A Robust Pulse Onset and Peak Detection Method for Automated PPG Signal Analysis System

Vadrevu, Simhadri; Manikandan, M. Sabarimalai

doi:10.1109/tim.2018.2857878

Cited by 64 publications

(31 citation statements)

References 29 publications

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“…The OHRM has the advantage of offering unobtrusive, remote, and continuous monitoring. The photopletysmography (PPG) sensor in the OHRM has shown potential to provide robust peak detection from which HR may be calculated [ 8 , 9 ]. Validation studies have been presented on the accuracy of these devices in healthy participants [ 10 - 17 ].…”

Section: Introductionmentioning

confidence: 99%

Validation of Heart Rate Extracted From Wrist-Based Photoplethysmography in the Perioperative Setting: Prospective Observational Study

et al. 2021

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Background Measurement of heart rate (HR) through an unobtrusive, wrist-worn optical HR monitor (OHRM) could enable earlier recognition of patient deterioration in low acuity settings and enable timely intervention. Objective The goal of this study was to assess the agreement between the HR extracted from the OHRM and the gold standard 5-lead electrocardiogram (ECG) connected to a patient monitor during surgery and in the recovery period. Methods In patients undergoing surgery requiring anesthesia, the HR reported by the patient monitor’s ECG module was recorded and stored simultaneously with the photopletysmography (PPG) from the OHRM attached to the patient’s wrist. The agreement between the HR reported by the patient’s monitor and the HR extracted from the OHRM’s PPG signal was assessed using Bland-Altman analysis during the surgical and recovery phase. Results A total of 271.8 hours of data in 99 patients was recorded simultaneously by the OHRM and patient monitor. The median coverage was 86% (IQR 65%-95%) and did not differ significantly between surgery and recovery (Wilcoxon paired difference test P=.17). Agreement analysis showed the limits of agreement (LoA) of the difference between the OHRM and the ECG HR were within the range of 5 beats per minute (bpm). The mean bias was –0.14 bpm (LoA between –3.08 bpm and 2.79 bpm) and –0.19% (LoA between –5 bpm to 5 bpm) for the PPG- measured HR compared to the ECG-measured HR during surgery; during recovery, it was –0.11 bpm (LoA between –2.79 bpm and 2.59 bpm) and –0.15% (LoA between –3.92% and 3.64%). Conclusions This study shows that an OHRM equipped with a PPG sensor can measure HR within the ECG reference standard of –5 bpm to 5 bpm or –10% to 10% in the perioperative setting when the PPG signal is of sufficient quality. This implies that an OHRM can be considered clinically acceptable for HR monitoring in low acuity hospitalized patients.

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Section: Introductionmentioning

confidence: 99%

Validation of Heart Rate Extracted From Wrist-Based Photoplethysmography in the Perioperative Setting: Prospective Observational Study

et al. 2021

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“…Annotations are the labeled PPG waveform, such as a systolic peak, a diastolic peak, an onset, and a dicrotic notch in the time domain [26,27]. For the toolbox developed in this study, the onset and systolic peak were supported.…”

Section: Annotationsmentioning

confidence: 99%

PPGTempStitch: A MATLAB Toolbox for Augmenting Annotated Photoplethsmogram Signals

Tang

Chen

Menon

et al. 2021

Sensors

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An annotated photoplethysmogram (PPG) is required when evaluating PPG algorithms that have been developed to detect the onset and systolic peaks of PPG waveforms. However, few publicly accessible PPG datasets exist in which the onset and systolic peaks of the waveforms are annotated. Therefore, this study developed a MATLAB toolbox that stitches predetermined annotated PPGs in a random manner to generate a long, annotated PPG signal. With this toolbox, any combination of four annotated PPG templates that represent regular, irregular, fast rhythm, and noisy PPG waveforms can be stitched together to generate a long, annotated PPG. Furthermore, this toolbox can simulate real-life PPG signals by introducing different noise levels and PPG waveforms. The toolbox can implement two stitching methods: one based on the systolic peak and the other on the onset. Additionally, cubic spline interpolation is used to smooth the waveform around the stitching point, and a skewness index is used as a signal quality index to select the final signal output based on the stitching method used. The developed toolbox is free and open-source software, and a graphical user interface is provided. The method of synthesizing by stitching introduced in this paper is a data augmentation strategy that can help researchers significantly increase the size and diversity of annotated PPG signals available for training and testing different feature extraction algorithms.

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“…In [13], a Hilbert transform was accompanied by moving average and Shannon energy envelope techniques to locate the position of the systolic peaks. Vadrevu et al [28] introduced a stationary wavelet transform to extract two sets of coefficients from the PPG signal. Then, using multiscale sum and product, the peaks' sharpness was enhanced in the edges, and the other values remained near zero.…”

Section: ) Envelope Extraction and Peak Determinationmentioning

confidence: 99%

“…In the comparison, true-positive (TP) is when the PPG peaks are detected correctly, falsenegative (FN) is when the method fails to detect a peak, and false-positive (FP) is when the algorithm detects, e.g., noise as a peak. Then, the performance of the proposed method is assessed by calculating precision, recall, and F1-score as follows [28]:…”

Section: A Evaluation Measuresmentioning

confidence: 99%

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

Kazemi¹,

Laitala²,

Azimi³

et al. 2021

Preprint

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<div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div>

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A Robust Pulse Onset and Peak Detection Method for Automated PPG Signal Analysis System

Cited by 64 publications

References 29 publications

Validation of Heart Rate Extracted From Wrist-Based Photoplethysmography in the Perioperative Setting: Prospective Observational Study

Validation of Heart Rate Extracted From Wrist-Based Photoplethysmography in the Perioperative Setting: Prospective Observational Study

PPGTempStitch: A MATLAB Toolbox for Augmenting Annotated Photoplethsmogram Signals

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

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