# A novel particle filtering method for estimation of pulse pressure variation during spontaneous breathing

- Sunghan Kim
^{1}Email authorView ORCID ID profile, - Fouzia Noor
^{1}, - Mateo Aboy
^{2}and - James McNames
^{3}

**Received: **17 February 2016

**Accepted: **24 July 2016

**Published: **11 August 2016

## Abstract

### Background

We describe the first automatic algorithm designed to estimate the pulse pressure variation (\(\text {PPV}\)) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly available algorithms to automatically estimate \(\text {PPV}\) accurately and reliably in mechanically ventilated subjects, at the moment there is no automatic algorithm for estimating \(\text {PPV}\) on spontaneously breathing subjects. The algorithm utilizes our recently developed sequential Monte Carlo method (SMCM), which is called a maximum *a-posteriori* adaptive marginalized particle filter (MAM-PF). We report the performance assessment results of the proposed algorithm on real ABP signals from spontaneously breathing subjects.

### Results

Our assessment results indicate good agreement between the automatically estimated \(\text {PPV}\) and the gold standard \(\text {PPV}\) obtained with manual annotations. All of the automatically estimated \(\text {PPV}\) index measurements (\(\text {PPV}_{\text {auto}}\)) were in agreement with manual gold standard measurements (\(\text {PPV}_{\text {manu}}\)) within ±4 % accuracy.

### Conclusion

The proposed automatic algorithm is able to give reliable estimations of \(\text {PPV}\) given ABP signals alone during spontaneous breathing.

### Keywords

Extended Kalman filter*a-posteriori*distribution Maximum

*a-posteriori*estimation Marginalized particle filter Multi-harmonic signal

## Background

Excessive blood loss due to severe medical conditions can result in insufficient tissue perfusion, which can lead to organ failure. Clinicians need to plan the course of fluid therapy carefully in order to maintain tissue perfusion [1–3]. However, individuals’ responsiveness to fluid therapy varies significantly and there are few clinical signs for clinicians to rely on to predict the fluid responsiveness.

Heart-lung interactions differ substantially between spontaneous breathing and mechanical ventilation. While mechanical inspiration decreases right ventricular filling and increases right ventricular afterload, spontaneous inspiration increases both right ventricular filling and afterload. Also, intrathoracic pressure oscilations during spontaneous breathing are insufficient and irregular and respiratory induced variables are not sensitive enough to evaluate the preload dependency [2, 9]. Due to this uncertainty of the usefulness of dynamic variables during spontaneous breathing, the clinical usage of dynamic variables is currently limited to predicting the fluid responsiveness of mechanically ventilated patients [10]. However, recent studies suggest that accurate prediction of the fluid responsiveness may have potential for those who are not under full mechanical ventilation support. For instance, Hong et al. [11] demonstrated that \(\text {PPV}\) is of use in predicting the fluid responsiveness during forced spontaneous breathing. Forced spontaneous breathing is a special breathing exercise, which involves deep inspiration and slow passive expiration. Another study proposed the use of Dynamic Arterial Elastance (Eadyn), which is the ratio between \(\text {PPV}\) and \(\text {SVV}\) during a single respiratory cycle, to predict the arterial blood pressure response to a fluid challenge during post-surgical recovery periods [12]. In one porcine study, pigs breathed spontaneously into the inspiratory and expiratory threshold resistors separately or combined under three volemic conditions: hypo-, hyper-, and normo-volemic [13]. The study result indicated that expiratory resistor could be used to predict the fluid responsiveness of spontaneously breathing subjects. Hoff et al. [10] investigated the ability of respiratory variations in \(\text {PPV}\) to reflect hypovolemia during noninvasive positive pressure ventilation (NPPV). They induced central hypovolemia with progressive lower body negative pressure. Their results clearly indicated that \(\text {PPV}\) is associated with volume status during NPPV.

The objectives of this paper are to introduce a new algorithm for automatic estimation of \(\text {PPV}\) given arterial blood pressure (\(\text {ABP}\)) signals alone during spontaneous breathing and to assess its performance on real \(\text {ABP}\) signals from the Massachusetts General Hospital Waveform Database (MGHDB) [14] available on PhysioNet [15]. It should be noted that our previous work in [8] introduced an algorithm for automatic \(\text {PPV}\) estimation for mechanically ventilated patients as opposed to the present work which is for spontaneously breathing patients.

## Methods: algorithm description

The subsequent sections explain a novel statistical signal model for ABP signals recorded from spontaneously breathing subjects and the \(\text {PPV}\) index tracking algorithm utilizing our recently developed sequential Monte Carlo estimation method.

### Notation

We have adopted the notation used in [16] with minor modifications. We use boldface to denote random processes, normal face for deterministic parameters and functions, upper case letters for matrices, lower case letters for vectors and scalars, superscripts in parenthesis for particle indices, upper-case superscripts for nonlinear/linear indication, and subscripts for time indices. For example, the nonlinear portion of the state vector for the \(i{\text {th}}\) state trajectory (i.e., particle) is denoted as \(\varvec{x}_{n}^{\mathrm {N},(i)}\) where *n* represents the discrete time index and (*i*) denotes the \(i{\text {th}}\) particle. The unnormalized particle weights are denoted as \(\tilde{w}^{(i)}\) and the normalized particle weights as \(w^{(i)}\). The state trajectories before resampling are denoted as \(\tilde{\varvec{x}}^{(i)}_n\) and as \(\varvec{x}^{(i)}_n\) after resampling.

### State-space model

*process model*, (3) a

*measurement model*, \(f\left( \cdot \right) \) and \(h\left( \cdot \right) \) functions of the state \(\varvec{x}_{n}\), and \(\varvec{u}_{n}\) and \(\varvec{v}_{n}\) uncorrelated white noises with variances

*q*and

*r*. A designer needs to incorporate prior domain knowledge of a system into the state-space model and define the functions \(f\left( \cdot \right) \) and \(h\left( \cdot \right) \). The flexibility and versatility of the state-space method are attributable to two functions, which can be either linear or nonlinear.

#### Measurement model

*r*, and \(\varvec{r}_{\cdot ,k,n}, \varvec{m}_{\cdot ,k,j,n}, \varvec{c}_{\cdot ,k,n}\) the sinusoidal coefficients. This measurement model was first introduced in [17].

#### Process model

### Maximum *A-posteriori* marginalized PF

The proposed automated \(\text {PPV}\) index estimation method requires accurate estimates of the instantaneous respiratory frequency \(\varvec{f}^{\text {r}}_{n}\), the instantaneous cardiac frequency \(\varvec{f}^{\text {c}}_{n}\), and the morphology of an ABP signal. In order to obtain those estimates, we utilize our recently developed particle filter technique, which is called the maximum *a-posteriori* adaptive marginalized particle filter (MAM-PF). The MAM-PF is a hybrid version of the marginalized particle filter (MPF) and maximum *a-posteriori* particle filter (MAP-PF), which leverages the advantages of the MPF and MAP-PF. In [18] we described the recursions for the MAM-PF in detail. We proposed two versions of the MAM-PF: optimal and fast MAM-PFs [18]. Within the state-space method framework, the Optimal MAM-PF computes the “optimal” trajectory of the state \(\varvec{x}_{n}\). However, its computational burden is too demanding to be practically useful. The fast MAM-PF is an approximation of the optimal MAM-PF, which requires dramatically less computational burden. However, the fast MAM-PF performs as well as the optimal MAM-PF, which we demonstrated in [8]. Recently, we proposed an automatic (\(\text {PPV}\)) estimation technique in mechanically ventilated patients by utilizing the fast MAM-PF as an ABP signal tracker [8]. Under full mechanical support, the respiratory rate of subjects is equal to the mechanical ventilation rate, which is known and constant. Therefore, the fast MAM-PF has to track only the instantaneous cardiac frequency \(\varvec{f}^{\text {c}}_{n}\) along with the signal morphology.

All ABP signals included in this study were recorded from spontaneously breathing subjects. Therefore, the ABP signal tracker has to track both the instantaneous respiratory frequency \(\varvec{f}^{\text {r}}_{n}\) and the instantaneous cardiac frequency \(\varvec{f}^{\text {c}}_{n}\) along with the signal morphology. Although the fast MAM-PF based ABP signal tracker is capable of tracking multiple frequencies, there are two major issues in using the fast MAM-PF algorithm as the ABP signal tracker for ABP signals of spontaneously breathing subjects. The first issue is that the morphology of the signal, which is represented by the sinusoidal coefficients in (6, 7), does not belong to the linear state any more. Since the modulating signal \(\varvec{\rho }_{k,n}\) is multiplied to the cardiac signal \(\varvec{\kappa }_{k,n}\), their sinusoidal coefficients \(\varvec{c}_{\cdot ,k,n}\) and \(\varvec{m}_{\cdot ,k,j,n}\) are nonlinear parameters of the measurement model in (4). The fast MAM-PF is applicable only to state-space models whose state vector can be partitioned into the linear and nonlinear portions. The second issue is that as the dimension of the state, where particle filters are used, increases the number of necessary particles to cover the state increases exponentially. As a result, the computational burden of the fast MAM-PF increases exponentially. The portion of the state space where particle filters are used is called the *particle space*. Since the new ABP signal tracker has to estimate both the instantaneous respiratory frequency \(\varvec{f}^{\text {r}}_{n}\) and the instantaneous cardiac frequency \(\varvec{f}^{\text {c}}_{n}\), the dimension of the particle state becomes 2, which results in a quadruple increase of computational burden if the fast MAM-PF has to be used for the current application. In order to address these two major issues we propose a new ABP signal tracker, which is a modified version of the Fast MAM-PF. It is called, the *Dual MAM-PF*. The term “Dual” is borrowed from Dual Kalman filters, in which the state is divided into two portions and each portion is estimated separately assuming that the other portion is known and equal to the currently estimated value. While the fast MAM-PF treats a two-dimensional particle space as a whole, the dual MAM-PF partitions the two-dimensional particle space into two one-dimensional particle spaces assuming independence between two particle space variables, which are the instantaneous respiratory frequency \(\varvec{f}^{\text {r}}_{n}\) and the instantaneous cardiac frequency \(\varvec{f}^{\text {c}}_{n}\).

*j*the particle state variable index, and \(i_{j}\) the particle index of the \(j{\text {th}}\) particle state variable. The total number of particle used in the dual MAM-PF algorithm is \(2 N_{\mathrm {p}}\) instead of \(N_{\mathrm {p}}^{2}\). At each time step

*n*the dual MAM-PF searches for the best trajectory of each particle \(i_{j}\) from the previous trajectory. This searching step can be written as,

### ABP signal envelope estimation

*x*for which

*f*(

*x*) attains its maximum and minimum values, respectively. The top plot in Fig. 1 shows a five respiratory cycle period of an ABP signal \(\varvec{y}_{n}\) (thick red), its estimate \(\hat{\varvec{y}}_{n}\) (thin green), and its estimated envelopes \(\varvec{e}_{\mu ,n}\) and \(\varvec{e}_{\ell ,n}\) (blue), which are described in (32) and (33).

### Pulse pressure signal envelope estimation

### Pulse pressure variation calculation

Figure 2 illustrates an example of the automatically computed continuous \(\text {PPV}\) index (thick green) and the manually obtained discrete \(\text {PPV}\) index (thin red) of a real 10 min ABP signal. Each hollow white dot represents a “discrete” \(\text {PPV}\) index, which can be obtained once per each respiratory cycle.

The subsequent sections describe how to assess the accuracy of the proposed \(\text {PPV}\) index tracking algorithm.

## Methods: algorithm assessment

### Assessment data

Summary of user-specified design parameters for the \(\text {PPV}\) index tracker

Name | Symbol | Value |
---|---|---|

No. particles | \(2 N^{\mathrm {p}}\) | 500 |

No. cardiac components | \(N^{\text {c}}\) | 10 |

No. respiratory components | \(N^{\text {r}}\) | 3 |

Minimum respiratory rate | \(f^{\text {r}}_{\min }\) | 6/60 Hz |

Maximum respiratory rate | \(f^{\text {r}}_{\max }\) | 30/60 Hz |

Minimum heart rate | \(f^{\text {c}}_{\min }\) | 50/60 Hz |

Maximum heart rate | \(f^{\text {c}}_{\max }\) | 140/60 Hz |

Measurement noise variance |
| \({{\mathrm{var}}}(y)\)/1e3 |

Respiratory frequency variance | \(q_{f^{\text {r}}}\) | 1e−6 \(T_s\) |

Cardiac frequency variance | \(q_{f^{\text {c}}}\) | 1e−6 \(T_s\) |

Respiratory amplitude variance | \(q_{a}, q_{b}\) | \({{\mathrm{var}}}(y)\)1e−6\(T_s\) |

Modulation factor amplitude variance | \(q_{c}, q_{d}\) | \({{\mathrm{var}}}(y)\)1e−8\(T_s\) |

Cardiac amplitude variance | \(q_{e}, q_{f}\) | \({{\mathrm{var}}}(y)\)1e−6\(T_s\) |

Initial respiratory amplitude | \(u_{a}, u_{b}\) | \({{\mathrm{std}}}(y)/1e1\) |

Initial modulation factor amplitude | \(u_{c}, u_{d}\) | \({{\mathrm{std}}}(y)/1e3\) |

Initial cardiac amplitude | \(u_{e}, u_{f}\) | \({{\mathrm{std}}}(y)/1e1\) |

### Manual PPV annotations (current standard)

We manually annotated the peaks and troughs of the ABP signals and calculated the \(\text {PPV}\) indices (current standard) as defined in (1). They are referred to as manual \(\text {PPV}\) indices \(\text {PPV}_{\text {manu}}\). \(\text {PPV}_{\text {auto}}\) represents \(\text {PPV}\) indices obtained using the proposed \(\text {PPV}\) index tracking algorithm.

### Statistical analysis

The statistical analysis used five \(\text {PPV}\) index measurements for each subject, and each measurement was separated by 2 min. Each \(\text {PPV}\) index measurement is an averaged value over 5 respiratory cycles. Figure 2 shows the 2 min apart measurement periods. The proposed \(\text {PPV}\) index tracking algorithm was assessed by calculating the agreement (mean ± standard deviation) between \(\text {PPV}_{\text {auto}}\) and \(\text {PPV}_{\text {manu}}\) measurements and using Bland–Altman analysis.

A Bland-Altman plot is a statistical and visualization method that is often used in the assessment of \(\text {PPV}\) estimation algorithms in order to determine the agreement between two different \(\text {PPV}\) estimates. It has the difference \(\Delta \text {PPV}\) between \(\text {PPV}_{\text {auto}}\) and \(\text {PPV}_{\text {manu}}\) on the y-axis and the \(\text {PPV}_{\text {manu}}\) on the x-axis. It visualizes the overall accuracy of estimation and estimation bias or trend versus \(\text {PPV}_{\text {manu}}\). We used it to compare the current standard using manual annotations with our automatic estimation algorithm.

## Results

Figure 4 depicts the Bland–Altman plot of the 11 subjects. There are 5 \(\text {PPV}\) measurements available per each subject. All of \(\text {PPV}_{\text {auto}}\) measurements were in agreement with \(\text {PPV}_{\text {manu}}\) measurements within ±3.5 % accuracy.

Summary of the mean and standard deviation of the \(\text {PPV}_{\text {manu}}\) and \(\text {PPV}_{\text {auto}}\) measurements

Subject | \(\text {PPV}_{\text {manu}}\) (%) | \(\text {PPV}_{\text {auto}}\) (%) |
---|---|---|

1 (mgh003) | 9.8 ± 1.0 | 8.9 ± 1.0 |

2 (mgh007) | 32.9 ± 1.5 | 33.9 ± 2.3 |

3 (mgh011) | 10.3 ± 1.0 | 10.6 ± 0.8 |

4 (mgh091) | 4.6 ± 0.3 | 3.3 ± 0.3 |

5 (mgh092) | 10.3 ± 1.1 | 9.2 ± 1.3 |

6 (mgh151) | 12.9 ± 2.7 | 11.3 ± 2.0 |

7 (mgh152) | 5.9 ± 0.5 | 6.1 ± 0.5 |

8 (mgh158) | 12.5 ± 0.8 | 10.3 ± 1.5 |

9 (mgh164) | 7.6 ± 1.0 | 6.7 ± 0.9 |

10 (mgh169) | 4.8 ± 0.5 | 5.3 ± 1.3 |

11 (mgh183) | 6.5 ± 0.8 | 6.5 ± 0.6 |

## Discussion

### Frequency clipping function

### Algorithm’s advantages

The proposed algorithm is the first automatic method described in the literature especially designed to estimate and track the \(\text {PPV}\) index in situations involving spontaneous breathing. It is important to note that the proposed algorithm is a complete new design from our previously described algorithm [20] which only worked for mechanically ventilated subjects. Our previous algorithm was made publicly available by the authors and due to its performance has been adopted by Philips Medical Systems. Currently, our previously published PPV algorithm is displayed in real-time on the Philips Intelliveu MP70 monitors (Intellivue MP70, Philips Medical Systems) and has been used in numerous studies related to PPV and fluid responsiveness. Its ability to monitor fluid responsiveness in the operating room and its accuracy against the current standard obtained by manual annotations were assessed by Cannesson [21]. Previously it was not possible to conveniently monitor the \(\text {PPV}\) index in the operating room or in the intensive care unit because it had to be manually calculated. Thus, the automatic PPV has potential clinical application for fluid management optimization in the operating room.

The major algorithm design difference of the proposed algorithm with respect to previously published algorithms [20, 22] is the fact that the proposed method is based on a statistical state-space capable of modeling spontaneous breathing, and estimation of the cardiovascular pressure signal based on this statistical model using optimal estimation methods. The state-space modeling stage results in an algorithm that is more robust to hemodynamic changes and artifacts. The statistical state-space signal model and associated model parameter estimation algorithm automatically filter out noise and artifact that cannot be captured with the model. Since the statistical signal model is based on cardiovascular physiology and pathophysiology, signal features that are not physiological in nature are automatically filtered out. Additionally, the model is general enough to accurately model both arterial blood pressure signals and plethysmogram signals. Consequently, it can also be used to calculate the pleth variability index (PVI).

Figures 5 and 6 exemplify a case where signal features that are not physiological in nature are automatically filtered out resulting in more accurate \(\text {PPV}\) index estimation than manual annotation. The top plot in Fig. 5 illustrates 4 respiratory cycles of the ABP signal (red) and its estimate (green). It also shows the manually annotated signal envelopes (black) and the automated computed signal envelopes (light blue). The bottom plot in Fig. 5 depicts the \(\text {PPV}_{\text {manu}}\) and \(\text {PPV}_{\text {auto}}\) over the same period. Around \(535\,\mathrm{s}\), the \(\text {PPV}_{\text {manu}}\) value (red) abruptly increases up to \(35\%\) while the \(\text {PPV}_{\text {auto}}\) value (green) remains at 8 %. Around 540 s, the \(\text {PPV}_{\text {manu}}\) value returns to 8 %. Figure 6 focuses on the time period marked with the black rectangular box in Fig. 5. The top plot in Fig. 6 shows that the heart beat between 535 and 535.5 s is contaminated by noise and has an abnormal morphology. As a result, the corresponding \(\mathrm {PP}_{\text {manu}}\) shown in the bottom plot reaches a large maximum value (\(\mathrm {PP}_{\text {min,manu}}:105 \mathrm{\, mm\, Hg}\)) around at 535 s. However, the automatically computed maximum \(\mathrm {PP}\) value (\(\mathrm {PP}_{\text {min, auto}}\)) at the same time is as low as \(83 \mathrm{\, mm\, Hg}\). This discrepancy between the manual annotation and the proposed automatic method results from the capability of the MAM-PF algorithm, which estimates the ABP signal based on the state-space model. While the original heart beat between 535 and 535.5 s in Fig. 6 is abnormal in a physiological sense, the estimated heart beat over the same time period shows the physiologically expected morphology and location of the heart beat.

### Study limitations

The algorithm’s assessment was based on only 11 subjects with pre-recorded ABP data. Additionally, for each subject five \(\text {PPV}\) estimates were used in the assessment study. This assessment was designed to be an engineering algorithm validation against current standard manual annotations, and not a clinical validation study. Consequently, a clinical validation study assessing the ability of the proposed algorithm to monitor fluid responsiveness in the operating room in situations involving spontaneously breathing subjects still needs to be conducted. This may require the proposed algorithm to be first adopted as part of a commercial system as was the case with our previous automatic \(\text {PPV}\) algorithm [20].

## Conclusion

We have described the first automatic \(\text {PPV}\) tracking algorithm for spontaneously breathing subjects. This novel algorithms is based on a statistical state-space model inspired in the underlying cardiovascular and respiratory physiology. This algorithm uses our recently developed SMCM (MAM-PF) for optimal parameter estimation. The assessment results indicate good agreement against the current standard \(\text {PPV}\). The algorithm was designed to work during regions of abrupt hemodynamic changes and spontaneous breathing. All of \(\text {PPV}_{\text {auto}}\) measurements were in agreement with \(\text {PPV}_{\text {manu}}\) measurements within ±4 % accuracy.

## Declarations

### Authors’ contributions

SK and JM developed the algorithm and carried out all simulation studies. FN was involved data management and literature review. MA conceived of the study and participated in the state-space model design and literature review. All authors read and approved the final manuscript.

### Acknowledgements

There is nobody who has made any significant contribution to this work other than the authors.

### Competing interests

The authors declare that they have no competing interests.

### Availability of data and material

All ABP signals included in this study are from the Massachusetts General Hospital Waveform Database (MGHDB) [14], which is publicly available on PhysioNet [15]. The proposed algorithm is protected by a US patent, “Method and Apparatus for Assessment of Fluid Responsiveness”, and cannot be shared.

### Funding

The current study was partially supported by East Carolina University’s new faculty startup funds for SK to write the manuscript.

**Open Access**This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

## Authors’ Affiliations

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