Respiratory rate, heart rate and continuous measurement of BP using PPG

Daimiwal, Nivedita; Sundhararajan, M.; Shriram, Revati

doi:10.1109/iccsp.2014.6949996

Cited by 32 publications

(11 citation statements)

References 2 publications

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“…Considering the published results, the achieved mean error for the systolic BP (SBP) and diastolic BP (DBP) estimation is 5.1 ± 4.3 mmHg, and 4.6 ± 4.3 mmHg, respectively, in a case study that encompasses 78 PPG records and matching SBP and DBP values [ 81 ]; an error of ±4.76 mmHg for DBP, ±4.23 mmHg for the mean arterial pressure (MAP), and ±5.93 mmHg for SBP is achieved in a pilot study of 150 recordings from 10 subjects [ 27 ]; 9 ± 5.6 mmHg for SBP and 1.8 ± 1.3 mmHg for DBP is obtained from a method that uses Ballistocardiography (BCG) and PPG signals [ 82 ]; 0.8 ± 7 mmHg for SBP and 0.9 ± 6 mmHg for DBP by using PPG signals [ 83 ]; accuracy results of 7.487 ± 8.824 mmHg (mean ± SD) for SBP and 4.076 ± 5.617 mmHg (mean ± SD) for DBP from a PTT-based study [ 26 ]; and a similar PTT-based method tested on 300 datasets from six subjects provides a SD of 6.492 mmHg [ 84 ]. Table 11 presents a comparison of the results reported in this paper with the results reported in the literature.…”

Section: Discussionmentioning

confidence: 99%

Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Simjanoska

Gjoreski

Gams

et al. 2018

Sensors

135

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Background: Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals. Methods: Raw ECG data are filtered and segmented, and, following this, a complexity analysis is performed for feature extraction. Then, a machine-learning method is applied, combining a stacking-based classification module and a regression module for building systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP) predictive models. In addition, the method allows a probability distribution-based calibration to adapt the models to a particular user. Results: Using ECG recordings from 51 different subjects, 3129 30-s ECG segments are constructed, and seven features are extracted. Using a train-validation-test evaluation, the method achieves a mean absolute error (MAE) of 8.64 mmHg for SBP, 18.20 mmHg for DBP, and 13.52 mmHg for the MAP prediction. When models are calibrated, the MAE decreases to 7.72 mmHg for SBP, 9.45 mmHg for DBP and 8.13 mmHg for MAP. Conclusion: The experimental results indicate that, when a probability distribution-based calibration is used, the proposed method can achieve results close to those of a certified medical device for BP estimation.

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Section: Discussionmentioning

confidence: 99%

Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Simjanoska

Gjoreski

Gams

et al. 2018

Sensors

135

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“…Normally, PTT is calculated from the time distance between R wave of ECG signal and following peak of fingertip PPG signal [10]. PTT based methods [5]- [7], [11]- [13] have been demonstrated to achieve relatively high accuracy on BP estimation, but two devices are required for the measurement. This is inconvenient and requires high operation skill in practical usage.…”

Section: Introductionmentioning

confidence: 99%

“…It was reported that Manuscript received August 4, 2016; revised December 9, 2016 cuff-less, noninvasive, and continuous measurement of BP could be achieved by photoplethysmography (PPG) [1]- [7]. PPG [8] is a simple, portable and low-cost optical pulse that can be used to detect blood volume changes.…”

Section: Introductionmentioning

confidence: 99%

Cuffless Blood Pressure Estimation Based on Photoplethysmography Signal and Its Second Derivative

Li¹,

Po²,

Fu³

2017

IJCTE

110

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I. INTRODUCTIONBlood pressure (BP) is one of the four vital signs of human body besides heart rate, respiratory rate, and body temperature. Recently, the risk of having hypertension is extending from older to younger groups of the population. This trend making the number of people who need daily monitoring of BP is significantly increased. In addition, the demand for public resources in the hospital is becoming more and more serious and the type of healthcare is tending to individual-centered rather than hospital-centered. The need of self-monitoring BP devices dramatically increases in the recent decades. However, most of the commercial BP measuring devices require to inflate and deflate a cuff for determining the BP values. This measurement has a high demand for the medical practitioner"s operation skills.Self-monitoring BP devices using cuffless and noninvasive detective methods are very attractive for personal healthcare applications. It was reported that Manuscript received August 4, 2016; revised December 9, 2016 cuff-less, noninvasive, and continuous measurement of BP could be achieved by photoplethysmography (PPG) [1]- [7]. PPG [8] is a simple, portable and low-cost optical pulse that can be used to detect blood volume changes. The waveform of PPG is formed by heartbeat and affected by the condition of cardiovascular system [9]. When BP increases, the volumetric elasticity of blood vessel increases. This makes vessel wall stiff and the pulse wave propagation velocity is increased. The pulse wave propagation velocity (PWV) is represented as the pulse wave propagation distance divided by Pulse transit time (PTT). PTT is the time for the pulse wave to travel between two arterial sites, which is highly related with PWV. Normally, PTT is calculated from the time distance between R wave of ECG signal and following peak of fingertip PPG signal [10]. PTT based methods [5]-[7], [11]- [13] have been demonstrated to achieve relatively high accuracy on BP estimation, but two devices are required for the measurement. This is inconvenient and requires high operation skill in practical usage. Moreover, synchronization between two devices is also a big challenge of PTT approach. Therefore, PPG waveform analysis approach using a single PPG signal is very attractive for continuous and noninvasive BP measurement. However, several reported PPG waveform analysis based BP estimation methods [14], [15] From the recent studies [17], [18] of vascular aging through the second derivative of PPG (SDPPG), Pulse Wave Velocity (PWV) and SDPPG contain the information about aortic compliance and stiffness. These factors highly correlated with blood pressure. Basically, SDPPG is the acceleration of PPG and its waveform is in "W" shape with five sequential waves normally, as shown in the lower part of Fig. 1. These five waves are named as Initial Positive Wave (IPW), Early Negative Wave (ENW), Late Upsloping Wave (LUW), Late Downsloping Wave (LDW), and Diastolic Positive Wave (DPW). The positions of "a", "b", "c", "d", and "e" are peaks...

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“…1. Types of PPG sensor [11] The PPG sensor provides the change in the voltage level according to the change in the blood flow and thus the PPG signal in voltage values can be obtained from it. This signal can be easily acquired and plotted to calculate the heart rate and various other parameters.…”

Section: Photoplethysmographymentioning

confidence: 99%

Development of a Low Cost Heart Rate Monitoring and Transmission System using PPG Signal Processing for Wearable Devices

Chaurasia¹,

Hasamnis²

2019

IJITEE

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Photoplethysmography (PPG) technique is used in most of the fitness tracking devices available now-a-days, due to its low cost and simplicity. These PPG signals are obtained from the variations in the blood flow with the help of pulse rate sensor. In this paper, PPG signal is acquired from two PPG sensors worn in the finger and the wrist to get the heart rate and its various parameters. The signal noise is removed with the help of MATLAB with various filters to get the smooth signal. The peak detection is done for heart rate (HR) and peak interval calculation, the spectral estimation is done, the first and second derivatives and the heart rate variability are obtained. The low cost Arduino nano and the Bluetooth module is used for the development and the transmission of HR value from the wearable device through an application developed for it, as well as the values are transmitted remotely with the help of the Global System for Communication (GSM) built in the mobile phone, which can display the value and can also transmit it to any particular person or remote physician who can monitor the person’s HR remotely. The complete system developed provides a low cost solution for heart rate detection and monitoring of a person from a distance.

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Respiratory rate, heart rate and continuous measurement of BP using PPG

Cited by 32 publications

References 2 publications

Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Cuffless Blood Pressure Estimation Based on Photoplethysmography Signal and Its Second Derivative

Development of a Low Cost Heart Rate Monitoring and Transmission System using PPG Signal Processing for Wearable Devices

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