Pulse arrival time (PAT) measurement based on arm ECG and finger PPG signals - comparison of PPG feature detection methods for PAT calculation

Rajala, Satu; Ahmaniemi, Teemu; Lindholm, Harri; Taipalus, Tapio

doi:10.1109/embc.2017.8036809

Cited by 25 publications

(19 citation statements)

References 12 publications

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“…Compared to the ECG signal, the PPG waveform is smooth and it is not characterized by a clearly detectable feature (Rajala et al, 2017). Hence, an important first step for PRV analysis is the accurate detection of the PPG pulse wave and pulse periods (PP).…”

Section: Introductionmentioning

confidence: 99%

Optimal fiducial points for pulse rate variability analysis from forehead and finger photoplethysmographic signals

et al. 2019

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Objective: The aim of this work is to evaluate and compare five fiducial points for the temporal location of each pulse wave from forehead and finger photoplethysmographic pulse waves signals (PPG) to perform pulse rate variability (PRV) analysis as a surrogate of heart rate variability (HRV) analysis.Approach: Forehead and finger PPG signals were recorded during tilt-table test simultaneously to the ECG. Artifacts were detected and removed and, five fiducial points were computed: apex, middle-amplitude and foot points of the PPG signal, apex point of the first derivative signal and, the intersection point of the tangent to the PPG waveform at the apex of the derivative PPG signal and the tangent to the foot of the PPG pulse defined as intersecting tangents method. Pulse period (PP) time intervals series were obtained from both PPG signals and compared to the RR intervals obtained from the ECG. Heart and pulse rate variability signals (HRV and PRV) were estimated and, classical time and frequency domain indices were computed.Main Results: The middle-amplitude point of the PPG signal (n M ), the apex point of the first derivative (n * A ), and the tangents intersection point (n T ) are the most suitable fiducial points for PRV analysis, which result in the lowest relative errors estimated between PRV and HRV indices, higher correlation coefficients and reliability indexes. Statistically significant differences according to the Wilcoxon test between PRV and HRV signals were found for the apex and foot fiducial points of the PPG, as well as the lowest agreement between RR and PP series according to Bland-Altman analysis. Hence, they have been considered less accurate for variability analysis. In addition, the relative errors are significantly lower for n M and n * A features by using Friedman statistics with Bonferroni multiple-comparison test and, we propose n M as the most accurate fiducial point. Based on our results, forehead PPG seems to provide more reliable information for a PRV assessment than finger PPG.Significance: The accuracy of the pulse wave detections depends on the morphology of the PPG. There is therefore a need to widely define the most accurate fiducial point to perform a PRV analysis under non-stationary conditions based on different PPG sensor locations and signal acquisition techniques. 2

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Section: Introductionmentioning

confidence: 99%

Optimal fiducial points for pulse rate variability analysis from forehead and finger photoplethysmographic signals

et al. 2019

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“…The ECG and PPG signals were filtered with a high pass filter of 0.5 Hz cut-off frequency to remove the baseline and later filtered with 60 Hz notch filter to remove the power line noise [11]. Since PPG signals are significantly vulnerable to physiological (e.g., respiration, head tilt) and measurement (e.g., motion) oriented artifacts [15], [16], PPG signals were later filtered again with a low pass filter of cut off frequency of 10 Hz [17].…”

Section: Discussionmentioning

confidence: 99%

“…Though PPG foot is the conventional and mostly used point for PAT calculation [22], [23], there are studies which used other points for PAT calculation as well. For example, Chua and Heneghan used ECG R-peak and the point with the highest gradient on the upward edge of the PPG for PAT calculation [24], Baek et al used PPG first derivative [25], Rajala et al used PPG peak, foot, first and second derivative [11], PPG peak, foot, 20% height, and the maximum first derivative was used by another group [26]. As stated earlier, we tried to use three known measures for PAT calculation, but in this study, we only focused on PPG peaks derived PAT because of its easy detection and less intersubject variability.…”

Section: Discussionmentioning

confidence: 99%

“…Photoplethysmogram (PPG) is another important physiological measure, which can detect the changes in peripheral blood volume using a non-invasive technique [10]. Usually, PPG is used in many clinical measurements like blood oxygen saturation [11], but recently also used for drowsiness detection purposes [12], [13]. Several critical physiological features can be easily obtained via ECG and PPG or the combination of two.…”

Section: Introductionmentioning

confidence: 99%

“…For example, these two signals can be used to calculate pulse arrival time (PAT) measures. PAT is measured as the time delay between the ECG R-wave and a distal arterial waveform [11], [14]. In this study of pilot's drowsiness detection, we are focusing on the PAT's behavioral changes during drowsy and alert periods.…”

Section: Introductionmentioning

confidence: 99%

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Using Photoplethysmography Based Features As Indicators of Drowsiness: Preliminary Results

Majumder

Verma

Wang

et al. 2019

2019 Design of Medical Devices Conference

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Drowsiness, a transitional psychological state between alertness and sleepiness, is one of the leading reasons for the increased risk of accidents, particularly in commercial aviation. Though electroencephalography (EEG) is widely used for drowsiness assessment, it is impractical for unobtrusive airborne monitoring due to discomfort induced by the number of electrodes touching the scalp. In this work, we investigated the potential of pulse arrival time (PAT), and photoplethysmogram (PPG) features as indicators of drowsiness. Simultaneous vertical electrooculogram (EOG), ear PPG, and electrocardiogram (ECG) were recorded from 18 commercially-rated pilots from 02:00 AM to 04:30 AM. ECG R-peaks and PPG peaks were detected and used for calculation of PAT and heart rate to observe their changes during drowsiness. During the drowsy periods, the PAT and RR intervals increased (i.e., decrease in heart rate) significantly (p < 0.05) compared to baseline. Features based on only PPG also showed a significant increase during drowsiness. However, decrease in PAT/RR during drowsiness for most of the subjects indicates that increase in PAT is not linearly correlated to RR interval and might be reflecting changes in arterial stiffness. Hence, PAT and derived PPG based features with continuous heart rate monitoring can serve as useful indicators of early drowsiness detection.

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Machine Learned Pulse Transit Time (MLPTT) Measurements from Photoplethysmography

Mehrgardt

Khushi

Withana

et al. 2020

Neural Information Processing

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Pulse transit time (PTT) provides a cuffless method to measure and predict blood pressure, which is essential in long term cardiac activity monitoring. Photoplethysmography (PPG) sensors provide a low-cost and wearable approach to obtain PTT measurements. The current approach to calculating PTT relies on quasi-periodic pulse event extractions based on PPG local signal characteristics. However, due to inherent noise in PPG, especially at uncontrolled settings, this approach leads to significant errors and even missing potential pulse events. In this paper, we propose a novel approach where global features (all samples) of the time-series data are used to develop a machine learning model to extract local pulse events. Specifically, we contribute 1) a new noise resilient machine learning model to extract events from PPG and 2) results from a study showing accuracy over state of the art (e.g. HeartPy) and 3) we show that MLPTT outperforms HeartPy peak detection, especially for noisy photoplethysmography data.

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Pulse arrival time (PAT) measurement based on arm ECG and finger PPG signals - comparison of PPG feature detection methods for PAT calculation

Cited by 25 publications

References 12 publications

Optimal fiducial points for pulse rate variability analysis from forehead and finger photoplethysmographic signals

Optimal fiducial points for pulse rate variability analysis from forehead and finger photoplethysmographic signals

Using Photoplethysmography Based Features As Indicators of Drowsiness: Preliminary Results

Machine Learned Pulse Transit Time (MLPTT) Measurements from Photoplethysmography

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