Review of photoplethysmography based non-invasive continuous blood pressure methods

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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“…Variations in the finger positioning within the cuff influence the BP measurement, reducing its precision [8].…”

Section: A Bp Estimation Based On Ppg With a Cuffmentioning

confidence: 99%

“…To simplify the measurement process and to make it more comfortable, noninvasive methods of measuring BP without cuffs, such as ballistocardiography (BCG), electronic bioimpedance (EBI), tonometry, and photoplethysmography (PPG), have been introduced [8]. DC and AC components of the PPG signal due to variation in light absorption [23].…”

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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“…Tekanan darah didefinisikan sebagai kekuatan yang diperlukan agar darah dapat mengalir di dalam pembuluh darah dan beredar mencapai ke seluruh jaringan tubuh manusia (Huynh & Jafari, 2018). Besarnya tekanan darah sangat bergantung pada elastisitas, kelenturan dan tebal dinding dari pembuluh darah arteri (Tjahjadi & Ramli, 2017).…”

Section: Pendahuluanunclassified

“…Pengembangan metode pengukuran non-invasive tekanan darah terus dikembangkan hingga kini. Salah satunya adalah metode PTT (Tjahjadi & Ramli, 2017).…”

Section: Pendahuluanunclassified

Pengukuran Tekanan Darah Non-Invasive Tanpa Manset Menggunakan Metode Pulse Transit Time Berbasis Machine Learning Multivariat Regresi

Susana

2019

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Non-Invasive Blood Pressure Measurement Without Cuff Using the Pulse Transit Time Method Based on Multivariate Regression Machine Learning.Currently used noninvasive blood pressure (NIBP) measurements (oscillometric method) has disadvantages related to pumping cuffs which can cause discomfort for patients due to pressure from pumping cuffs. The aim of this study was to measure blood pressure in a non-invasive manner without cuffs with the Pulse Transit Time (PTT) methodbase on machine learning technology.The blood pressure measurement by the PTT method is obtained from the calculation of the distance of the R-ECG wave with the peak signal photoplethysmogram (PPG). The main problem of the PTT method in some previous studies is that the estimation of systolic (SBP) and diastolic (DBP) values is still inaccurate. The blood pressure measurement method in this study used a combination of PTT calculations with machine learning multivariate regression. Therefore expected to obtain a more accurate estimate of systolic (SBP) and diastolic blood pressure (DBP). This study is a laboratory experiment research on 30 healthy volunteers aged 20 ± 1 years. The measurement of the blood pressure value of the PTT-to-oscillometric method is 5 ± 5 mmHg. The blood pressure values generated by this PTT method have a p-value for the sequential estimation of SBP and DBP of 0.7374 and 0.0262.

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“…The features of PPG are also recognized to carry important information that can be used as physiological parameters. Indeed, our previous work has presented statistical evidence that the features of PPG can be used to assess BP [20]. A single PPG-based BP estimation study was conducted to make users more comfortable [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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

Tjahjadi¹,

Ramli²

2020

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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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Review of photoplethysmography based non-invasive continuous blood pressure methods

Cited by 14 publications

References 17 publications

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

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

Pengukuran Tekanan Darah Non-Invasive Tanpa Manset Menggunakan Metode Pulse Transit Time Berbasis Machine Learning Multivariat Regresi

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

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