Is Heart Rate a Confounding Factor for Photoplethysmography Markers? A Systematic Review

Lazim, Rizman Md Lazin Md; Aminuddin, Amilia; Chellappan, Kalaivani; Ugusman, Azizah; Hamid, Adila A.; Ahmad, Wan Amir Nizam Wan; Mohamad, Mohd Shawal Faizal

doi:10.3390/ijerph17072591

Cited by 11 publications

(7 citation statements)

References 52 publications

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“…For clinical applications, the lossless decrypted PPG signals could be further used to evaluate the blood pressure, arterial stiffness, compliance, and UEVD/LEVD risk levels. In this study, with the time-domain analysis, experimental results indicated that the PPG signals could be applied for PPI, PRV, RT, and RI analysis [9,46,47]. RT and RI indexes of the 40 subjects could be divided into three groups, separating the normal condition from the lower PAD/higher PAD.…”

Section: Clinical Applications With Decrypted Ppg Signals and Discussionmentioning

confidence: 99%

“…The time-domain analysis in Figure 4a indicates that a PPG waveform can also be used to assess CVDs, such as SI and RI [9,46,47]. SI can be estimated by (body height/t sdn ), where t sdn is the time interval between early systolic peak and diastolic peak, which indicates the stiffness level in large arteries.…”

Section: Evaluation Of the Decryption Performances For Clinical Appli...mentioning

confidence: 99%

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Intelligent physiological signal infosecurity: Case study in photoplethysmography (PPG) signal

Lin

Pai

et al. 2021

IET Signal Processing

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A wrist‐based photoplethysmography (PPG) tool offers a simple and non‐invasive approach for applications in vital sign monitoring and healthcare. However, in a telecare network, these physiological signals ensure the authorisation demands for the domain of medical applications. Hence, this study proposes an intelligent method for PPG signal encryption and decryption. This method combines chaotic map and radial basis function network (RBFN) into symmetric cryptography with an adaptive scheme. The sine‐power chaotic map is used as a key generator of 256 non‐ordered numbers (key space) to set the private cipher codes. Two RBFNs are used to train an encryptor and a decryptor with the authorised cipher codes. Its substitution‐based infosecurity scheme can change the numerical values of PPG raw data for each encrypted communication, and the decrypted PPG data are further applied for time‐ and frequency‐domain analyses in clinical applications, such as heart and respiration rate analysis and arterial stiffness and upper extremity vascular disease evaluations. Through experimental results, the security levels are validated using the number of pixel change rate (NPCR), unified averaged changed intensity (UACI), and correlation analysis. The average NPCR, UACI, and correlation coefficient (CC) are 96.57%, 35.43%, and 0.005, respectively, between the plain PPG and the encrypted PPG data against hacker attacks. The larger‐the‐better of NPCR and UACI indexes and the smaller‐the‐better of CC index are obtained to evaluate the efficiency of the proposed cryptography method. The encrypted PPG also guarantees physiological signals of good quality in clinical applications. In addition, the performance of RBFN‐based method is superior in adaptive learning capability than that of the traditional learning method in real‐time applications. The cover image is based on the Original Research Paper Intelligent physiological signal infosecurity: Case study in photoplethysmography (PPG) signal by Chia Hung‐Lin et al., https://doi.org/10.1049/sil2.12089.

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Section: Clinical Applications With Decrypted Ppg Signals and Discussionmentioning

confidence: 99%

Section: Evaluation Of the Decryption Performances For Clinical Appli...mentioning

confidence: 99%

Intelligent physiological signal infosecurity: Case study in photoplethysmography (PPG) signal

Lin

Pai

et al. 2021

IET Signal Processing

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“…Among these methods the 2 nd derivative method is the most widely used method for DN detection in the literature [28,31,32]. This approach employs the e-peak, the third peak within the 2 nd derivative of the PPG cardiac cycle, to estimate the DN within the cardiac cycle [31]. The primary limitation of the 2 nd derivative approach is its high sensitivity to noise.…”

Section: Introductionmentioning

confidence: 99%

An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method

Pal,

Rudas,

Kim

et al. 2024

Preprint

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Background and Objective: Detection of the dicrotic notch (DN) within a cardiac cycle is essential for assessment of cardiac output, calculation of pulse wave velocity, estimation of left ventricular ejection time, and supporting feature-based machine learning models for noninvasive blood pressure estimation, and hypotension, or hypertension prediction. In this study, we present a new algorithm based on the iterative envelope mean (IEM) method to detect automatically the DN in arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms. Methods: The algorithm was evaluated on both ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. The analysis involved a total of 1,171,288 cardiac cycles for ABP waveforms and 3,424,975 cardiac cycles for PPG waveforms. To evaluate the algorithm's performance, the systolic phase duration (SPD) was employed, which represents the duration from the onset of the systolic phase to the DN in the cardiac cycle. Correlation plots and regression analysis were used to compare the algorithm with an established DN detection technique (second derivative). The marking of the DN temporal location was carried out by an experienced researcher using the help of the find_peaks function from the scipy PYTHON package, serving as a reference for the evaluation. The marking was visually validated by both an engineer and an anesthesiologist. The robustness of the algorithm was evaluated as the DN was made less visually distinct across signal-to-noise ratios (SNRs) ranging from -30 dB to -5 dB in both ABP and PPG waveforms. Results: The correlation between SPD estimated by the algorithm and that marked by the researcher is strong for both ABP (R2(87343) =.99, p<.001) and PPG (R2(86764) =.98, p<.001) waveforms. The algorithm had a lower mean error of dicrotic notch detection (s): 0.0047 (0.0029) for ABP waveforms and 0.0046 (0.0029) for PPG waveforms, compared to 0.0693 (0.0770) for ABP and 0.0968 (0.0909) for PPG waveforms for the established 2nd derivative method. The algorithm has high accuracy of DN detection for SNR of >= -9 dB for ABP waveforms and >= -12 dB for PPG waveforms indicating robust performance in detecting the DN when it is less visibly distinct. Conclusion: Our proposed IEM- based algorithm can detect DN in both ABP and PPG waveforms with low computational cost, even in cases where it is not distinctly defined within a cardiac cycle of the waveform (DN-less signals). The algorithm can potentially serve as a valuable, fast, and reliable tool for extracting features from ABP and PPG waveforms. It can be especially beneficial in medical applications where DN-based features, such as SPD, diastolic phase duration, and DN amplitude, play a significant role.

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“…Several reviews on the application of PPG signals have been carried out. Some of them focus on the specific medical use of PPG like pulse rate [ 3 ], blood pressure [ 4 , 5 ], atrial fibrillation [ 6 , 7 ], circulatory monitoring [ 8 ], nociception [ 9 ], or on the specific placement of PPG [ 10 , 11 ], others instead focused on reviewing the way that the signal has been analyzed [ 7 , 12 , 13 , 14 , 15 , 16 ] or the type of the sensor [ 17 ]. More than a decade ago, J. Allen [ 2 ] published an interesting review about the applications PPG in clinical physiological measurement.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review

et al. 2022

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Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual’s quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.

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Is Heart Rate a Confounding Factor for Photoplethysmography Markers? A Systematic Review

Cited by 11 publications

References 52 publications

Intelligent physiological signal infosecurity: Case study in photoplethysmography (PPG) signal

Intelligent physiological signal infosecurity: Case study in photoplethysmography (PPG) signal

An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method

Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review

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