Variance Analysis of Photoplethysmography for Blood Pressure Measurement

Tjahjadi, Hendrana; Ramli, Kalamullah

doi:10.11591/eecsi.v4.999

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The multi-channel information is then included into the suggested methodologies for measuring circulatory strain directly. Hendrana Tjahjadi, et al, [12] proposed a method for the classification of BP using the K-nearest neighbors (KNN) algorithm based on PPG. A collection of data for 121 subjects from the PPG-BP figshare database were used and segregated the subjects into three classification levels, namely normotension, prehypertension, and hypertension, according to the BP levels of the Joint National Committee report.…”

Section: Introductionmentioning

confidence: 99%

Non-invasive Cuff Free Blood Pressure and Heart Rate Measurement From Photoplethysmography (Ppg) Signal Using Machine Learning

Chakraborty

Tharini

2022

Preprint

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Measuring and monitoring Blood Pressure for cardiovascular patients is critical and essential too. A variation in heart rate is very important for a patient’s physiological condition analysis. But using cuff every time for measuring Blood Pressure, passing electrical signal to monitor Heart rate may irritate the patient. This idea provides cuff less measurement of Blood Pressure and Heart rate measurement using Photoplethysmography only. Arterial Blood Pressure and Photoplethysmogram along with Electrocardiogram is the most popular methods of measuring Cardio Vascular Status these days. Mostly both ABP and PPG together perform operations like monitoring Blood pressure with high efficiency and ECG for Heart rate. Measuring Heart rate from ECG and Blood Pressure from ABP may cause discomfort to the subject. So, we implement only PPG based system to monitor Both Blood Pressure and Heart rate. Now a days, Internet of Things, Cloud, Artificial Intelligence, and Machine Learning are taking their place everywhere to reduce the man power and ease the computations in automating this world. We detect the PPG signal and transfer the data to cloud in order to apply the Machine Learning algorithm to measure the Blood pressure and Heart rate. This paper gives about the various Machine learning ideas of measuring Blood Pressure along with Heart rate. We used Support Vector Regression, Random Forest, Decision Tree, Adaboost and Adaboost hyper tuning algorithms of machine learning for prediction of Blood Pressure and resulted with high accuracy in Decision Tree and Adaboost algorithms among all the five algorithms tried.

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Section: Introductionmentioning

confidence: 99%

Non-invasive Cuff Free Blood Pressure and Heart Rate Measurement From Photoplethysmography (Ppg) Signal Using Machine Learning

Chakraborty

Tharini

2022

Preprint

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“…The features of PPG are also known to carry important information that can be used as physiological parameters [9]. Our previous work has shown statistically that the features of PPG can be used to estimate BP [10]. The generation of PPG signals requires some optoelectronic components, namely, a light-emitting diode (LED) and a photodetector.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Classification of Blood Pressure Based on Photoplethysmography Signals Using Bidirectional Long Short-Term Memory and Time-Frequency Analysis

2020

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The photoplethysmography (PPG) method for continuous noninvasive measurements of blood pressure (BP) offers a more comfortable solution than conventional methods. The main challenge in using the PPG method is that its accuracy is greatly influenced by motion artifacts. In addition, the characteristics of PPG vary depending on physiological conditions; hence, the system must be calibrated to adjust for such changes. We attempt to address these limitations and propose a novel method for the classification of BP using a bidirectional long short-term memory (BLSTM) network with time-frequency (TF) analysis based on PPG signals. The TF analysis extracts information from PPG signals using a short-time Fourier transform (STFT) in the time domain to produce two features, namely, the instantaneous frequency and spectral entropy. Training the BLSTM network using TF features significantly improves the classification performance and decreases the training time. We classify 900 PPG waveform segment samples from 219 adult subjects into three classification levels: normotension (NT), prehypertension (PHT) and hypertension (HT). The results show that the proposed method is successful in the classification of BP with accuracy, sensitivity, and speciticity values of 97.33%, 100%, and 94.87%, respectively. The F1 scores of three BP classifications were 97.29%, 97.39%, and 93.93%, respectively. A comparison of current and previous approaches to the classification of BP is accomplished. Our proposed method achieves a higher accuracy than convolutional neural networks (CNNs), k-nearest neighbors (KNN), bagged tree, logistic regression, and AdaBoost tree methods.

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Non-invasive cuff free blood pressure and heart rate measurement from photoplethysmography (PPG) signal using machine learning

Chakraborty,

Tharini

2024

Wireless Pers Commun

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Variance Analysis of Photoplethysmography for Blood Pressure Measurement

Cited by 3 publications

References 7 publications

Non-invasive Cuff Free Blood Pressure and Heart Rate Measurement From Photoplethysmography (Ppg) Signal Using Machine Learning

Non-invasive Cuff Free Blood Pressure and Heart Rate Measurement From Photoplethysmography (Ppg) Signal Using Machine Learning

Noninvasive Classification of Blood Pressure Based on Photoplethysmography Signals Using Bidirectional Long Short-Term Memory and Time-Frequency Analysis

Non-invasive cuff free blood pressure and heart rate measurement from photoplethysmography (PPG) signal using machine learning

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