In-ear photoplethysmography for central pulse waveform analysis in non-invasive hemodynamic monitoring

Tigges, Timo; Rockstroh, Jonas; Pielmuş, Alexandru; Klum, Michael; Feldheiser, Aarne; Hunsicker, Oliver; Orglmeister, Reinhold

doi:10.1515/cdbme-2017-0122

Cited by 7 publications

(23 citation statements)

References 4 publications

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“…The models of Lognormal function have higher precision than the models of Gaussian function under the same model order, and the lognormal function is more suitable for ABP wave modeling. This conclusion is consistent with [21] and [22]. The two-sample ks-test results show that the parameters of all models are markedly different at a highly significant level (h = 1, p < 0.001, as shown in Table 6(a) to Table 7(b)).…”

Section: Discussionsupporting

confidence: 83%

“…Wang et al [20] suggested that four-or five-Gaussian models have maximum accuracy. Tigges et al [16], [21] proposed that we can obtain a model with an arbitrarily small error simply by increasing the number of the kernel function, while it will lead to overfitting of data and consequently to the physiologically uninterpretable solution. Recently, Liu et al [17], [22] demonstrated that morphological models with three-Gaussian and three-Lognormal functions are better than that of Raleigh and double-exponential functions for healthy subjects.…”

Section: Introductionmentioning

confidence: 99%

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The Optimal Morphological Model for Arterial Blood Pressure Wave Related Classification: Comparison of Two Types of Kernel Function Mixtures

2020

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The morphological modeling methods are efficient in quantifying the change of arterial blood pressure (ABP) waves. The related works focus on minimizing the modeling error but ignore the classification related modeling expression in practical applications. In this study, we explored the optimal modeling method for ABP wave related classifications. Two types of conventional models, Gaussian or Lognormal kernel function mixtures, were employed to quantitively describe the change of ABP signals, and the parameters of different models were engaged to train the different classifiers by probabilistic neural network (PNN) and random forest (RF) for identifying the ABP waves by age, gender, and whether belonging to extreme bradycardia (EB) or extreme tachycardia (ET). Then, we defined some indexes about the performance of modeling and classifications as the references to compare the different models. The ABP signals of Fantasia and 2015 PhysioNet/CinC Challenge databases were exploited as the experimental data to select the optimal model. The modeling results show that the Lognormal kernel function mixtures have a lower error in ABP wave modeling. The two-sample Kolmogorov-Smirnov test (ks-test) results indicate that the parameters of all models are markedly different at a highly significant level (h = 1, p < 0.05) between different groups. The classification results show that the classifiers based on the four-Gaussian function model have the best performance with the average Kappa coefficients (KC) of 99.160 ± 0.123%, while the average KC for the classifiers of two-Lognormal function models is 97.585 ± 0.172%, which means there is excessive information redundancy in the classifications by the three and four kernel functions models. Considering some other indexes such as time consumption and RAM space, the 2 Lognormal function model has more potential in practical applications. INDEX TERMS Arterial blood pressure signal, optimal morphological model, Gaussian function, lognormal function, classification.

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Section: Discussionsupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

The Optimal Morphological Model for Arterial Blood Pressure Wave Related Classification: Comparison of Two Types of Kernel Function Mixtures

2020

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“…As can be seen in table 1, there is a wide variety of suggested PWD algorithms in the literature that differ in the number and type of used kernels. Tigges et al (2017a) conducted a comparison of accuracy between several PWD algorithms and found an algorithm based on three Gamma kernels to achieve the best results. Huang et al (2015) examined the benefit of the usage of a mixture of Gamma and Gaussian kernels over pure Gaussian kernel-based algorithms and showed that the algorithm consisting of a Gamma kernel and three Gaussian kernels exhibited the best results in residual analysis.…”

Section: Background On Morphological Ppg Signal Analysis By Pwdmentioning

confidence: 99%

Pulse decomposition analysis in photoplethysmography imaging

et al. 2020

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Objective: Photoplethysmography imaging (PPGI) has gained immense attention over the last few years but only a few works have addressed morphological analysis so far. Pulse wave decomposition (PWD), i.e. the decomposition of a pulse wave by a varying number of kernels, allows for such analyses. This work investigates the applicability of PWD algorithms in the context of PPGI. Approach: We used simulated and experimental data to compare various PWD algorithms from the literature regarding their robustness against noise and motion artifacts while preserving morphological information as well as regarding their ability to reveal physiological changes by PPGI. Main results: Our experiments prove that algorithms that combine Gamma and Gaussian distributions outperform other choices. Further, algorithms with two kernels exhibit the highest robustness against noise and motion artifacts (improvement in N R M S E ‾ of 14.09 %) while preserving the morphology similarly to algorithms using more kernels. Lastly, we showed that PWD can reveal physiological changes upon distal stimuli by PPGI. Significance: This work proves the feasibility of pulse decomposition analysis in PPGI, particularly for algorithms with a low number of kernels, and opens up novel applications for PPGI. Not only for PPGI but for future research on PWD in general, our findings have importance as they elucidate differences between PWD algorithms and emphasize the importance of using initial values. To support such future research, we have released the algorithms and simulated data to the public.

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“…For the expression of the PDA model, there is a controversy with regard to the ideal number and type of kernel function. Tigges et al (2017aTigges et al ( , 2017b proposed that a model with an arbitrarily small error can be obtained simply by increasing the number of the kernel function, while it will lead to overfitting of data and consequently to a physiologically uninterpretable solution. Jiang et al (2018) demonstrated that the three Gaussian function model has better fitting accuracy and the obvious physiological implication after comparing four types of fitting functions, including Gaussian function, Raleigh function, double-exponential function and logarithmic normal function.…”

Section: Abp Wave Modeling and Parameter Estimationmentioning

confidence: 99%

A recognition method for extreme bradycardia by arterial blood pressure signal modeling with curve fitting

Chou

Zhang

et al. 2020

Physiol. Meas.

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Objective: The aim of this study is to investigate the potential of arterial blood pressure (ABP) signal for the detection of the subjects with life-threatening extreme bradycardia (EBr). Approach: The steps of the proposed method include ABP signal preprocessing, ABP wave segmentation, model parameter estimation, and EBr subject detection. First, the noise, interference and abnormal segments are eliminated in the pre-processing. Then, the ABP signal is segmented into a series of ABP waves by cardiac cycles. The pulse decomposition analysis (PDA) approach is presented to quantitively describe the changes in ABP waves. The back-propagation neural network, probabilistic neural network and decision tree (DT) are engaged to design the classifiers to discriminate the EBr subjects from healthy subjects by the parameters of PDA models. The international physiological signal databases of Fantasia for healthy subjects and 2015 PhysioNet/CinC Challenge for EBr subjects are exploited to validate the proposed method, and 79 310 ABP waves of healthy subjects and 4595 ABP waves of EBr subjects are extracted. Main results: We obtain the average PDA models of healthy subjects and EBr subjects and derive their changes. The two-sample Kolmogorov–Smirnov test result shows that all model parameters are markedly different (H= 1, P < 0.05) between the healthy and EBr subjects. The classification results show that the DT has the best performance with specificity of 99.74% ± 0.07%, sensitivity of 93.12% ± 1.24%, accuracy of 99.37% ± 0.10% and kappa coefficient of 93.92% ± 0.92%. Significance: The proposed method has the potential to detect EBr subjects by the ABP signal.

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In-ear photoplethysmography for central pulse waveform analysis in non-invasive hemodynamic monitoring

Cited by 7 publications

References 4 publications

The Optimal Morphological Model for Arterial Blood Pressure Wave Related Classification: Comparison of Two Types of Kernel Function Mixtures

The Optimal Morphological Model for Arterial Blood Pressure Wave Related Classification: Comparison of Two Types of Kernel Function Mixtures

Pulse decomposition analysis in photoplethysmography imaging

A recognition method for extreme bradycardia by arterial blood pressure signal modeling with curve fitting

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