Arterial pulse waveform analysis by the probability distribution of amplitude

Tsui, Po-Hsiang; Lin, Lian‐Yu; Chang, Chien-Chung; Hwang, Juey‐Jen; Lin, Jen-Jen; Chu, Chin‐Chou; Chen, Chiung-Nien; Chang, King‐Jen; Chang, Chien-Cheng

doi:10.1088/0967-3334/28/8/004

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…This study showed that TDB is a robust and stable parameter representing arterial stiffness from PPG measurement. Statistical parameters can also be used for estimating the augmentation index from the PPG signal (Tsui et al 2007). However, it is not real-time processing and therefore is not suitable for continuous real-time blood pressure monitoring.…”

Section: Waveform Analysis Of Ppgmentioning

confidence: 99%

Effect of confounding factors on blood pressure estimation using pulse arrival time

Kim

Baek

et al. 2008

Physiol. Meas.

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Two confounding factors were selected and analyzed in blood pressure estimation using pulse arrival time (PAT) for each individual. The heart rate was used as the confounding factor for the cardiac cycle, and the duration from the maximum derivative point to the dicrotic peak (TDB) in the photoplethysmogram was used as another confounding factor representing arterial stiffness. By considering these factors with PAT in multiple regression analysis, the performance of blood pressure estimation is enhanced significantly in the diastolic phase as well as in the systolic phase. The reproducibility of this method was also validated with formerly obtained regression equations from the training set. The correlation between estimated and measured blood pressure decreased a little, but the validity was still maintained (r congruent with 0.8). This shows the value of the method in non-intrusive blood pressure estimation for individual patients and may be useful for various applications.

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Section: Waveform Analysis Of Ppgmentioning

confidence: 99%

Effect of confounding factors on blood pressure estimation using pulse arrival time

Kim

Baek

et al. 2008

Physiol. Meas.

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“…The working principle of this algorithm was described by other authors (Tsui et al, 2007), relies on the PDF property of creating a local maximum for the amplitudes close to the inflection point that defines AIx.…”

Section: Probability Density Functionmentioning

confidence: 99%

SYNTHESIZED CARDIAC WAVEFORM IN THE EVALUATION OF AUGMENTATION INDEX ALGORITHMS - Case Study for a New Wavelet based Algorithm

Almeida¹,

Pereira²,

Borges³

et al. 2010

Proceedings of the Third International Conference on Bio-Inspired Systems and Signal Processing

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We developed and tested the performance of a new wavelet based algorithm for Augmentation Index (AIx) determination. The evaluation method relies on reference cardiac-like pulses that are synthesized using a weighted combination of exponentially shaped sub-pulses that represent the three main components of real pulses: the systolic stroke, its reflected replica and the carotid reservoir or windkessel effect. The pulses are parameterized so as to reproduce the main types of cardiac waveforms. The values of AIx yielded by the new algorithm are compared with the ones computed directly from the synthesized waveform and with the values produced by standard Probability Density Function (PDF) analysis.

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“…Therefore, quantitative methods are needed. Much effort is being spent on pulse analysis, such as the classification of pulse waveforms [1][2][3][4][5][6][7] and cardiovascular assistant diagnosis [8][9][10][11]. In pulse diagnosis, time-domain parameters can reflect the specificity of pulse signals.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Automatic Time-Domain Characteristic Parameters Extraction of Human Pulse Signals

Zhang

Wang

2007

EURASIP J. Adv. Signal Process.

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A methodology for the automated time-domain characteristic parameter extraction of human pulse signals is presented. Due to the subjectivity and fuzziness of pulse diagnosis, the quantitative methods are needed. Up to now, the characteristic parameters are mostly obtained by labeling manually and reading directly from the pulse signal, which is an obstacle to realize the automated pulse recognition. To extract the parameters of pulse signals automatically, the idea is to start with the detection of characteristic points of pulse signals based on wavelet transform, and then determine the number of pulse waves based on chain code to label the characteristics. The time-domain parameters, which are endowed with important physiological significance by specialists of traditional Chinese medicine (TCM), are computed based on the labeling result. The proposed methodology is testified by applying it to compute the parameters of five hundred pulse signal samples collected from clinic. The results are mostly in accord with the expertise, which indicate that the method we proposed is feasible and effective, and can extract the features of pulse signals accurately, which can be expected to facilitate the modernization of pulse diagnosis.

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Arterial pulse waveform analysis by the probability distribution of amplitude

Cited by 8 publications

References 15 publications

Effect of confounding factors on blood pressure estimation using pulse arrival time

Effect of confounding factors on blood pressure estimation using pulse arrival time

SYNTHESIZED CARDIAC WAVEFORM IN THE EVALUATION OF AUGMENTATION INDEX ALGORITHMS - Case Study for a New Wavelet based Algorithm

A Framework for Automatic Time-Domain Characteristic Parameters Extraction of Human Pulse Signals

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