An Automated Algorithm to Extract Time Plane Features from the PPG Signal and its Derivatives for Personal Health Monitoring Application

Chakraborty, Abhishek; Sadhukhan, Deboleena; Mitra, Madhuchhanda

doi:10.1080/03772063.2019.1604178

Cited by 18 publications

(10 citation statements)

References 27 publications

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“…At present, only systolic annotations are available for evaluation. A total of five performance matrices are calculated to evaluate the systolic peak detection using Equations ( 6)- (10), which includes sensitivity (SN), positive predictivity (PPV), accuracy (ACC), error rate (ERR), and mean absolute error (MAE). Complete results for systolic peak detection of the two CSL data and 43 IHD subjects' data are shown in Table 3.…”

Section: Resultsmentioning

confidence: 99%

“…• PPG: onset, systolic, notch, and diastolic peaks • VPG: u, v, and w peaks • APG: a, b, c, d, and e peaks Photoplethysmogram is recorded using a pulse oximetry device, which is sensitive to skin structure (color), skin temperature, electrical noise, motion artifacts, and measuring environment (9). PPG is also significantly affected by the physiological variability of aging, hypertension, diabetes, vessel compliance, and apnoeic episodes (10). These have caused the peak detection of PPG to become challenging.…”

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%

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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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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Derivatives of PPG: according to literature, the first and second derivatives of PPG also provide valuable information or features while predicting BP. They are called various names such as PPG', PPG" or Velocity of PPG (VPG), Acceleration of PPG (APG), or FDPPG (First Derivative of PPG), SDPPG (Second Derivative of PPG) [52][53][54] (Figure 2). To find the VPG and APG from PPG, MATLAB's 'diff' function was used.…”

Section: Algorithm 1 Pseudo-code: Baseline Drift Correctionmentioning

confidence: 99%

A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals

Mahmud¹,

Ibtehaz²,

Khandakar³

et al. 2022

Sensors

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Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous blood pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict systolic blood pressure (SBP) and diastolic blood pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP from non-invasively collectible signals such as photoplethysmograms (PPG) and electrocardiograms (ECG), which can be used for continuous monitoring. In this study, we explored the applicability of autoencoders in predicting BP from PPG and ECG signals. The investigation was carried out on 12,000 instances of 942 patients of the MIMIC-II dataset, and it was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with state-of-the-art performance on a very large dataset. An independent test set from a portion of the MIMIC-II dataset provided a mean absolute error (MAE) of 2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of 40 subjects, the model trained on the MIMIC-II dataset provided an MAE of 2.728 and 1.166 for SBP and DBP, respectively. For both the cases, the results met British Hypertension Society (BHS) Grade A and surpassed the studies from the current literature.

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“…Furthermore, baseline modulation was removed from the PPG data. This baseline wander can be a result of losing body and sensor contact due to the subject's unwanted movement during measurement or because of abnormal breathing [36].…”

Section: Preprocessingmentioning

confidence: 99%

Poincaré’s Section Analysis of Photoplethysmography Signals for Cuff-Less Non-Invasive Blood Pressure Measurement

Shoeibi¹,

Aghdam²,

Ebrahimi³

2021

Preprint

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Background and Objective: Hypertension is a serious problem that has become dramatically more common in recent decades. Hypertension can be managed in its early stages by regular monitoring of blood pressure. Blood pressure, as a vital signal, has an essential role in the prediction of many cardiovascular diseases. Therefore, non-invasive, cuff-less, continuous monitoring of blood pressure has special importance in personal health care. Recently, due to the capabilities of PPG sensors in embedding and compacting as a wearable device, application of the PPG signal and its characteristics as a useful facilities for BP measurement have been highlighted. Methods: This study attempts to provide a new indicator of PPG waveforms to help the rapid developments in this research area. The proof of the feasibility of using Poincaré’s section for extracting the profitable features of the PPG signal for BP estimation is one of the key achievements of this paper. Results: The performance of the method was evaluated on 101 subject’s clinical data from the MIMIC II database. The proposed method obtains a mean absolute error of 2.1 mmHg for systolic pressure and 1.4 mmHg for diastolic pressure prediction. Also, the results meet the AAMI and BHS standards, which demonstrate the feasibility of Poincaré’s section-based indices in BP estimation. Conclusions: The results confirm the proficiency of this method in the blood pressure estimation and a straightforward way to reduce the computational and hardware complexity, which in turn helps to achieve a real-time wearable BP monitoring system.

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An Automated Algorithm to Extract Time Plane Features from the PPG Signal and its Derivatives for Personal Health Monitoring Application

Cited by 18 publications

References 27 publications

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

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

A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals

Poincaré’s Section Analysis of Photoplethysmography Signals for Cuff-Less Non-Invasive Blood Pressure Measurement

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