JP2013192905A - Biophotonic measuring apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To efficiently extract signal changes for a phenomenon, in observed signals including noises resulting from repeated measurements for signal changes to the phenomenon, without assuming any model waveform, and also without assignments of signals after analysis.SOLUTION: If a mixing coefficient is obtained for mixing all the observation signals gathered from a plurality of view points for a phenomenon, a method is used that obtains as a reference the maximum degree of similarity among all the signals resulting from repeated measurements for their changes related to the phenomenon.

Description

  The present invention is designed to activate a brain activation task (for example, a function for recognizing the color of an object in the visual cortex of the brain) designed for deriving a change in a brain activity state related to a specific brain function. Changes in the state of the subject (for example, the presentation of memory problems designed to activate functions related to short-term memory in the frontal lobe of the brain, for example). (For example, changes in cerebral hemodynamics, changes in skin blood flow, changes in skin condition due to sweating, changes in measurement state due to body movement, etc.) were observed twice or more times, and signals were acquired. For a device that implements this method to remove the noise signal that is not related to the event and superimposes the change signal corresponding to the event with high accuracy. It is.

  Or even if the observer is not actively presenting a subject to the subject, the hemodynamics associated with the biological changes that the subject spontaneously and repeatedly causes, for example, autonomous control of respiration and blood pressure The present invention relates to a device that implements a technique for separating a signal change accompanying a specific spontaneous response and other signal changes with high accuracy when a fluctuation of the change is repeatedly observed as a spontaneous response.

  In general, in actual measurement, when observing a signal change for a certain event, a noise component that is difficult to predict is mixed in the observed signal. In this case, conventionally, it is common to reduce the influence of noise components by repeating the observation of events and averaging the obtained observation signals. It is said that the amplitude of the noise component can be reduced. However, in order to obtain a sufficient effect when the amplitude of the noise component is large, it is necessary to increase the number of repetitions N, that is, the observation time becomes long.

  As described in Non-Patent Document 1, when a target signal or a signal pattern of a mixed noise component can be assumed and they are considered to be linear mixture, several model waveforms are set. Based on these, a general method is to estimate the magnitude of the signal change related to the event by regression analysis of the observed waveform. As described in Non-Patent Document 2, principal component analysis and independent component analysis are generally used as methods for separating signals based on the assumption that the statistical characteristics of the assumed target signal and the superimposed noise signal are different. Is.

K.J. Friston, A.P. Holmes, K.J.Worsley, J.B.Poline, C. Frith, and R.S.J.Frackowiak.Statistical Parametric Maps in Functional Imaging: A General Linear Approach.Human Brain Mapping, 2: 189-210, 1995 Katura T, Sato H, Fuchino Y, Yoshida T, Atsumori H, Kiguchi M, Maki A, Abe M, Tanaka N. Extracting task-related activation components from optical topography measurement using independent components analysis.J Biomed Opt. 2008 3 (5 ): 054008

  In the method of setting several model waveforms in Non-Patent Document 1, a correct result cannot be obtained if the assumption of the signal pattern is incorrect. In particular, regression analysis cannot be used when unpredictable noise components are present.

  Further, in the method of principal component analysis and independent component analysis of Non-Patent Document 2, it is necessary to make meanings for each component after separation, and it may be difficult to make objective meanings sufficiently.

  Therefore, it is possible to change the signal change for an event without assuming a model waveform or making meaning for the analyzed signal with respect to the observed signal mixed with the noise that is measured repeatedly. It is important to extract efficiently.

A plurality of light irradiation units that irradiate light to a plurality of locations of the subject, a plurality of light detection units that detect the irradiated light through the subject, and a plurality of lights detected by the plurality of light detection units In a biological light measurement device including a calculation unit that calculates a signal, for a signal obtained by repeatedly measuring a subject twice or more, the calculation unit calculates a mixing coefficient for mixing the signals detected by the plurality of detection units, Provided is a biological light measurement device that is derived on the basis that the degree of similarity between signals obtained by repeatedly measuring the subject is maximized.

It enables separation / removal of noise components generated in synchronization with the experimental task and mixed in the signal, and provides a signal with less noise.

Configuration diagram of the present invention Example of signals generated on a computer Example of observed signal Screen configuration example Example of the result of separating the observed signal examples Example results of separating simulated observation signals Examples of brain activity signals and analysis results Example of distribution obtained by repetition Measurement signal discrimination flow Example of teacher signal Example results

  A specific configuration diagram as an embodiment is shown in FIG. The biological light measurement interface unit (110) is attached to a part or the whole of the subject's head. By the optical fiber (111) connected to the interface unit (110), the light irradiation unit (101) irradiates the living body with light having a wavelength that transmits the living body to some extent, and the optical fiber (111) connected to the interface unit (110). The light having passed through the living body is detected by the light detection unit (102), and the detection result is recorded in the storage unit (103).

  It is assumed that there are a plurality of optical fibers in contact with the living body, that is, a plurality of observation points. Here, the wavelength of the irradiated light may be other. Three or more combinations of wavelengths may be used. Moreover, you may use what performed some arithmetic processing, for example, the conversion to a hemoglobin density | concentration change, with respect to the obtained optical signal for subsequent processes.

  Signals from a plurality of observation points obtained as described above are used as measurement signals. During the measurement, subjects are presented with experimental tasks that activate specific brain functions. The presentation of the experimental task is repeated. Information on the timing of the event of presenting the experiment task is recorded in the storage unit (103) as a measurement signal.

  Next, the calculation unit (121) extracts a signal change (event related component) that occurs with high reproducibility in relation to the repeatedly presented event from the observation signal and the event timing information.

  In this embodiment, the correlation maximization method, which is a novel method in the present invention, is used as the extraction method. Details will be described below.

-In order to show the correlation maximization analysis method, Fig. 2 shows a pseudo-generated signal on a computer. The signal waveforms in (201) to (206) simulate signal changes corresponding to changes in the state of the subject. In particular, the signal waveforms are assumed to be optical topography measurements. Signal change due to brain activity caused by event (however, event was assumed to have been repeatedly presented 5 times, and the presentation was made over the period of gray area in the figures (207) to (211)) , (202) is a low-frequency fluctuation noise component related to autonomous hemodynamic control, (203) is a pulse wave noise component caused by heartbeat, and (204) is a drift noise component often found in optical topography signals , (205) is a body motion noise component caused by a relatively small body motion, and (206) is a simulation of a body motion noise component caused by a relatively large body motion. These (201) to (206) are called original signals.

  Also, a signal waveform (201) that simulates a component related to brain activity is called a simulated brain activity signal, and signal waveforms (202) to (206) that simulate various noise components are called noise signals 1 to 5. In actual measurement, these original signals are mixed and observed as one signal at observation points on different heads. Therefore, it is possible to simulate the observation signal by mixing the original signal at a random ratio as a signal waveform that simulates the observation signal.

  In this example, assuming that there are 6 observation points, each of the 6 original signals was mixed at a random mixing ratio, and a signal was generated that was mixed with white noise that could be mixed during the observation. . A pseudo observation signal is shown in FIG. 3 (301 to 306).

  When actual brain function measurement is assumed, the objective is to extract simulated brain activity signals from simulated observation signals. For the above simulated observation signal, if the mixed coefficient sequence of the simulated original signal set when generating the simulated observation signal is known, the simulated original signal should be derived from the six simulated observation signals by simultaneous equations. Is possible in principle.

However, in the case of an actual observation signal, the mixing coefficient sequence is unknown. Here, the brain activity signal (here, simulated brain activity signal) can be derived by multiplying the observation signal obtained from multiple observation points (here, simulated observation signal) by the optimal coefficient and taking the sum. Assume that Based on this assumption, if the simulated brain activity signal (201) is y (t), y (t) is the simulated observation signals (101) to (106) x ₁ (t) to x ₆ (t) By using a certain coefficient sequence w,

Will be expressed.

  Here, the subscript i means the observation point number. If y (t) derived in this way is equal to the true simulated brain activity signal, the pattern of the five waveform parts cut out to include each time of the event occurrence period (207) to (211) Should be the same.

  That is, the correlation coefficient between any two waveform portions of the five waveform portions should be 1. Expressing this as an expression, the time including the arbitrary time before and after the event (for example, 5 seconds before the event and 10 seconds after the event, etc.) Then, the correlation coefficient of the signal change corresponding to any kth event and any lth event is

Is defined.

  Here, the subscripts i and j are observation point numbers, Corr (A, B) is the correlation coefficient between A and B, and E [] is the expected value. This equation is used to derive a correlation coefficient generally used in the field of statistics and the like, and the similarity between the observed signal waveform corresponding to the kth event and the observed signal waveform corresponding to the lth event. This is an index indicating 1 when the waveforms are most similar and -1 when the waveforms are not most similar.

  In the equation (2), an index for determining the similarity between signal waveforms corresponding to two events is obtained. Therefore, this equation is further expanded to provide an index for comprehensively determining the similarity of the signal waveform corresponding to five events, or arbitrary K events as a mathematical formula, and the signal change of two events. Is the sum calculated for the possible combinations of selecting two events from five (K) events. This can be expressed as an expression:

It becomes. This is defined as a correlation sum.

  Therefore, if y (t) calculated by Equation (1) is equal to the simulated brain activity signal (201), the correlation sum derived from Equation (3) is derived for five events. If it is different from the simulated brain activity signal, it should be less than 5. Therefore, it is considered that by obtaining w that maximizes the correlation sum, it is possible to extract a target signal that is reproduced in response to a repetitive event, that is, a simulated brain activity signal.

  However, to prevent divergence in the maximization calculation, the boundary condition is

At the same time.

  The actual calculation can be considered as a general optimization problem. For example, there is a simplex method. However, since there are problems such as giving local extreme values as solutions, it is desirable to use a more advanced method that has been developed.

  Fig. 5 shows an example of the result of separating the observed signal example shown in Fig. 2 using the correlation maximization method. Components corresponding to the simulated brain activity signal (101) in FIG. 1 are extracted with high accuracy.

  The above is the description of the correlation maximization method. In this embodiment, the measurement signal and the timing of the repeatedly presented event are held in the storage unit (103), and the calculation unit (121) uses them to derive the correlation sum defined in equation (3). Deriving an optimal mixture coefficient sequence of multiple measurement points to best extract the signal components synchronized with the event. Then, the display unit (122) displays the derived waveform. In the display section, the actual observed waveform and the waveform derived by this method are explicitly distinguished and displayed. An example of the screen configuration is shown in FIG.

  The arithmetic unit performs preprocessing on the observation signal as necessary. For example, when high frequency noise is large, smoothing processing is performed, and when there is a large variation in low frequency, the signal is processed to some extent by preprocessing such as a high-pass filter.

  However, in the correlation maximization method according to the present invention, it is possible to appropriately extract event-related components even when there are high-frequency noise and low-frequency fluctuations. .

  In the display, the eigenvector corresponding to the brain activity signal may be displayed as a map corresponding to the position of each channel. The degree to which the simulated brain activity signal is included in each channel may be derived from the observed signal by the least square method or the like and displayed as a map.

  In the first embodiment, the correlation maximization method is used as a means for deriving the optimum mixing coefficient. In this embodiment, the covariance maximization method proposed in the present invention is used instead of the correlation maximization method. The covariance maximization method will be described below.

  In the correlation maximization method, a correlation coefficient is used as an evaluation function. In this embodiment, covariance is used as an evaluation function. The covariance of the signal change corresponding to event k and event l is

  The sum of the covariances of brute force events that is defined as

It is expressed as here,

It is. However, i and j mean measurement point numbers. This evaluation function is called a covariance sum. By obtaining the mixing coefficient w that maximizes this covariance sum, it is possible to extract a simulated brain activity signal that is reproduced in response to repeated events. However, in order to prevent divergence in the maximization calculation, assuming that the boundary condition is the same as in the first embodiment, this maximization is expressed by the following equation.

  From the Rayleigh-Ritz theorem

The eigenvector of is w. At this time, the eigenvalue is an index indicating the validity of y synthesized by each eigenvector.

  Figure 6 shows an example of the result of separating the simulated observation signal shown in Fig. 3 using the covariance maximization method. The component corresponding to the simulated brain activity signal (101) in FIG. 1 is accurately extracted as the component (601).

  Examples 1 and 2 are examples in which brain activity signals are extracted from observation signals based only on information related to the event presentation period. In the present embodiment, a technique for performing noise separation with high accuracy will be described. That is, when a time-series signal related to a noise signal is observed at the same time as the observation signal, the signal related to the noise is used to improve the noise separation accuracy.

  In actual measurement, although it is not related to the event, the observation signal related to the signal component superimposed as noise on the observation signal, for example, the artifact accompanying the movement of the body, such as the movement amount of the body movement, acceleration, etc. If the time change is measured at the same time as the observation signal and the information is known, the observation signal used in the calculation to derive the optimal mixing coefficient by the correlation maximization method or the covariance maximization method By adding this signal as an observation signal from a new observation point, artifacts can be removed more effectively.

  An example is shown in FIG. FIG. 7- (a) shows a brain activity signal (701), a noise signal (702) simulating, for example, respiration superimposed on the observation signal, and a noise signal (703) simulating noise due to body movement. (704) to (706) in Fig. 7- (b) add the three signals (701 to 703) in Fig. 7- (a) after multiplying them by an arbitrary random coefficient, and add white noise. Signal. (707) is a simulation of a known signal component related to the body motion noise signal (703), which simulates a signal obtained by observing an acceleration change accompanying the movement of the body.

  In the first and second embodiments, the analysis method uses only the three observation signals (704) to (706) in this case, but in this embodiment, (707) is added as an observation signal from a new observation point. , (704) to (707), the correlation maximization method or the covariance maximization method is applied, and the optimal mixture coefficient sequence for each observation point for deriving the brain activity signal is derived.

  An example of actual analysis results is shown in Fig. 7-c. Here, the correlation maximization method is used as an analysis method. First, the derived signal (708) is a signal that is analyzed and derived without adding the observation signal (707) as in the first embodiment. The derived signal (709) is the result of analysis by adding the observation signal (707). Compared with the derived signal (708), the noise component is clearly removed in the derived signal (709). In this example, the original noise signal (703) and the signal (707) added at the time of analysis are the same signal sequence, but they are not necessarily the same. In this example, one noise component is added, but a plurality of components may be added.

In the automatic waveform selection covariance maximization method based on statistical criteria in the covariance maximization method, for example, a plurality of extracted signals (extracted waveforms (601) to (606)) are generated as shown in FIG. Therefore, it is necessary to select which corresponds to the brain activity signal related to the event, and an extracted signal having a larger eigenvalue can be considered to be a component synchronized with the event. Here, the eigenvalue is obtained by diagonalizing the matrix shown in Equation (9).

  For example, in FIG. 6, the eigenvalue of (601) is 28.374, whereas (602) is 1.0464, and (601) is more synchronized with the event, that is, a reasonable result as a brain activity signal. It is thought that. However, the objectivity of the selection criterion remains arbitrary, and it is difficult to determine the selection criterion particularly when the difference between eigenvalues between extracted signals is small. Therefore, it is possible to show the significance of eigenvalues based on statistical criteria based on the degree of deviation from the distribution, based on the distribution of analysis results using pseudo-generated random signals.

  As the generation of the random signal, for example, event timing information is set at random. Based on the timing information set at random, the covariance maximization method is applied to obtain the eigenvalue. This is repeated as many times as sufficient distribution is obtained. For example, FIG. 8 (801) shows a distribution (802 to 804) of eigenvalues derived based on the distribution obtained by 1000 repetitive calculations and the actual event timing information. If the statistical significance level is 5%, for example, the wavy line (805) is the reference, and the eigenvalues exceeding this reference are two (803, 804).

・ Distinguish issues by optical topography measurement signal (BMI application)
In the embodiments so far, the case where only one type of event is repeated has been exemplified. Here, when there are two or more types of events, for example, when different events such as right hand movement (event A) and left hand movement (event B) are presented as events to be repeatedly presented, examples of applying the present invention Will be explained.

  Discriminating which event is caused by a change in signal caused by different event A or event B is the field of Brain Computer Interface (BCI). This is the subject of research.

  Generally, in BMI, in order to discriminate between two different brain activity states, a learning signal measured in advance is used, a discrimination means (filter) is prepared, and an actual measurement is discriminated by a discrimination means (filter) prepared in advance. To do. The simplest discrimination means is signal thresholding.

  In other words, the difference in signal amplitude between the two states to be discriminated is derived from the learning signal, a threshold for discriminating between the two states is derived, and whether or not the amplitude of the signal to be discriminated exceeds the threshold The actual discrimination is performed by. FIG. 9 shows a measurement signal discrimination flow according to the method of the present invention.

  In the first step, a mixing matrix M is derived based on the learning signal. An example of the teacher signal is shown in FIG. The learning signal means an observation signal whose event content corresponding to the signal is known. In the example of FIG. 10, event A and event B are signals obtained from five observation points that are alternately repeated five times, and correspond to the learning signal (901).

  Based on this, in the flow (902), a mixing matrix M is derived that has high reproducibility in each of the events A and B and has high discrimination ability for the events A and B. For this purpose, the evaluation matrix defined by Equation 3 and Equations 6 and 7 is changed. For example, the correlation between repeated event A is high, the correlation between event B is also high, and the correlation between events A and B is low (negatively large) at the same time. An evaluation matrix is determined so as to obtain a mixing matrix M for deriving a satisfying extracted component. The concrete formula is the evaluation sequence for events A and B.

And However,

It is.

  The evaluation matrix S for deriving the mixing matrix M for discriminating the events A and B using the equation (10) is expressed as follows.

  Based on this evaluation matrix, the mixing matrix M for discriminating the events A and B is derived by the correlation maximization method of the first embodiment and the covariance maximization method of the second embodiment. Then, for use in the discrimination process, reference mixed signals RA and RB corresponding to events A and B are calculated from the teacher signal using the derived mixing matrix M.

  An example of the result is shown in FIG. This is the learning process that is the first step. Next, in the discrimination process which is the second step, the mixed signal Y is derived from the mixing matrix M obtained by deriving the signal to be discriminated in the first step. The evaluation function for discrimination is

When D is sufficiently large, it is distinguished from event A, and when D is sufficiently small, it is determined as event B.

Claims (4)

A plurality of light irradiation units that irradiate light to a plurality of locations of the subject, a plurality of light detection units that detect the irradiated light through the subject, and a plurality of lights detected by the plurality of light detection units In a biological light measurement device including a calculation unit that calculates a signal,
For signals measured repeatedly more than twice
The biological light measurement device, wherein the calculation unit derives a mixing coefficient for mixing signals detected by the plurality of detection units on the basis that a similarity between signals obtained by repeatedly measuring the subject is maximized. The biological light measurement apparatus according to claim 1, wherein a correlation coefficient is used as the measure of similarity. The biological light measurement apparatus according to claim 1, wherein covariance is used as the similarity measure. The calculation unit, based on the criteria that the similarity between the same events is maximized and the similarity between the different events is minimized with respect to a signal obtained by repeatedly measuring changes related to different events. The biological light measurement apparatus according to claim 1, wherein a coefficient is derived.