHg respectively for DBP. The proposed approach meets the Advancement of Medical Instrumentation standard for SBP and DBP estimations. Additionally, according to the British Hypertension Society standard, the results attained Grade A for both SBP and DBP estimations. It concludes that BP can be estimated more accurately using the optimal feature set and DNN models. The proposed algorithm has the potential ability to facilitate mobile healthcare devices to monitor continuous BP.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Technique for Continuous Blood Pressure Estimation from Optimal Feature Set of PPG Signal Using Deep Learning Approach

Raju

Dipto

Hossain

et al. 2023

Preprint

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“…Chakraborty et al applied PPG signals to nonparametric regression in order to estimate systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) in150 participants. The mean absolute error ± standard deviation for SBP, DBP, and MAP were 2.50 ± 2.07, 2.12 ± 1.79, and 2.69 ± 1.71 mmHg, respectively [3]. Chen et al used 1060 PPG and electrocardiography (ECG) signals to estimate blood pressure, normalizing related physiological signals such as age, height, weight, and body fat.…”

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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“…It outputs two signals: blood oxygen saturation and photoplethysmogram, which are important in clinical diagnosis. The photoplethysmogram signal can be used to calculate the physiological parameters, such as heart rate, [1][2][3] blood pressure, 4 pattern analysis, 5 authentication, 5,6 breathing, hypovolemia, and cardiovascular. 7 However, the PPG signal is susceptible to the interfered movement which would induce that the quality of the signal degrades and cannot accurately estimate the physiological parameters.…”

Section: Introductionmentioning

confidence: 99%

Abnormal pattern detection in PPG signals during sports and exercises

Quan

2020

Internet Technology Letters

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Pulse oximeter is an important clinical equipment, which can measure the photoplethysmogram (PPG) signal to calculate the heart rate, blood volume, breathing, hypovolemia etc. However, the PPG signal is easily interfered by some factors, such as body movement in sports or exercises. Before calculating the heart rate, blood volume, breathing, or hypovolemia, we need to recognize these interfered segments. In order to identify these abnormal patterns, the PPG signal is converted as a symbol sequence which consists of several characters. Then, the segment of PPG signal is represented as a character sequence. The abnormal pattern in PPG signal is detected by the distance between character sequences which are obtained from local and global information. When the distance is great than a threshold, the segment is regarded as an abnormal pattern. The proposed method also can be used in other physiological signals.

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Measurement of Arterial Blood Pressure Through Single-Site Acquisition of Photoplethysmograph Signal

Cited by 29 publications

References 27 publications

A Novel Technique for Continuous Blood Pressure Estimation from Optimal Feature Set of PPG Signal Using Deep Learning Approach

A Novel Technique for Continuous Blood Pressure Estimation from Optimal Feature Set of PPG Signal Using Deep Learning Approach

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

Abnormal pattern detection in PPG signals during sports and exercises

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