Essential Feature Extraction of Photoplethysmography Signal of Men and Women in Their 20s

Djawad, Yasser Abd; Mu’nisa, Andi; Rusung, Pangayoman; Kurniawan, Abdi; Idris, Irma Suryani; Taiyeb, Mushawwir

doi:10.4186/ej.2017.21.4.259

Cited by 13 publications

(5 citation statements)

References 16 publications

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“…After decryption, the low‐pass filter with cutoff frequency of 10 Hz is used to filter the decrypted PPG signal to remove the unwanted high‐frequency noises and to strengthen the pulsatile components of PPG signal. The decrypted PPG signals require to have the reliable and lossless performance for further time‐domain PPI , HR , and RI analyses for evaluating diabetic neuropathy, myocardial dysfunction, CVD, and cardiac rhythm [43, 44]. HR is the number of heartbeat in a minute (beats per minute, bpm) and can be estimated by

HR = \frac{f_{s}}{| peak (t + 1) - peak (t) |} \times 60

where the term |peak( t + 1) – peak( t )| is the interval between two peaks (PPI), as shown in Figure 3, t = 1, 2, 3, …, N p – 1, N p is the peak number, which indicates a cardiac beat‐to‐beat interval of PPG signal, and f s is the sampling rate ( f s ≈ 1.04).…”

Section: Methodsmentioning

confidence: 99%

“…After decryption, the low-pass filter with cutoff frequency of 10 Hz is used to filter the decrypted PPG signal to remove the unwanted high-frequency noises and to strengthen the pulsatile components of PPG signal. The decrypted PPG signals require to have the reliable and lossless performance for further timedomain PPI, HR, and RI analyses for evaluating diabetic neuropathy, myocardial dysfunction, CVD, and cardiac rhythm [43,44]. HR is the number of heartbeat in a minute (beats per minute, bpm) and can be estimated by…”

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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HR = \frac{f_{s}}{| peak (t + 1) - peak (t) |} \times 60

Section: Methodsmentioning

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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“…(6) Detrended Fluctuation Analysis (DFA) Detrended Fluctuation Analysis is a time-domain analysis of a signal. DFA is a statistical method to compute correlations that have a long range in a time series [86]. An integrated time series can be calculated by the following formula:…”

Section: Mathematical Details Of Ppg Featuresmentioning

confidence: 99%

Noninvasive Blood Glucose Monitoring Systems Using Near-Infrared Technology—A Review

Hina

Saadeh

2022

Sensors

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The past few decades have seen ongoing development of continuous glucose monitoring (CGM) systems that are noninvasive and accurately measure blood glucose levels. The conventional finger-prick method, though accurate, is not feasible for use multiple times a day, as it is painful and test strips are expensive. Although minimally invasive and noninvasive CGM systems have been introduced into the market, they are expensive and require finger-prick calibrations. As the diabetes trend is high in low- and middle-income countries, a cost-effective and easy-to-use noninvasive glucose monitoring device is the need of the hour. This review paper briefly discusses the noninvasive glucose measuring technologies and their related research work. The technologies discussed are optical, transdermal, and enzymatic. The paper focuses on Near Infrared (NIR) technology and NIR Photoplethysmography (PPG) for blood glucose prediction. Feature extraction from PPG signals and glucose prediction with machine learning methods are discussed. The review concludes with key points and insights for future development of PPG NIR-based blood glucose monitoring systems.

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“…Recent research studies have shown the applications of PPG analyzed in the frequency domain for blood pressure estimation and gender identification implying that it might be useful to analyze PPG in frequency domain [42,43]. The frequency range of PPG signals is low and hence we focus only on 0-5 Hz range.…”

Section: Spectrogram Deep Learning Featuresmentioning

confidence: 99%

On Assessing Driver Awareness of Situational Criticalities: Multi-modal Bio-Sensing and Vision-Based Analysis, Evaluations, and Insights

Siddharth

Trivedi

2020

Brain Sciences

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Automobiles for our roadways are increasingly utilizing advanced driver assistance systems. These changes require us to develop novel perception systems not only for accurately understanding the situations and context of these vehicles, but also to understand the awareness of the driver in differentiating between safe and critical situations. The research presented in this paper focused on this specific problem. Even after the development of wearable and compact multi-modal bio-sensing systems in recent years, their application in driver awareness context has been scarcely explored. The capability of simultaneously recording different kinds of bio-sensing data in addition to traditionally used computer vision systems provide exciting opportunities to explore the limitations of these modalities. In this work, we explore the applications of three different bio-sensing modalities namely electroencephalogram (EEG), photoplethysmogram (PPG) and galvanic skin response (GSR) along with a camera-based vision system in driver awareness context. We assess the information from these sensors independently and together using both signal processing and deep learning tools. We show that our methods outperform previously reported studies in the context of monitoring driver awareness and detecting hazardous/non-hazardous situations for short time scales of two-seconds. We verify our methods by collecting user data on twelve subjects for two real-world driving datasets among which one is publicly available (KITTI dataset) and one that we collect ourselves (LISA dataset) with the vehicle being driven in an autonomous mode. This work presents an exhaustive evaluation of multiple sensor modalities on two different datasets for attention monitoring and hazardous events classification.

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Essential Feature Extraction of Photoplethysmography Signal of Men and Women in Their 20s

Cited by 13 publications

References 16 publications

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

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

Noninvasive Blood Glucose Monitoring Systems Using Near-Infrared Technology—A Review

On Assessing Driver Awareness of Situational Criticalities: Multi-modal Bio-Sensing and Vision-Based Analysis, Evaluations, and Insights

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