Comparison of Heart Rate statistical parameters from Photoplethysmographic signal in resting and exercise conditions

Sengthipphany, T.; Tretriluxana, Suradej; Chitsakul, Kitiphol

doi:10.1109/ecticon.2015.7207074

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…Hence, the intensity of light allows for the detection of the cardiac cycle. The PPG signal is a pulse wave with a smooth behaviour, and is the result of a mechanical wave resulting from the mechanical action of blood in the vessels (Sengthipphany et al 2015). Since it is related with the pulsatile arterial blood, by detecting fiducial points in the waveform, it is possible to measure the time between consecutive pulses, known as pulse-to-pulse interval (PPI).…”

Section: Analysis Of Heart Rate Variability and Pulse Rate Variability Datamentioning

confidence: 99%

Pulse rate variability in cardiovascular health: a review on its applications and relationship with heart rate variability

et al. 2020

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Heart rate variability has been largely used for the assessment of cardiac autonomic activity, due to the direct relationship between cardiac rhythm and the activity of the sympathetic and parasympathetic nervous system. In recent years, another technique, pulse rate variability, has been used for assessing heart rate variability information from pulse wave signals, especially from photoplethysmography, a non-invasive, non-intrusive, optical technique that measures the blood volume in tissue. The relationship, however, between pulse rate variability and heart rate variability is not entirely understood, and the effects of cardiovascular changes in pulse rate variability have not been thoroughly elucidated. In this review, a comprehensive summary of the applications in which pulse rate variability has been used, with a special focus on cardiovascular health, and of the studies that have compared heart rate variability and pulse rate variability is presented. It was found that the relationship between heart rate variability and pulse rate variability is not entirely understood yet, and that pulse rate variability might be influenced not only due to technical aspects but also by physiological factors that might affect the measurements obtained from pulse-to-pulse time series extracted from pulse waves. Hence, pulse rate variability must not be considered as a valid surrogate of heart rate variability in all scenarios, and care must be taken when using pulse rate variability instead of heart rate variability. Specifically, the way pulse rate variability is affected by cardiovascular changes does not necessarily reflect the same information as heart rate variability, and might contain further valuable information. More research regarding the relationship between cardiovascular changes and pulse rate variability should be performed to evaluate if pulse rate variability might be useful for the assessment of not only cardiac autonomic activity but also for the analysis of mechanical and vascular autonomic responses to these changes.

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Section: Analysis Of Heart Rate Variability and Pulse Rate Variability Datamentioning

confidence: 99%

Pulse rate variability in cardiovascular health: a review on its applications and relationship with heart rate variability

et al. 2020

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“…Moreover, the variability was analyzed. A previous study revealed that the PPG and ECG exhibited a good positive correlation in the resting state, indicating that PPG signal can replace ECG signal in the non-exercise state for the variability analysis [ 19 , 20 ]. Comparison of the mean and standard deviation of NN intervals (SDNN) between RRI and PPI can be seen in Figure 10 of ref.…”

Section: Technical Backgroundmentioning

confidence: 99%

Drowsiness Detection System Based on PERCLOS and Facial Physiological Signal

Chang

Wang

Chen

et al. 2022

Sensors

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Accidents caused by fatigue occur frequently, and numerous scholars have devoted tremendous efforts to investigate methods to reduce accidents caused by fatigued driving. Accordingly, the assessment of the spirit status of the driver through the eyes blinking frequency and the measurement of physiological signals have emerged as effective methods. In this study, a drowsiness detection system is proposed to combine the detection of LF/HF ratio from heart rate variability (HRV) of photoplethysmographic imaging (PPGI) and percentage of eyelid closure over the pupil over time (PERCLOS), and to utilize the advantages of both methods to improve the accuracy and robustness of drowsiness detection. The proposed algorithm performs three functions, including LF/HF ratio from HRV status judgment, eye state detection, and drowsiness judgment. In addition, this study utilized a near-infrared webcam to obtain a facial image to achieve non-contact measurement, alleviate the inconvenience of using a contact wearable device, and for use in a dark environment. Furthermore, we selected the appropriate RGB channel under different light sources to obtain LF/HF ratio from HRV of PPGI. The main drowsiness judgment basis of the proposed drowsiness detection system is the use of algorithm to obtain sympathetic/parasympathetic nervous balance index and percentage of eyelid closure. In the experiment, there are 10 awake samples and 30 sleepy samples. The sensitivity is 88.9%, the specificity is 93.5%, the positive predictive value is 80%, and the system accuracy is 92.5%. In addition, an electroencephalography signal was used as a contrast to validate the reliability of the proposed method.

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“…However, it has a disadvantage that its attachment restricts movement of a person. The existing researches suggested photoplethysmography as an alternative approach [16] [17]. It measures the fluctuation of the blood flow in the artery and the capillary by contraction of the heart.…”

Section: The Vital Signs and Emotionmentioning

confidence: 99%

Estimation of Intimacy Change in Team Using Vital Signs

Hattori¹,

Tanaka²,

Kajiwara

et al. 2018

Annals of Computer Science and Information Systems

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In this research, we propose a method using vital signs, to estimate changes of the intimacy inside a team from interactions of team members in the same space. The method estimates both intimacy change between two members and that among whole members. The method facilitates team leaders to grasp the relationships and improve the team performance. Since various measurements representing features of the pulse wave are known to reflect personal emotion, we can expect to estimate the change of intimacy, providing the measurements with a machine learning algorithm. An experiment evaluating the proposed method showed high accuracy in the estimation among all members, but low accuracy in the estimation between two members. In both cases, the accuracy can be improved by choice of effective measurements. Through this experiment, we have found it is necessary to decide the effective measurements for each team to construct a model to estimate intimacy inside the team.

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Comparison of Heart Rate statistical parameters from Photoplethysmographic signal in resting and exercise conditions

Cited by 6 publications

References 4 publications

Pulse rate variability in cardiovascular health: a review on its applications and relationship with heart rate variability

Pulse rate variability in cardiovascular health: a review on its applications and relationship with heart rate variability

Drowsiness Detection System Based on PERCLOS and Facial Physiological Signal

Estimation of Intimacy Change in Team Using Vital Signs

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