CN114159045A - High-robustness automatic respiratory signal measuring system - Google Patents
High-robustness automatic respiratory signal measuring system Download PDFInfo
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Abstract
The application provides a high robustness respiratory signal automatic measurement system, including Photoelectric Plethysmograph (PPG) signal acquisition module, and Photoelectric Plethysmograph (PPG) signal extraction and analysis module. The invention obtains photoplethysmography pulse wave signals through photoplethysmography (PPG) equipment, and carries out algorithm processing analysis with high robustness on the photoplethysmography (PPG) signals to obtain accurate respiration signals and give out respiration frequency (RR). The method improves the accuracy and robustness of respiratory signal measurement, and the measurement method is full-automatic and has accurate and reliable measurement data. The invention is used for disease prevention of heart and apnea, Systemic Inflammatory Response Syndrome (SIRS), renal failure and the like which take respiratory signals and respiratory frequency as important monitoring indexes, and has important clinical significance. And because the automatic respiratory measuring equipment provided by the invention has low cost, the automatic respiratory measuring equipment can be widely applied to places such as general wards, community hospitals, nursing homes and the like, and the development of preventive medical health careers is promoted.
Description
Technical Field
The application relates to the technical field of physiological information processing, in particular to a system and a device for monitoring and implementing respiratory signals on line.
Background
Respiratory signal and Respiratory Rate (RR) are among the indices of hospital prevention of abnormal conditions, such as cardiac and respiratory arrest, Systemic Inflammatory Response Syndrome (SIRS), renal failure, etc. The normal respiration rate in adults is 8-20 beats per minute (bpm). We found that in a study on respiratory abnormalities, 54% of patients with cardiac arrest had a respiratory frequency (RR) >27bpm at least three days before cardiac arrest. Therefore, it is crucial for monitoring the breathing of the patient. Unlike other vital signs, in most cases the measurement of the Respiratory Rate (RR) is performed manually, since currently widely available automated devices meeting clinical requirements are used for the measurement of respiratory rate. The importance of Respiratory Rate (RR) and the lack of technical means at present indicate that finding an automatic and continuous respiratory monitoring method which meets clinical requirements is of great clinical significance. Currently, methods used clinically include capnography (capnography), impedance plethysmography (impedance plethysmography), and flow thermography (flow thermography), which are expensive and invasive and not suitable for patients in general wards.
With the advancement of photoplethysmography, photoplethysmography (PPG) is widely used for respiratory monitoring both inside and outside of hospitals. Scientists have given a number of methods to estimate the respiratory rate from photoplethysmography (PPG) signals, such as the earliest Wavelet decomposition (Wavelet decomposition), digital filtering (digital filters), fourier transform (FFT), and complex demodulation (complex decomposition), classical modal decomposition and autoregressive models (AR), to the recent Principal Component Analysis (PCA) and artificial Neural Networks (NN), which have been successfully applied to various photoplethysmography (PPG) databases, with good estimates. There are studies using neural networks to analyze the three modulation signals to select the best waveform for the final algorithm design. There are also studies using data fusion in combination with estimates of multiple modulation signals. Despite such many advances, the use of pulse oximeters to measure respiratory signals has only recently been adopted for commercial use due to the lack of accuracy with which the above methods estimate respiratory signals. It is therefore important to provide a reliable method. A more common approach today is to introduce a Signal Quality Index (SQI) to evaluate the signal quality, and not to perform an algorithmic estimation of the photoplethysmography (PPG) signal if it does not carry meaningful physiological information. Lack of quality indicators may lead to serious clinical errors, and introduction of evaluation indicators may improve accuracy and reliability. That is, there is no respiratory signal monitoring method that is safe, accurate and convenient for wide application in clinic.
Disclosure of Invention
The application aims to provide a high-robustness automatic respiratory signal measuring system, and provides a method and equipment which are accurate, convenient to use and capable of monitoring on line for measuring by using a photoplethysmograph (PPG) device and obtaining a respiratory signal and Respiratory Rate (RR) with high accuracy from a PPG signal.
To achieve the above object, the present invention provides a highly robust respiratory signal automatic measurement system, comprising:
a PPG signal acquisition module 100, which acquires a photoplethysmograph (PPG) signal by a photoplethysmograph device;
the PPG signal extraction and analysis module 200 extracts three respiratory modulation signals, namely, peak sequence amplitude (rriiv), continuous peak time difference (RIFV) and pulse height (RIAV), performs spectral analysis on the respiratory modulation signals, performs data fusion on the signals and data obtained by the spectral analysis, and finally obtains accurate and reliable respiratory signals and Respiratory Rate (RR).
Preferably, the PPG signal acquisition module comprises a photoplethysmograph device employing a wearable pulse oximeter, estimating the blood oxygen saturation (SpO2) according to Beer-Lambert's law, using the photoplethysmogram to display the change of blood volume in the finger over time, including a pulse component and a constant component, the respiratory and heartbeat frequencies being included in the pulse component of the photoplethysmograph (PPG) signal, and transmitting the signal to a PPG signal extraction and analysis module;
preferably, the PPG signal extraction and analysis module comprises: a respiration modulation signal extraction unit which extracts and evaluates three respiration modulation signals of peak sequence amplitude (RRIV), continuous peak time difference (RIFV) and pulse height (RIAV); the signal analysis unit is used for carrying out spectrum analysis on the three modulation signals; and the data fusion unit is used for performing data fusion on the result of the frequency spectrum analysis.
Preferably, the respiration signal modulation extraction unit includes: three respiration modulation signals of peak value sequence amplitude (RRIV), continuous peak value time difference (RIFV) and pulse height (RIAV) are obtained by adopting peak value detection, peak value verification and trough detection, and signal quality evaluation is carried out on the signals, wherein the evaluation method comprises an increment merging subdivision algorithm (IMS), double pulse detection and Signal Quality Index (SQI) calculation.
Preferably, the signal analyzing unit includes: performing spectrum analysis on the three modulation signals, including high-efficiency Fourier transform (FFT), autocorrelation Analysis (AC) and establishing an autoregressive model to obtain three respiratory signals and Respiratory Rate (RR) pre-estimated values;
preferably, the data fusion unit includes: and performing data fusion on the estimated signal and the data obtained by the frequency spectrum analysis to obtain a high-accuracy respiratory signal and Respiratory Rate (RR).
The high-robustness automatic respiratory signal measuring system provided by the invention is simple in equipment operation, low in cost, accurate and reliable in result, and suitable for online monitoring of respiratory frequency in various places.
The invention relates to a high robustness respiration signal automatic measuring system which is characterized in that:
1) the device can be used for monitoring the respiratory frequency on line in various places such as hospitals, community hospitals and families;
2) the equipment is simple and safe to operate, high in automation degree and accurate and reliable in result;
3) the equipment cost is low, the limitation by fund is small, and the device can be used in places such as common medical institutions and the like.
Drawings
In order to more clearly illustrate the technical solution of the embodiment of the present application, a structural block diagram of a high-robustness automatic respiratory signal measurement system in fig. 1 is provided. The drawings illustrate only certain embodiments of the application and are therefore not to be considered limiting of scope, for those skilled in the art will appreciate that other related drawings may be derived therefrom without the exercise of inventive faculty.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application, and it should be noted that the described embodiments are only intended to facilitate understanding of the present invention, and do not have any limiting effect.
The high-robustness respiratory signal automatic measurement system in the embodiment of the application is composed of a PPG signal acquisition module 100 and a PPG signal extraction and analysis module 200.
The PPG signal acquisition module is a wearable pulse oximeter 100. Wearable pulse oximeter 100 collects the photoplethysmograph (PPG) signals, transmits them to PPG signal extraction and analysis module 200 via wifi.
The PPG signal extraction and analysis module 200 is composed of a respiratory modulation signal extraction unit 210, a signal analysis unit 220, and a data fusion unit 230. The respiratory modulation signal extraction unit 210 extracts three respiratory modulation signals, namely peak sequence amplitude (RRIV), continuous peak time difference (RIFV) and pulse height (RIAV), from a photoplethysmograph (PPG) signal; the signal analysis unit 220 analyzes the extracted respiration modulation signal to obtain three estimated respiration signals and estimated respiration frequency (RR) values; the data fusion unit 230 performs data fusion on the obtained estimated respiratory signal and the estimated Respiratory Rate (RR) value to finally obtain an accurate and reliable respiratory signal and Respiratory Rate (RR).
The following detailed description of embodiments of the invention:
The signal acquisition module 100 of the photoplethysmography (PPG) is worn at the finger end by the wearable pulse oximeter 111, driven by a firmware program, and transmits the acquired signal of the photoplethysmography (PPG) to the PPG signal extraction and analysis module 200 through Wifi via the data acquisition and transmission unit 110.
(II) PPG Signal extraction and analysis Module 200
The PPG signal extraction and analysis module 200 runs on a computer and is composed of a respiration modulation signal extraction unit 210, a signal analysis unit 220, and a data fusion unit 230.
The respiratory modulation signal extraction unit 210 extracts three respiratory modulation signals, namely peak sequence amplitude (RRIV), continuous peak time difference (RIFV) and pulse height (RIAV), from a photoplethysmograph (PPG) signal; the signal analysis unit 220 analyzes the extracted respiration modulation signal to obtain an estimated respiration signal and an estimated respiration frequency (RR); the data fusion unit 230 performs data fusion on the obtained result to finally obtain an accurate and reliable respiratory signal and Respiratory Rate (RR). The specific analysis method is as follows:
1. extraction of respiratory modulation signals
A. Method idea
a. The respiratory frequency (RR) is extracted from a photoplethysmograph (PPG), which is generally segmented using sliding windows, each window yielding a respective respiratory frequency (RR). The first step of the algorithm is typically to extract the respiration modulation signal from a photoplethysmograph (PPG) signal, which has many methods, the most common being the detection of peaks and troughs of the photoplethysmograph (PPG) signal. We define the photoplethysmograph (PPG) signal peak time series as a set of points tpk，i，yPk，i}i＝1...NpkDefining a trough time series as a set of points { t }tr，i，ytr，i}i＝1...NtrNpk, Ntr are the number of peaks and troughs respectively. Due to noise in the signal or false detection in the detection algorithm, Npk≠Ntr。
b. The peak-to-valley time series will be used to generate three different breath modulation signals representing three different pieces of information for a breath.
1) Peak sequence amplitude (RRIV): the breath-guided intensity variation is the amplitude of the sequence of peaks. This is due to changes in intrathoracic pressure resulting in changes in perfusion baseline, which are manifested as changes in amplitude of the photoplethysmograph (PPG) peak. Therefore, the temperature of the molten metal is controlled,
2) pulse height (RIAV): the respiratory-guided amplitude variation, i.e. the height of the photoplethysmograph (PPG) pulse, is defined as the height difference between two adjacent peak troughs. This is due to respiration-induced cardiac output changes, resulting in changes in the absolute amplitude of the photoplethysmography (PPG) signal. Therefore, the temperature of the molten metal is controlled,
3) continuous peak time difference (RIFV): respiratory induction frequency changes are changes in the instantaneous HR value of the respiratory cycle, a phenomenon known as Respiratory Sinus Arrhythmia (RSA). Defined as the time difference between successive peaks. Therefore, the temperature of the molten metal is controlled,
still other respiratory modulation signals are also given, such as pulse width variations. These modulation signals can be used to estimate the Respiratory Rate (RR).
c. Preprocessing operations are typically required before data processing can take place. High frequency noise is first filtered out using low pass filtering. The photoplethysmograph (PPG) signal is then captured using an incremental merging and subdividing algorithm (IMS). The respiratory modulation signal is then extracted using the three approaches mentioned above. Due to the non-uniform time sequence, the signal needs to be resampled at fs-4 Hz. To unify the metrics of the three signals, normalization with zero mean-one variance was used. In order to increase the reliability of the signal, a signal evaluation index (signal quality index SQI) is added to evaluate the modulation signal.
B. For Respiratory Rate (RR) estimation, the most critical is the extraction of the respiratory modulation signal. The main methods for extracting the respiration modulation signal are peak detection and signal quality evaluation. The present invention presents an improved peak detection method and signal evaluation method, followed by estimation of respiratory signal and frequency using spectral analysis and data fusion.
a. Peak detection
The general principle of peak detection is that any singular point of a differentiable signal corresponds to a zero crossing or two inflection points in its derivative signal. The left side of the peak value is raised and the right side is lowered intuitively. The general approach to peak detection is to have the first derivative be zero and the second derivative be less than zero. There is also a method to provide that the maximum value of the original signal is the peak value between two inflection points of the first derivative, i.e. the subscripts corresponding to the maximum value and the minimum value. The invention provides a new method for peak detection without a second derivative and without judging the inflection point of the first derivative. In order to avoid the influence of the amplitude change of the front and back of the photoplethysmograph (PPG) signal on peak detection and verification, windowing is carried out on the photoplethysmograph (PPG) signal, a 10s time window is used, and the overlapping time is 5s (avoiding missing part of peaks).
Peak verification: peak verification is primarily initiated from two aspects, the amplitude threshold and the time interval threshold. In terms of amplitude threshold setting, let thresh1 be the ninety-th quantile, thresh2 be the first ten-decile, and then,
then order
Wherein peaks is a peak point. If the highdiff is less than middlediff&If highdiff < lowdiff, the peak point is recorded. In terms of time interval, since the pulse wave is mainly controlled by the heartbeat, the normal resting heart rate is 60-100, and the peak value in this range is detected.
Wave trough detection: when a Photoelectric Plethysmograph (PPG) signal is observed, the minimum value between two peak values is the trough.
b. Signal quality assessment (incremental Merge Subdivision Algorithm IMS, Dual pulse recognition, Signal Quality Index (SQI))
Since motion artifacts (artifacts) and noise that cannot be filtered out exist in the photoplethysmograph (PPG) signal, an evaluation of the quality of the photoplethysmograph (PPG) signal is required. The method for evaluating the signal quality comprises the steps of dividing a pulse of a photoplethysmography (PPG) into line segments (convenient to process and judge) by using an IMS (IP multimedia subsystem) algorithm, and judging the proportion of an artifact and a clip in the double pulse as a Signal Quality Index (SQI) to judge the signal quality after the double pulse is processed.
IMS (Incremental-Merge Segmentation increment merging subdivision) algorithm: the IMS algorithm has a sliding window structure and can be used for real-time processing. The algorithm only needs to set a parameter m (the number of points moved each time, mainly related to the sampling rate). The principle is that the Photoelectric Plethysmograph (PPG) signal is divided into n segments with the length of m, the slopes of the segments are calculated, if the slopes have the same slope, the segments are combined (the starting point of the first line segment and the end point of the second line segment are connected into a straight line), and if the slopes have different slopes, the segments are divided into new line segments.
Double pulse detection: by judging the slope as a negative line segment and a subsequent line segment, if a certain condition is met, the pulse is double pulse. After the double pulse is judged, the three line segments of the double pulse are merged.
Signal Quality Index (SQI): the rise segments are in one-to-one correspondence with the fall segments, and therefore, the rise segments are analyzed separately. If the threshold and the slope of the ascending line segment both exceed the threshold, judging the ascending line segment to be artifact; if the slope is zero, determining that the slope is clip; the segment immediately following the clip is also artifact; and calculating the ratio of artifact to clip in a photoplethysmography (PPG) signal, calculating a Signal Quality Index (SQI), and judging the signal quality.
2. Signal analysis (FFT, autocorrelation, autoregressive, autocorrelation + autoregressive)
After obtaining the peak-to-valley sequence from the peak detection, three respiratory modulation signals, peak sequence amplitude (RRIV), continuous peak time difference (RIFV), and pulse height (RIAV), were calculated using the methods described above. Since a non-uniform time series is obtained, it is resampled f by linear interpolation for the purpose of subsequent spectral analysiss4 Hz. And meanwhile, zero-mean square difference standardization is used, so that the amplitudes of the three modulation signals are unified to the same range. Then, a high-pass filter is used to filter out the low-frequency signal interference, and a moving average filter is used to smooth the signal.
FFT: performing efficient Fourier transform on the three modulation signals, and selecting the frequency with the maximum amplitude from a frequency domain as a respiratory frequency (RR); because the number of modulation signal points is less, zero padding processing is carried out on the modulation signals in order to improve the frequency spectrum resolution.
AC: autocorrelation analysis is a mathematical tool that looks for repetitive patterns, such as looking for periodic signals that are masked by noise. The breathing signal can be regarded as a noisy periodic signal. The autocorrelation formula is
Wherein xiFor time series of signals, xi+τFor time series shifted by τ units, μ being the mean, σ2Is the variance. An autocorrelation sequence can be obtained from τ -0 to τ -n-1. The abscissa is the number of translation points tau, the relationship between the frequency and the number of translation points is f ═ fs/tau, and fs is the sampling frequency. In the autocorrelation signal, each peak (except the first one) represents a period of strong autocorrelation, and the period with the largest correlation is taken as the Respiratory Rate (RR).
AR: the AR model is an alternative method to Discrete Fourier Transform (DFT) and is also one of the methods for short-time series high-resolution spectral estimation. In biomedical engineering, AR models are widely used for spectral analysis of heart rate variability and electroencephalography. In the AR model, each value of the time series is a regression to its past values. The number of past values used is referred to as the order of the model. The AR model can be viewed as a filter that divides the time series into a predictable time series and a prediction error series. It provides a smoother, more easily understood power spectrum than the discrete fourier transform, but also has the disadvantage of complex model identification. The AR model is defined as:
wherein M is the model order, aiAs a weight, ε [ n ]]Is a prediction error term and obeys ε -N (0, σ). Using least squares to make the prediction error epsilon n]Minimizing to obtain optimal parameter ai。
The above equation is developed
x[n]＝a1x[n-1]+a2x[n-2]+...+aMx[n-M]+ε[n] (2)
The first N predictions are:
m unknowns require M equations that can be solved using the least squares method. Matrixing the above formula:
x＝Xa+ε (4)
wherein X is a matrix with M rows and M columns, and a and epsilon are M-dimensional column vectors.
The optimum parameter a is requiredoptI.e. the minimum prediction error n is required]Namely:
ε＝x-Xaopt0; (5) i.e. by
(XTX)-1，
(XTX)-1(XTX)aopt＝aopt＝(XTX)-1XTx (7)
At the same time, due to XTX is very close to the autocorrelation matrix of X,
XTx is very close to the autocorrelation vector of x,
Another important point of the autoregressive model is the choice of the order M of the model, with different order fits of the model being good or bad. In practical application, a plurality of models are fitted to the sequence, and different models are selected according to effects. The most common selection criteria is Akaike's Information Criterion (AIC), expressed as:
Meanwhile, according to a Yule-Walker equation:
σ2＝[r(0) r(1) r(2)…r(M)][1 a1 a2…aM]T。
from the z field, formula (1) can be written as:
And because z is ejwTTo obtain
Then, the frequency spectrum R (e) of the time seriesjw) Can be obtained by multiplying the square of the transfer function by the variance of the prediction error. Namely, it is
AC + AR: as can be seen from the characteristics of the autocorrelation method, autocorrelation can remove part of the noise in the periodic signal. Each peak of the autocorrelation coefficients represents a period of strong autocorrelation, so that the period of autocorrelation corresponds to the period of the respiration signal. Therefore, the respiratory frequency (RR) can be estimated using the autocorrelation signal of the respiratory modulation signal as an input signal for AR spectral analysis.
3. Data fusion
For the estimated respiratory signal and Respiratory Rate (RR) obtained by the three respiratory modulation signals, a data fusion can be performed in order to improve the accuracy and reliability. The common fusion method is to directly average the frequency spectrums of the three modulation signals, and take the maximum value as the Respiratory Rate (RR). The present invention provides two improved methods:
first, from the above spectral analysis, for each kind of respiration modulation signal, a Respiration Rate (RR) is obtained, which is respectively a Respiration Rate (RR) _ peak sequence amplitude (RRIV), a Respiration Rate (RR) _ pulse height (RIAV), a Respiration Rate (RR) _ continuous peak time difference (RIFV), and if the absolute value of the difference between the three Respiration Rates (RR) is within 2bpm, the average value of the three signal spectra is determined; if the absolute value of the difference value of any two respiratory frequencies (RR) is within 2bpm, taking the average value of the frequency spectrum; otherwise, the Respiration Rate (RR) _ continuous peak time difference (RIFV) is taken (because it is observed from the experiment that the waveform of the continuous peak time difference (RIFV) is closer to the respiration waveform).
Secondly, as the autocorrelation signal of the modulation signal can be used as a respiration signal to be analyzed, and the waveform is more regular, the autocorrelation signal is segmented by using an IMS algorithm (as the autocorrelation signal of the normal respiration signal is approximate to a sine wave, the variance value is smaller, and the mean value is close to 1, namely the waveform is more stable), variance analysis is carried out on the decomposed segment, the Signal Quality Index (SQI) ═ var/mean is used as an index for measurement, and the modulation signal of which the Signal Quality Index (SQI) is smaller than a certain range is taken as frequency spectrum average.
To sum up, the high-robustness respiration signal automatic measurement system provided by the embodiment of the application acquires the PPG signal through the PPG signal acquisition module, and analyzes the signal through the PPG signal extraction and analysis module to finally obtain the accurate and reliable respiration signal and the respiration frequency. Accurate and effective respiratory signal online monitoring data can be provided for clinical use.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions in actual implementation, and for example, a plurality of units may be combined or integrated into another module, or a unit of a module may further refine more units.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (6)
1. A highly robust respiratory signal automatic measurement system, comprising:
a PPG signal acquisition module 100, which acquires a photoplethysmograph (PPG) signal by a photoplethysmograph device;
a PPG signal extraction and analysis module 200; the PPG signal extraction and analysis module 200 comprises the following units: a respiratory signal extraction unit 210 that extracts three respiratory modulation signals, namely, a peak sequence amplitude (RRIV), a continuous peak time difference (RIFV), and a pulse height (RIAV); a signal analysis unit 220 that performs spectrum analysis on the respiration modulation signal; the data fusion unit 230 performs data fusion on the signals and data obtained by the spectrum analysis, and finally obtains accurate and reliable respiratory signals and Respiratory Rate (RR).
2. The system as claimed in claim 1, wherein the PPG signal acquisition module comprises:
the photoplethysmograph device employs a wearable pulse oximeter, estimates the blood oxygen saturation (SpO2) according to Beer-Lambert's law, uses a photoplethysmogram to display the change in blood volume in the finger over time, including a pulse component and a constant component, with the respiratory and heartbeat frequencies included in the pulse component of the photoplethysmograph (PPG) signal, and transmits the signal to a PPG signal extraction and analysis module.
3. The system as claimed in claim 1, wherein the PPG signal extraction and analysis module comprises:
a respiration modulation signal extraction unit which extracts and evaluates three respiration modulation signals of peak sequence amplitude (RRIV), continuous peak time difference (RIFV) and pulse height (RIAV);
the signal analysis unit is used for carrying out spectrum analysis on the three modulation signals;
and the data fusion unit is used for performing data fusion on the result of the frequency spectrum analysis.
4. The PPG signal extraction and analysis module of claim 3, wherein the respiratory signal modulation extraction unit comprises:
three respiration modulation signals of peak value sequence amplitude (RRIV), continuous peak value time difference (RIFV) and pulse height (RIAV) are obtained by adopting peak value detection, peak value verification and trough detection, and signal quality evaluation is carried out on the signals, wherein the evaluation method comprises an increment merging subdivision algorithm (IMS), double pulse detection and Signal Quality Index (SQI) calculation.
5. The PPG signal extraction and analysis module of claim 3, wherein the signal analysis unit comprises:
and performing spectrum analysis on the three modulation signals, including high-efficiency Fourier transform (FFT), autocorrelation Analysis (AC) and establishing an autoregressive model to obtain three respiratory signals and Respiratory Rate (RR) pre-estimated values.
6. The PPG signal extraction and analysis module of claim 3, wherein the data fusion unit further comprises:
and performing data fusion on the estimated signal and data obtained by the spectrum analysis, performing spectrum evaluation or autocorrelation signal quality analysis, and obtaining a high-accuracy respiratory signal and Respiratory Rate (RR).
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