Feature Selection Method for Estimating Systolic Blood Pressure Using the Taguchi Method

Suzuki, Arata; Ryu, Kazuteru

doi:10.1109/tii.2013.2288498

Cited by 34 publications

(10 citation statements)

References 34 publications

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“…Due to the high clinical importance of BP, in past few decades, a number of BP prediction methods have already been introduced, with the main agenda of predicting BP based on the parameters extracted from cardiac signals (ECG, PPG, Oscillometric waveform, ICG) [7][8][9][10]. Many researchers have proved that reliable BP estimation can be obtained using only one cardiac signal also [11][12][13]. Bussink et al in [14] and Fernández-Lozano et al in [15], conducted a study that focused on the relationship of right bundle branch block beats with the health of a person.…”

Section: Related Workmentioning

confidence: 99%

“…There are a number of feature based BP prediction approaches, introduced recently. All the studies followed a common concept-Systolic and Diastolic BP prediction based on the parameters extracted from the cardiac signals like ECG and/or PPG [7][8][9][10][11][12][13]33]. In our study, we predict beat to beat BP based on three parameters extracted from ECG-HR, QRS duration and PT duration.…”

Section: B Classification Based On Bpmentioning

confidence: 99%

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Cardiac Health Prediction using ElectrocardiographyPratishtha Agnihotri

Agnihotri¹,

Jain²

2019

IJRTE

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Amongst various physiological signals, that can be collected from the human body, Electrocardiogram (ECG) is one widely used signal that gives an overview of individual’s health non-invasively. Some prognostic tools, based on ECG, have already been introduced in the past. However, the diagnostic information contained in ECG is still under used. In the present study, we propose an algorithm that predicts the cardiac health (both present and future) by analyzing subject’s ECG. The prediction is based on diagnostic information like Blood Pressure (BP), Arrhythmia and Heart Rate Variability (HRV), where BP and Arrhythmia are used to predict the present cardiac health, and Arrhythmia and HRV are used to predict the future cardiac health associated with an individual. To verify the algorithm, we use: (1) Linear Regression Model to extract BP based on parameters extracted from ECG; (2) Neural Network Pattern Recognition Application to detect Arrhythmia- Right and Left bundle branch block beat, Atrial premature contraction beat, Premature ventricular contraction beat and Premature or ectopic supraventricular beats, in any ECG signal; (3) SelfOrganized Maps for HRV analysis using ECG. These models are used on ECG of 30 subjects chosen from an existing database. Based on the outputs of these models our algorithm predicts the present as well as the future cardiac health of 30 subjects under study. Our predictions are compared with the present and future cardiac health of these subjects already documented in the database. The prediction accuracy showed that present and future cardiac health risk of an individual can be satisfactorily determined using the proposed algorithm, which, in future, can be easily incorporated in any health monitoring device which can record ECG.

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Section: Related Workmentioning

confidence: 99%

Section: B Classification Based On Bpmentioning

confidence: 99%

Cardiac Health Prediction using ElectrocardiographyPratishtha Agnihotri

Agnihotri¹,

Jain²

2019

IJRTE

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“…Various CVD-related parameters can be obtained from PPG. These include heart rate (HR), heart rate variability ( 3 ), respiration rate ( 4 ), estimation of blood pressure ( 5 ), vascular aging index, ankle-brachial pressure index, and pulse transit time that has a strong correlation with arterial stiffness ( 5 , 6 ). Parameters other than those specified can be found in Ab Hamid and Nayan ( 7 ).…”

Section: Introductionmentioning

confidence: 99%

“…These have caused the peak detection of PPG to become challenging. Multiple methods to detect systolic peaks have been reported using a few filters or rules ( 9 , 10 ) and involve complex mathematical algorithms ( 6 , 11 , 12 ). Meanwhile, there is also a simple method to detect the peak using the derivatives method as reported in Li et al ( 13 ) and Elgendi et al ( 14 ), where the systolic peak can be easily detected using the zero-crossing point after the maximum amplitude of VPG.…”

Section: Introductionmentioning

confidence: 99%

Analysis on Four Derivative Waveforms of Photoplethysmogram (PPG) for Fiducial Point Detection

Suboh

Jaafar

Nayan

et al. 2022

Front. Public Health

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Fiducial points of photoplethysmogram (PPG), first derivative PPG (VPG), and second derivative PPG (APG) are essential in extracting numerous parameters to diagnose cardiovascular disease. However, the fiducial points were usually detected using complex mathematical algorithms. Inflection points from derivatives waveforms are not thoroughly studied, whereas they can significantly assist in peak detection. This study is performed to investigate the derivative waveforms of PPG and use them to detect the important peaks of PPG, VPG, and APG. PPGs with different morphologies from 43 ischemic heart disease subjects are analyzed. Inflection points of the derivative waveforms up to the fourth level are observed, and consistent information (derivative markers) is used to detect the fiducial points of PPG, VPG, and APG with proper sequence. Moving average filter and simple thresholding techniques are applied to detect the primary points in VPG and the third derivative waveform. A total of twelve out of twenty derivative markers are found reliable in detecting fiducial points of two common types of PPG. Systolic peaks are accurately detected with 99.64% sensitivity and 99.38% positive predictivity using the 43 IHD dataset and Complex System Laboratory (CSL) Pulse Oximetry Artifact Labels database. The study has introduced the fourth derivative PPG waveform with four main points, which are significantly valuable for detecting the fiducial points of PPG, VPG, and APG.

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“…The proposed cuffless method for estimating the blood pressure, which uses only a single photoplethysmography sensor, is drawing attention as a blood pressure measuring method that does not involve the use of an electrocardiographic sensor [8][9][10][11] . In this method, the blood pressure is determined on the basis of the pulse wave patterns acquired with the photoplethysmography sensor.…”

Section: Introductionmentioning

confidence: 99%

Inverse-model-based cuffless blood pressure estimation using a single photoplethysmography sensor

Suzuki

2015

Proc Inst Mech Eng H

Self Cite

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This paper proposes an inverse-model-based cuffless method for estimating blood pressure using a single photoplethysmography sensor. The proposed method, which is based on the relationship between blood pressure and the features of pulse waves, employs an inverse estimation and uses the blood pressure as the explanatory variable. Using this method, the blood pressure can be estimated with high accuracy even in situations where the pulse wave features are scattered, as the method uses the dynamic signal-to-noise ratio of the Taguchi method. In order to verify the effectiveness of the proposed method, we employed it to measure the systolic blood pressure. It could be confirmed that the estimation accuracy of the proposed method is higher than that of similar methods.

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Feature Selection Method for Estimating Systolic Blood Pressure Using the Taguchi Method

Cited by 34 publications

References 34 publications

Cardiac Health Prediction using ElectrocardiographyPratishtha Agnihotri

Cardiac Health Prediction using ElectrocardiographyPratishtha Agnihotri

Analysis on Four Derivative Waveforms of Photoplethysmogram (PPG) for Fiducial Point Detection

Inverse-model-based cuffless blood pressure estimation using a single photoplethysmography sensor

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