An Assessment of Pulse Transit Time For Detecting Heavy Blood Loss During Surgical Operation

Wang, Chien-Hao; Lu, Cheng-Wei; Lin, T.; Abbod, Maysam F.; Shieh, Jiann-Shing

doi:10.2174/1874120701206010104

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…To avoid negative values, we added an offset value of 3.57, which made a sample mean of 5, to the generated values. The PTTs were then considered as square roots of the generated arterial compliance values based on (2). The histograms of the arterial compliance and the PTT with their skewness and kurtosis are shown in Fig.…”

Section: A Simulation Studymentioning

confidence: 99%

“…The ongoing research defines the PTT as various time intervals depending on the equipment used and applications: (a) the time interval between the R-wave of the electrocardiogram (ECG) and the following pulse peak of the photoplethysmogram (PPG) [1], [2]; (b) the time interval between the R-wave of the ECG and the pulse onset of the PPG [3]; (c) the time interval between the R-wave of the ECG and the partial amplitude (e.g., 25 % and 50 %) on the upslope of the PPG [4]; (d) the time interval between the R-wave of the ECG and either the maximum peak of the first or second derivative of the PPG [5]; and the time interval between pulse waveforms measured from one or two distinct locations [6], [7]. The PTT is mainly used not only for estimating both systolic and diastolic blood pressures continuously [1], [8], but also for calculating the pulse wave velocity (PWV), which is a measure of arterial stiffness [9], [10].…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we used the PTT measured as the time interval from the R-wave of the ECG to the following pulse peak of the PPG, within the same cardiac cycle (Fig. 1) [1], [2]. In spite of the wide applicability of the PTT, there is a lack of research on its estimation.…”

Section: Introductionmentioning

confidence: 99%

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A Gaussian Model-Based Probabilistic Approach for Pulse Transit Time Estimation

Jang

Park²,

Hahn

2016

IEEE J. Biomed. Health Inform.

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In this paper, we propose a new probabilistic approach to pulse transit time (PTT) estimation using a Gaussian distribution model. It is motivated basically by the hypothesis that PTTs normalized by RR intervals follow the Gaussian distribution. To verify the hypothesis, we demonstrate the effects of arterial compliance on the normalized PTTs using the Moens-Korteweg equation. Furthermore, we observe a Gaussian distribution of the normalized PTTs on real data. In order to estimate the PTT using the hypothesis, we first assumed that R-waves in the electrocardiogram (ECG) can be correctly identified. The R-waves limit searching ranges to detect pulse peaks in the photoplethysmogram (PPG) and to synchronize the results with cardiac beats--i.e., the peaks of the PPG are extracted within the corresponding RR interval of the ECG as pulse peak candidates. Their probabilities of being the actual pulse peak are then calculated using a Gaussian probability function. The parameters of the Gaussian function are automatically updated when a new pulse peak is identified. This update makes the probability function adaptive to variations of cardiac cycles. Finally, the pulse peak is identified as the candidate with the highest probability. The proposed approach is tested on a database where ECG and PPG waveforms are collected simultaneously during the submaximal bicycle ergometer exercise test. The results are promising, suggesting that the method provides a simple but more accurate PTT estimation in real applications.

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Section: A Simulation Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Gaussian Model-Based Probabilistic Approach for Pulse Transit Time Estimation

Jang

Park²,

Hahn

2016

IEEE J. Biomed. Health Inform.

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“…PTT and PWV are extensively used for determining physiological parameters such as blood pressure [14], left ventricular ejection time (LVET), left ventricular pre-ejection period (LVPEP) [15] and arterial stiffness [16]. PTT is also a good indicator for monitoring patient's blood loss during a surgical operation [17]. Hence peak detection has become very important in time domain analysis of PPG signal.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Automatic Systolic Peak Detection Algorithm for Photoplethysmographic Signals

Kuntamalla¹,

Reddy²

2014

IJCA

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Processing of physiological signals often involves detection of peaks and finding intervals between them. Well developed methods are available for Electrocardiogram(ECG) QRS complex detection. However, there are only a few algorithms published for peak detection suitable for pulse wave signals such as arterial pressure wave and photoplethysmographic (PPG) signals. Algorithms for detection of QRS complex in ECG are based on the impulsive character of the signal and are not applicable for pulse wave signals, which are more sinusoidal in nature and the shape varies with age. In this background, a versatile algorithm based on the physiology of the pulse wave is developed to detect the peaks from a pulse wave signal such as PPG. The algorithm combines the technique of moving average of valley-peak differences with an adaptive threshold filtering to detect the systolic peaks. The algorithm is validated against a publicly available validation dataset and achieved a sensitivity of 99.82 and a positive predictivity of 98.88 when compared to expert manual annotations. This algorithm is computationally simple and can be easily implemented in real time processing hardware.

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“…It is currently used as a prognostic prediction tool for cardiovascular disease [1, 2]. Because PTT is affected by blood pressure [3], its usefulness as an indicator of blood pressure fluctuations during cardiovascular and emergency surgical procedures has been reported [4]. Although PTT is measured for adults in many clinical circumstances, there are no reports regarding the use of PTT in fetus.…”

mentioning

confidence: 99%