Study on Techniques Involved in Tourniqueteless Blood Pressure Measurement Using PPG

Savkar, Anagha; Khatate, Prathamesh; Patil, Chaithra

doi:10.1109/iccons.2018.8663139

Cited by 5 publications

(6 citation statements)

References 7 publications

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“…Most related studies have extracted parameters such as systolic time, diastolic time, 1/2 pulse amplitude, and maximum-minimum interval to estimate blood pressure. Although this technique improves the accuracy of blood pressure estimation, it must estimate blood pressure according to pulse arrival time, and that can only be achieved when a complete and stable waveform is obtained [14]. Therefore, continuous blood pressure estimation is challenging with this technique.…”

Section: Ppg Pulse Wavementioning

confidence: 99%

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Development of a Continuous Blood Pressure Measurement and Cardiovascular Multi-Indicator Platform for Asian Populations by Using a Back Propagation Neural Network and Dual Photoplethysmography Sensor Signal Acquisition Technology

Yen

Chang

Cai

2021

Journal of Nanomaterials

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This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography (PPG) sensors and a back propagation neural network (BPNN) 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), heart rate (HR), and pulse wave analysis (PWA); these indicators were then fed into the BPNN to calculate blood pressure. The hardware of the experiment comprised 2 PPG components (i.e., Raspberry Pi 3 Model B and analog-to-digital converter [MCP3008]), which were connected using a serial peripheral interface. The BPNN 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) and diastolic blood pressure (DBP). To increase the robustness of the BPNN 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 and standard deviation of SBP were 2.23 ± 2.24 mmHg , with a mean squared error of 3.15 mmHg. The mean and standard deviation of DBP was 3.5 ± 3.53 mmHg , with a mean squared error of 4.96 mmHg. The proposed real-time blood pressure measurement system exhibited a mean accuracy of 98.22% and 95.58% for SBP and DBP, respectively.

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Section: Ppg Pulse Wavementioning

confidence: 99%

“…PWV is inversely correlated with PTT. Although the methods differ, the three variables are closely related [14]. However, in recent years, some scholars have proposed perfusion index (PI), which is defined as the ability of the cardiovascular system to provide sufficient blood for normal functioning and metabolism of body tissues.…”

Section: Introductionmentioning

confidence: 99%

Development of a Continuous Blood Pressure Measurement and Cardiovascular Multi-Indicator Platform for Asian Populations by Using a Back Propagation Neural Network and Dual Photoplethysmography Sensor Signal Acquisition Technology

Yen

Chang

Cai

2021

Journal of Nanomaterials

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“…Two common ways are used for finding BP: noninvasive and invasive methods. Noninvasive BP measurement is done through cuff-based readings, but it is not comfortable for infants, injured, and overweight people [ 9 ]. Also, its end result is discrete (that is, some set of intervals is required) and thus uncomfortable to the patient.…”

Section: Introductionmentioning

confidence: 99%

Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing

Sadad

Bukhari

Munir

et al. 2022

Computational Intelligence and Neuroscience

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Hypertension is the main cause of blood pressure (BP), which further causes various cardiovascular diseases (CVDs). The recent COVID-19 pandemic raised the burden on the healthcare system and also limits the resources to these patients only. The treatment of chronic patients, especially those who suffer from CVD, has fallen behind, resulting in increased deaths from CVD around the world. Regular monitoring of BP is crucial to prevent CVDs as it can be controlled and diagnosed through constant monitoring. To find an effective and convenient procedure for the early diagnosis of CVDs, photoplethysmography (PPG) is recognized as a low-cost technology. Through PPG technology, various cardiovascular parameters, including blood pressure, heart rate, blood oxygen saturation, etc., are detected. Merging the healthcare domain with information technology (IT) is a demanding area to reduce the rehospitalization of CVD patients. In the proposed model, PPG signals from the Internet of things (IoT)-enabled wearable patient monitoring (WPM) devices are used to monitor the heart rate (HR), etc., of the patients remotely. This article investigates various machine learning techniques such as decision tree (DT), naïve Bayes (NB), and support vector machine (SVM) and the deep learning model one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) to develop a system that assists physicians during continuous monitoring, which achieved an accuracy of 99.5% using PPG-BP data set. The proposed system provides cost-effective, efficient, and fully connected monitoring systems for cardiac patients.

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“…While invasive methods can measure BP precisely and continuously, they are not very suitable to apply and trigger infections in patients [5]. The noninvasive methods that are presently implemented using a cuff cause discomfort, particularly for injured people, overweight people, and infants [6].…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Blood Pressure Classification Based on Photoplethysmography Using K-Nearest Neighbors Algorithm: A Feasibility Study

Tjahjadi¹,

Ramli²

2020

Information

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Blood pressure (BP) is an important parameter for the early detection of heart disease because it is associated with symptoms of hypertension or hypotension. A single photoplethysmography (PPG) method for the classification of BP can automatically analyze BP symptoms. Users can immediately know the condition of their BP to ensure early detection. In recent years, deep learning methods have presented outstanding performance in classification applications. However, there are two main problems in deep learning classification methods: classification accuracy and time consumption during training. We attempt to address these limitations and propose a method for the classification of BP using the K-nearest neighbors (KNN) algorithm based on PPG. We collected data for 121 subjects from the PPG-BP figshare database. We divided the subjects into three classification levels, namely normotension, prehypertension, and hypertension, according to the BP levels of the Joint National Committee report. The F1 scores of these three classification trials were 100%, 100%, and 90.80%, respectively. Hence, it is validated that the proposed method can achieve improved classification accuracy without additional manual pre-processing of PPG. Our proposed method achieves higher accuracy than convolutional neural networks (deep learning), bagged tree, logistic regression, and AdaBoost tree.Information 2020, 11, 93 2 of 18 blood volume changes, which occur between the systolic and diastolic phases of the cardiac cycle. The essential frequency of the AC component depends on the heart rate and is covered onto the DC component [11]. An LED is a light source that can be used to illuminate blood vessels so minor perfusion changes can be supervised on the photodetectors [12]. Perfusion is measured as the degree at which blood is distributed to tissue [13].

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Study on Techniques Involved in Tourniqueteless Blood Pressure Measurement Using PPG

Cited by 5 publications

References 7 publications

Development of a Continuous Blood Pressure Measurement and Cardiovascular Multi-Indicator Platform for Asian Populations by Using a Back Propagation Neural Network and Dual Photoplethysmography Sensor Signal Acquisition Technology

Development of a Continuous Blood Pressure Measurement and Cardiovascular Multi-Indicator Platform for Asian Populations by Using a Back Propagation Neural Network and Dual Photoplethysmography Sensor Signal Acquisition Technology

Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing

Noninvasive Blood Pressure Classification Based on Photoplethysmography Using K-Nearest Neighbors Algorithm: A Feasibility Study

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