Abstract:
Provided is technology for blood clotting reactions capable of analyzing a blood clotting reaction with a high degree of precision, by precisely detecting and removing noise, regardless of the location where the noise is generated in the light intensity data. This automated analyzer approximates, with an approximation curve, time series data for transmitted light intensity or scattered light intensity of light emitted onto a sample, and, in this process, removes abnormal data points that deviate from the approximation curve (see  FIG. 2 ).

Description:
TECHNICAL FIELD 
       [0001]    The present invention relates to techniques for analyzing components included in blood-derived samples. In particular, the present invention relates to techniques for analyzing blood-clotting reactions. 
       BACKGROUND ART 
       [0002]    Blood clotting test is performed for various purposes such as for identifying clinical conditions of coagulation fibrinolytic system, for diagnosing DIC (disseminated intravascular coagulation), for checking effects of thrombus treatments, or for diagnosing hemophilia. Conventionally, blood clotting test has been performed by visually identifying fibrin precipitation which is a final point of blood clotting reaction. However, after 1960&#39;s, blood clotting analysis devices which are developed for improving test throughputs and precisions have been used in usual tests. 
         [0003]    In detecting fibrin precipitations by blood clotting analysis devices, methods such as electric resistance detection., optical detection, or mechanical methods are used, in particular, optical detection is a contactless method in which samples do not touch with foreign objects in clotting reactions. Thus optical detection is widely used. There are two types of optical detections; a transmitted light detection in which, variation of transmitted light, is measured when aggregated substances are generated in reaction containers; and a scattered light detection in which scattered Sight is detected. Both methods analyze the detected chronological light intensity to calculate clotting time. Various methods have been proposed so far. 
         [0004]    In the technique described in Patent Literature 1 listed below : times T1 and T2, at which the most recently acquired signals become X-times and Y-times (0&lt;X&lt;Y&lt;1) larger respectively, are searched by scanning from the most recently acquired signals toward the initially acquired signals ; polynomial regression, analysis is performed with respect to the signals from the time T1 to the time T2; and the clotting time is calculated from the acquired approximated curve. Accordingly, the unevenness of data is removed, thereby attempting to accurately calculate the clotting time. 
       CITATION LIST 
     Patent Literature 
       [0005]    Patent Literature 1: JP Patent (Koukoku) No. H07-82020 B2 (1995) 
       SUMMARY OF INVENTION 
     Technical Problem 
       [0006]    Optical detection calculates the clotting time by analyzing chronological light intensities acquired from immediately after the reagent is mixed with the sample. However, during the reaction, especially immediately after the initiation of the reaction, it is highly likely that noises are included in the acquired light intensity and erroneous clotting, times may be calculated. 
         [0007]    In Patent Literature 1 above, data before the time T1, which may include noises, is excluded from the test target and data within the range between the times T1 and T2 only is provided to the test, thereby attempting to solve the problem. However, if a noise occurs at a stage where aggregated substances are well generated, for example, the noise is included between the times T1 and T2. Accordingly, erroneous approximated curves and erroneous clotting times may be calculated. 
         [0008]    The present invention is made in the light of the above-described technical problems. It is an objective of the present invention to provide a technique for precisely detecting and removing noises regardless of the locations where noises occur in the light intensity data, thereby analyzing blood clotting reactions with high precision. 
       Solution to Problem 
       [0009]    An automated analysis device according to the present invention approximates, using an approximated curve, chronological data of transmitted light or of scattered light generated by light irradiated onto a sample. During that process, the automated analysis device removes abnormal data points that are departed from the approximated curve. 
       Advantageous Effects of Invention 
       [0010]    With the automated analysis device according to the present invention, it is possible to precisely detect and remove noises occurring in measured data during blood clotting reactions, thereby analyzing blood clotting reactions with high precision. 
         [0011]    Technical problems, configurations, and effects other than those mentioned above will be apparent with reference to the embodiments below. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0012]      FIG. 1  is a configuration diagram of an automated analysis device according to an embodiment 1. 
           [0013]      FIG. 2  is a diagram showing a process How in which a controller  121  detects and removes a noise in light intensity data to calculate a blood clotting time. 
           [0014]      FIG. 3  is a diagram showing a processing image when detecting a noise data point in step S 50 . 
           [0015]      FIG. 4  is a diagram showing an output screen example of an operational computer  118 . 
           [0016]      FIG. 5  is a diagram showing a process flow in which the controller  121  calculates a blood clotting time in an embodiment 2. 
           [0017]      FIG. 6  is a diagram showing a processing image when detecting a noise data point in the embodiment 2. 
           [0018]      FIG. 7  is a diagram showing a condition after processes of step S 40 , S 110 , S 120 , S 60 , and S 30  are applied. 
           [0019]      FIG. 8  is a diagram showing a condition after repeating detections and removals of noise data points and calculations of an approximated curve  340 . 
           [0020]      FIG. 9  is a diagram showing a process flow in which the controller  121  calculates a blood clotting time in an embodiment 3. 
           [0021]      FIG. 10  is a diagram in which a difference between chronological light intensity data  330  and the approximated curve  340  shown in  FIG. 6  is calculated and is plotted. 
           [0022]      FIG. 13  is a diagram showing a condition after repeating detections and removals of noise data points and calculations of an approximated curve  340 . 
       
    
    
     DESCRIPTION OF EMBODIMENTS  
     Embodiment 1: Device Configuration 
       [0023]      FIG. 1  is a configuration diagram of an automated analysis device according to an embodiment 1 of the present invention. Since functionalities of each of parts are commonly known, detailed descriptions will be omitted. 
         [0024]    The automated analysis device according to the embodiment 1 is configured so that a sample in a sample container  103  located in a sample disc  102  rotating leftward or rightward is suctioned using a sample dispenser  101  and is discharged into a reaction container  104 . The sample dispenser  101  performs suctioning actions and discharging actions along with actions of a sample syringe pump  105 . A reagent dispenser  106  is configured to suction the sample in a sample container  108  located in a sample disc  107  and to discharge the sample into the sample container  104 . The reagent dispenser  106  includes a reagent heater  109  within it. The reaction container  104  is held up from a reagent container stock  111  by a rotating reaction container carrier  112 , is moved rotationally, and is located in a reagent container locator  114  in a detector  113 . The reagent container locator  114  is provided with a recess so that the reagent container  104  may be placed on it. The reagent container  104  may be inserted into the recess. At least, one of the reagent container locator  114  and at least one of the detector  113  are provided. The reagent container carrier  112  carries and places the reagent container  104 . 
         [0025]    The measurement flow will be described below. Analysis items to be analyzed for each of the samples are inputted from an input unit  120  such as keyboards or from an operational computer  118 . A controller  121  controls operations of the detector  113 . The sample dispenser  101  suctions the sample in the sample container  103  located in the sample disc  102 , and dispenses the sample into the reaction container  104  placed on the reaction container locator  114  in the detector  113 . The reagent dispenser  106  suctions the reagent from the reagent container  108  located in the reagent disc  107 . The reagent beater  109  heats the reagent up to an appropriate temperature. The reagent dispenser  106  dispenses the reagent into the reaction container  104 . After the reagent is discharged, the blood clotting reaction begins immediately. 
         [0026]    A light source  115  irradiates light onto the reaction container  104 . A detector  116  detects, using such as photo diodes, scattered light or transmitted light caused by reactive solutions in the reaction container. The measured light signal is captured by the controller  121  as chronological light intensity data through an A/D converter  122  and through an interface  123 . The controller  121  uses the light intensity data to calculate the clotting time. The calculated result is printed out by a printer  124  through the interface  123  or is outputted on a display of the operational computer  118 , and is stored in a storage unit  119  implemented by RAMs or hard discs. The reaction container  104  after measuring the light is held by the reaction container carrier  112  and is discarded into a reaction container disposer  117 . 
         [0027]    The controller  121  may be configured using hardware such as circuit devices implementing the functionalities. Alternatively, the controller  121  may be configured using arithmetic devices such as CPU (Central Processing Unit) executing software implementing the functionalities. The storage unit  119  may be configured using storage devices such as hard discs. The printer  124 , the display of the operational computer  118 , and the storage unit  119  correspond to the output unit in the embodiment 1. 
       Embodiment 5: Device Operation 
       [0028]    Hereinafter, detailed, process of the controller  121  will be described. The controller  121  detects and removes noises unique to blood clotting reactions from the chronological light intensity data, and calculates the clotting time with high precision. Hereinafter, a noise characteristic of blood clotting reaction will be described first. Then details of the processing sequence will be described. 
         [0029]    Blood clotting reaction has a characteristic that it relatively begins rapidly after mixing the sample with the reagent. However, at the initial stage of the reaction, bubbles or other related particles are sometimes dragged into the mixture liquid. It can be assumed that the noise occurring in. the light intensity data is caused due to a temporal variation of the measured light intensity when these bubbles or particles pass through the measured region. 
         [0030]    In the light of the above-described circumstances, it is anticipated that the noise occurring in the light intensity data has a characteristic that the light intensity varies when the bubbles or the particles enter the measured region and that the light intensity returns back to the original value when the bubbles or the particles exit from the measured region. Therefore, when plotting the light intensity data with the vertical axis as the light intensity and with the horizontal axis as the time of data acquisition, the plotted, shape of the noise portion has an upward or downward convex. In scattered light detection, the amount of scattered light is temporally increased when the light pass through, the bubbles or the particles. Thus the noise shape during reaction has an upward convex. 
         [0031]    At the time when the measurement begins, disturbances of fluid flow or bubbles may occur due to impacts of mixing the reagent. In such cases, the light intensity data originally includes noises. Thus the data shape once varies into a recessed shape and then a convex noise appears. These examples of noises will be described later with reference to  FIG. 3 . 
         [0032]    In the light, of these noise characteristics, the controller  121 : calculates an approximated curve of the chronological light intensity data; compares the chronological light intensity data with the approximated curve; and detects data departed from the approximated curve only as noise data. Accordingly, it is possible to detect and remove initial noises with convex or recess shapes described above from the chronological light intensity data, thereby highly precisely calculating the blood clotting time. 
         [0033]      FIG. 2  is a diagram showing a process flow in which the controller  121  detects and removes a noise in the light intensity data to calculate a blood clotting time. Hereinafter, each step in  FIG. 2  will be described. 
         [0000]    ( FIG. 2 : step S 10 ) 
         [0034]    Through the A/D converter  122  and through the interface  123 , the controller  121  reads, as the chronological light, intensity data, light/electric current conversion data detected by the detector  116  at a predetermined interval. The controller  121  monitors the acquired chronological light intensity data. When the blood clotting reaction stops, the process proceeds to step S 20 , Commonly known methods may be used as the method for determining whether the clotting reaction has stopped. For example, it can be determined whether the clotting has stopped by methods such as: setting a threshold for the measuring time of the chronological light intensity data; or setting a threshold of newest light intensity or of amount of variation in light intensity. 
         [0000]    ( FIG. 2 : step S 20 ) 
         [0035]    The controller  121  selects and acquires, among approximation functions previously stored in the storage unit  119  that approximate temporal variation of light intensity, an optimal approximation function adapted to the combination of the test items and the reagent. For example, the combination of the test, items and the reagent and corresponding approximation function are previously defined, and the optimal approximation function may be automatically selected according to the definition. Logistic function shown in Equation 1, below may be used as the approximation function, for example. In Equation 1, t represents time and y represents light intensity, ymax, yrange, α1, and α2 are parameters, 
         [0000]        y=y max− y range/(1+exp(α1( t −α2))   Equation 1
 
         [0000]    ( FIG. 2 : step S 30 ) 
         [0036]    The controller  121  calculates parameters in the approximation function, so that the difference between the approximated, curve of time-light intensity described by the approximation function selected in step S 20  and the actual chronological light intensity data becomes small as far as possible. For example, the controller  121  determines the parameters in the approximation function so that the square error between the chronological light intensity data and the light intensity calculated by the approximation function becomes small as far as possible. For example, least square method may be combined with steepest descent method and with newton method as the method for calculating the parameters. An approximated curve that approximates the light intensity data most precisely will be acquired in this step. In other words, the approximation function stored in the storage unit  119  works as an initial value for calculating the approximated curve indicating the light intensity data. 
         [0000]    ( FIG. 2 : step S 40 ) 
         [0037]    The controller  121  calculates, for each point of the chronological light intensity data, the error between the chronological light intensity data and the approximated curve calculated in step S 30 . The error in this step is not calculated as an absolute value but as a value having positive or negative values. For example, the difference between both of the data is calculated by subtracting the light intensity calculated by the approximated curve from the chronological light intensity data. 
         [0000]    ( FIG. 2 : step S 50 ) 
         [0038]    The controller  121  compares the chronological light intensity data with the approximated curve calculated in step S 30 . The controller  121  detects data points departed from the approximated curve (in scattered light detection, data points above the approximated curve) as noise data points. For example, for each of the data points with positive errors calculated in step S 40 , the error is compared with a predetermined threshold. If the error is at or above the threshold, the corresponding data point is detected as noise data point. Noises such as convex-shaped noises or recess-shaped noises may be detected in this step. 
         [0039]    ( FIG. 2 : step S 60 ) 
         [0040]    The controller  121  removes the noise data points detected in step S 50  from the chronological light, intensity data. The noise data points may be actually removed from the chronological light intensity data. Alternatively, a flag indicating whether the data point is a. noise may be assigned for each of the data points and the flag data may be stored in the storage unit  119 , thereby managing the noise data points. 
         [0000]    ( FIG. 2 : step S 70 ) 
         [0041]    The controller  121  calculates the blood clotting time using at least one of: the chronological light intensity data after removing the noise data points acquired in step S 60 ; the approximated curve calculated in step S 30 . Both of the light intensity data after removing the noise data and the approximated curve provide some indicators about the actual blood clotting reaction. Thus any one of them may be used depending on the purpose of analysis or on. the required accuracy. Any commonly known method may be used, as the method for calculating the clotting time. For example, a differential data may be calculated by calculating a difference between adjacent data points of the chronological light intensity data after removing the noise data, and the peak position of the differential data may be calculated as the clotting time. 
         [0000]    ( FIG. 2 : step S 80 ) 
         [0042]    The controller  121 , outputs to the operational computer  118  and to the printer  124  and stores in the storage unit  119 , the information such as: fundamental information such as sample numbers or test items: the chronological light intensity data acquired in step S 10 : the approximated curve calculated in step S 30 ; the noise data points detected in step S 50 ; or the blood clotting time calculated in step S 70 . 
         [0043]      FIG. 3  is a diagram showing a processing image when detecting a noise data point in step S 50 . The horizontal axis  310  represents the time progress from the start of reaction. The vertical axis  320  represents the light intensity. The dotted curve  330  represents data, points schematically showing the chronological light intensity data. The solid line  340  represents the approximated curve calculated in step S 30 . 
         [0044]    The dotted curve  330  includes a noise data with recessed shape immediately after the start of measurement (dotted line p-q portion). In addition, at the initial stage of reaction, the clotted curve  330  includes a noise data with convex shape due to such as bubbles (dotted line r-s). By comparing the approximated curve  340  with the light intensity data  330 , the noise data points departed from, the approximated curve  340  (in  FIG. 3 , only the noises above the approximated curve  340  are detected) may be detected. 
         [0045]      FIG. 4  is a diagram showing an output screen example of the operational computer  118 . The screen shown in  FIG. 4  includes: a fundamental information display area  410  showing fundamental information such as sample numbers, test items, or analysis results (blood clotting time); and a graph display area  420  showing the chronological light intensity data of the selected sample and showing the approximated curve calculated in step S 30 . The graph display area  420  may display the light intensity data itself measured by the detector  113 . Alternatively, the graph display area  420  may display the light intensity data after removing the noise data points. Further, the graph display area  420  may display the equation of the approximation function used in calculating the approximate line. 
         [0046]    The controller  121  may store, in the storage unit  119 , data such as: the data calculated in the process of  FIG. 2 ; or the light intensity data in progress of removing the noise data points. Further, the controller  121  may display the data regarding the progress of the process on the screen shown in  FIG. 4 . 
       Embodiment 1: Modified Example 
       [0047]    In the description above, the controller  121  performs the process shown in  FIG. 2 . However, other functional units may perform the process. For example, software implementing the same process is provided in the operational computer  118 , and the computer  118  may perform the process instead of the controller  121 . It applies to the embodiments below. 
         [0048]    In the description above, the approximation function, the data in progress, or the processed result, are stored in the storage unit  119 . However, these data may be stored in other functional units. For example, these data may be stored in a storage unit in the operational computer  118 . 
         [0049]    In step S 20 , Equation 1 is used as the approximation function. However, the approximation function which may be used in the present invention is not limited to Equation 1. Various types of growth function with growth curve may be used. The growth, function mentioned here is a function that has a shape in which; the amount of variation with respect to time is small at initial stage; the amount, of variation is gradually increased; and the amount of variation is decreased again at later stages. Examples of such growth function include logistic function, Gompertz function, or Hill function. Other functions may be used such as: a function in which parts of terms in the above-described growth functions are exponentiated; a function in which a non-linear equation with respect to time is multiplied to or added to the above-described growth functions; or a function in which inputted values of the above-described growth functions are nonlinearly converted by nonlinear equations. Multi-term functions may be used as the nonlinear equations. 
         [0050]    In step S 50 , in order to remove noise data points with upward convex shape, the noise data points above the approximated curve are removed. When analyzing blood clotting reactions according to transmitted light, it is necessary to remove noise data points with downward convex shape. In this case, the data points below the approximated curve are detected as noise data points. The basis on which the data points are removed is the same as that in step S 50 . 
         [0051]    In step S 80  and in  FIG. 4 , the clotting reaction curve indicated by the chronological light intensity data and the approximated curve of step S 30  are outputted in the same graph. However, these data may be outputted in different graphs separately. In addition, these data may be outputted with different colors so that the noise data points can be easily distinguished. Alternatively, the shape of data points may be modified. 
       Embodiment 1: Summary 
       [0052]    As discussed thus far, the automated analysis device according to the embodiment 1: calculates the approximated curve of the chronological light intensity data; and compares the chronological light intensity data with the approximated curve, thereby detecting the data above or below the approximated curve only as noise data points. Accordingly; it is possible to detect and remove convex-shaped noises unique to blood clotting reactions or recess-shaped noises at the time starting the measurement. As a result, it is possible to highly precisely calculate the blood clotting time. 
       Embodiment 2 
       [0053]    In an embodiment 2 of the present invention, an operational example for removing noise data points will be described which is different from that of the embodiment 1. The configurations of the automated analysis device are the same as those of the embodiment 1. Thus the difference regarding the operation of the controller  121  will be mainly described. 
         [0054]    In the embodiment 2, the controller  121  repeats: a first process for calculating the approximated curve of the chronological light intensity data; a second process for detecting noise data points using the approximated curve; and a third process for removing the noise data points. In general, with respect to data that always includes a certain amount of noise, it is possible to reproduce the original data without noise by calculating an approximated curve once. However, the noise unique to blood clotting reaction occurs at limited positions with asymmetric shapes with respect to increasing direction of data and to decreasing direction of data. Thus it is likely that the precise approximated curve cannot be acquired only by calculating the approximated curve once. Thus in the embodiment 2, the controller  121  repeats the first-third processes to detect and remove noise data points gradually, thereby calculating the precise approximated curve. Accordingly, it can be assumed that the blood clotting time is highly precisely calculated. 
         [0055]      FIG. 5  is a diagram showing a process flow in which the controller  121  calculates a blood clotting time in the embodiment 2. In the process flow shown in  FIG. 5 , the same step numbers are assigned for the same steps described in  FIG. 2  of the embodiment 1. Namely, steps S 10 -S 40  and steps S 60 -S 80  are same as those of  FIG. 2  and thus detailed descriptions are omitted here. In the embodiment 2, steps S 110  and S 120  are introduced between steps S 40  and S 70 . Step S 60  is branched from step S 120 . The process returns to step S 30  after step S 60 . 
       (FIG. 5: Step S 110 ) 
       [0056]    The controller  121  detects data points that are departed from the approximated curve calculated in step S 30 . For example, the controller  121 : compares a predetermined threshold with the error for each of data points with positive errors calculated in step S 40 ; and detects data points with errors at or above the threshold as noise data points. 
       (FIG. 5: Step S 120 ) 
       [0057]    The controller  121  determines whether the current approximated curve is ideal with respect to the chronological Sight intensity data. For example, the controller  121 : compares the number of noise data points detected in step S 110  with a preconfigured threshold; and determines whether the approximated curve is precisely estimated on the basis of whether the number of noise data points is at or above the preconfigured threshold. If the approximated curve is precisely estimated, the process proceeds to step S 70 . Otherwise the process proceeds to step S 60 . 
         [0058]    Hereinafter, examples of detecting and removing noises in the embodiment 2 will be described using  FIGS. 6-8 . 
         [0059]      FIG. 6  is a diagram showing a processing image when detecting a noise data point in the embodiment 2. The reference signs in  FIG. 6  represent the same meaning as those in  FIG. 3 . In  FIG. 6 , there are noise data points above the approximated curve. 
         [0060]      FIG. 7  is a diagram showing a condition after processes of step S 40 , S 110 , S 120 , S 60 , and S 30  are applied to the chronological light intensity data  330  and to the approximated curve  340  in  FIG. 6 . In  FIG. 7 , the noise data points of the chronological light intensity data  330  are removed by step S 60 . In addition, the approximated curve  340  is updated in the second execution of step S 30 . 
         [0061]      FIG. 8  is a diagram showing a condition after repeating detections and removals of noise data points and calculations of the approximated carve  340  until it is determined in step S 120  that the number of noise data points is at or below the threshold. All noise data points are detected and removed from the chronological light intensity data  330 . The approximated curve  340  almost matches with the chronological light intensity data  330  after removing the noise data points. 
         [0062]    Comparing  FIG. 8  with  FIG. 6 , the initial rise of waveform and noise potions are removed from the chronological light intensity data  330  by repeating detections and removals of noise data points and calculations the approximated curve. In addition, the approximated curve  340  in  FIG. 8  is an ideal approximated curve that interpolates the region where the noise data points are removed from the chronological light intensity data  330 . 
       Embodiment 2: Modified Example 
       [0063]    In step S 110 , in order to remove the noises with upward convex shape, noise data points are detected considering the positive and negative errors, as in the embodiment 1. However, when analyzing blood clotting time according to scattered light, it is necessary to remove noise data points with downward convex shape. Alternatively, both of noise data points above and below the approximated curve may be detected as noise data points. In this case, a threshold may be set with respect to an absolute difference between the approximated curve and the chronological data points. If the absolute error exceeds the threshold, the corresponding data point, may be handled as a noise data point. 
         [0064]    In step S 120 , it is determined whether the approximated curve is precisely estimated by comparing the number of data points with the threshold. However, the basis of determination in step S 120  is not limited to it. For example, a threshold may be configured with respect to the proportion of number of noise data points to the total number of the chronological light intensity data points. Alternatively, a threshold may be configured with respect to a standard deviation of the error. 
       Embodiment 2: Summary 
       [0065]    As discussed thus far, the automated analysis device according to the embodiment 2 repeats: a first process for calculating the approximated curve of the chronological light intensity data; a second process for detecting noise data points using the approximated curve; and a third process for removing the noise data points, thereby detecting and removing the noise data points gradually. Accordingly, it is possible to calculate an ideal approximated curve that interpolates the region where the noise data points are removed, thereby highly precisely calculating the blood clotting time. 
       Embodiment 3 
       [0066]    In an embodiment 3 of the present invention, an operational example for removing noise data points will be described which is different from those of the embodiments 1-2. The configurations of the automated analysis device are the same as those of the embodiments 1-2. Hereinafter, the difference regarding the operation of the controller  121  will be mainly described. 
         [0067]    In the embodiment 3, the controller  121  configures, using the inputted chronological light intensity data, the threshold in detecting noise data described in the embodiments 1-2. Accordingly, the controller  121  attempts to improve the accuracy for detecting and removing the noise data. 
         [0068]      FIG. 9  is a diagram showing a process flow in which the controller  121  calculates a blood clotting time in the embodiment 3. In the process flow shown in  FIG. 9 , the same step numbers are assigned for the same steps described in  FIG. 2  of the embodiment 1. Namely steps S 10 -S 40  and steps S 60 -S 80  are same as those of  FIG. 2  and thus detailed descriptions are omitted here. In the embodiment 3, steps S 210 -S 240  are introduced between steps S 40  and S 70 . Step S 60  is branched from step S 240 . The process returns to step S 30  after step S 60 . 
       (FIG. 9: Step S 210 ) 
       [0069]    The controller  121  selects a subset of the chronological light intensity data as a low noise range, and calculates a standard deviation S of the errors within the subset. The low noise range selected in this step is a range where it is assumed that the chronological light intensity data does not include large noises. For example, in blood clotting reactions, it is likely that large noises occur immediately after the start of reaction. Therefore, the low noise range may be determined by such as: assuming that the reaction start time of the acquired chronological light intensity data is t_start and that the reaction end time is t_end, the center of those times t_middle (t_middle=(t_start+t_end)/2) is calculated; and the data after the time t_middle may he selected as the low noise range. 
       (FIG. 9: Step S 220 ) 
       [0070]    The controller  121  determines an acceptable error T for noise detection using the standard deviation S calculated in step S 210 . T may be determined according to Equation 2 below, for example. In Equation 2, K represents a predefined constant parameter that adjusts sensitivity for detecting noise data points. For example, if the dispersion of data within the low noise range is a normal distribution, the parameter K=3.0 detects data departed from the approximate line within the low noise range at sensitivity of about 0.3%. 
         [0000]        T=K*S    Equation 2
 
       (FIG. 9: Step S 230 ) 
       [0071]    The controller  121  detects data points departed from the approximated curve using the acceptable error T calculated in step S 220 . For example, for each of the data points with a positive error calculated in step S 40 , the controller  121  compares the error of each data point calculated in step S 40  with the acceptable error T, and detects data points with errors at or above the threshold as noise data points. 
       (FIG. 9: Step S 240 ) 
       [0072]    The controller  121  determines whether the current approximated curve is ideal with respect to the chronological light intensity data. For example, the controller  121 : compares the number of noise data points detected in step S 230  with a preconfigured threshold; and determines whether the approximated curve is precisely estimated on the basis of whether the number of noise data points is at or above the threshold. If the approximated curve is precisely estimated, the process proceeds to step S 70 . Otherwise the process proceeds to step S 60 . 
       Embodiment 3: Advantageous Effect 
       [0073]    In general, the chronological light intensity data includes the three components below. (1) original chronological variations of blood clotting reaction, (2) micro noises that are globally derived from measuring environments, (3) local noises that are derived from hubbies and particles when stirring. 
         [0074]    The noise of (2) is a noise derived from various environments such as: a noise derived from hardware such as the detector  116  or the A/D converter  122 ; or a noise derived from the reactive solution in the reaction container. The noise of (2) is typically symmetrical with respect to the data increasing and decreasing directions and its amplitude is small. Thus it is possible to, by calculating the approximated curve, reproduce the original data in which the noise is removed. 
         [0075]    On the other hand, the noise of (3) is asymmetric with respect to the data increasing and decreasing directions and its amplitude is large. Thus it is not possible to reproduce the original data in which the noise is removed even if the approximated curve is calculated. Therefore, it can be expected to acquire a highly precise approximated curve by removing the noise of (3) before calculating the approximated curve. However, when using the noise data point detection described in the embodiments 1-2, both of the noises (2) and (3) may be detected depending on the threshold. Thus the data points may be excessively removed or the noise may not be removed sufficiently. 
         [0076]    Thus in the embodiment 3, step S 220  configures the threshold according to the chronological light intensity data at the later stage in which it is not likely that the noise of (3) is included. The noise Is detected using this threshold. This threshold is configured according to the variation of (2), which corresponds to a steady noise component. By configuring an appropriate value of K (e.g. 3.0) in Equation 2, it is possible to detect data points related to the noise of (3) only. A specific example will be described using  FIG. 10 . 
         [0077]      FIG. 10  is a diagram, in which a difference between the chronological light intensity data  330  and the approximated curve  340  shown in  FIG. 6  is calculated and is plotted. The horizontal axis  310  represents the same meaning as that of  FIG. 3 . The vertical axis  1010  represents an error between the chronological light intensity data  330  and the approximated curve  340 . The dotted curve  1020  plots the error between the chronological light intensity data  330  and the approximated curve  340 . The reference sign  1030  represents a low noise range configured in step S 210 . The reference sign  1040  represents the threshold T configured in step S 220 . 
         [0078]    In  FIG. 10 , the dotted curve a-b, the dotted curve c-d, and the dotted curve e-f are chronological data points that are determined as noises by comparing with the threshold T. By configuring the threshold T according to light intensity data at the later stage, it is possible to detect local noises only that are derived from such as bubbles or particles. 
         [0079]    The automated analysis device according to the embodiment 3 not only detects the local noises as described, with  FIG. 10  but also improves the accuracy for determining the end of repetition. This advantageous effect will be described below. 
         [0080]    At the initial stage of repetition, the noise of (3) is not completely removed. Thus the approximated curve is not precisely calculated and the data point is not precisely approximated in some cases even within the low noise range. In this case, the threshold configured on the basis of the standard deviation of errors within the low noise range does not. precisely reflect the variations of noise (2). Thus the threshold only detects data points with large amplitudes among the noises (3). However, as the repetition proceeds and as the noise (3) is removed, the approximated curve gradually approaches the original data after the noise is removed. At this time, while the noise (2) still remains, the threshold configured by using the low noise range gradually approaches an optical threshold that is capable of removing the noise (3) only. By determining the threshold for determining the end of repetition using the method of the embodiment 3, it is possible to appropriately determine the end of repetition without excessively removing data points or without leaving the noise. A specific example will be described using  FIG. 11 . 
         [0081]      FIG. 11  is a diagram showing a condition after repeating detections and removals of noise data points and calculations of an approximated curve  340  with respect to the error between the chronological light intensity data  330  and the approximated curve  340  in  FIG. 10 . The reference signs in  FIG. 11  represent the same meanings as those of  FIG. 10 . 
         [0082]    At the initial stage of repetition (the state of  FIG. 10 ), the initial portion of reaction in the chronological light intensity data  330  includes the noise of (3), and there are a lot of data points above the threshold T. At the later stage of repetition (the state of  FIG. 11 ), the noise (3) is removed and the approximated curve reproduces the original chronological light intensity data in which the noise is removed. Thus the error variations at the initial stage are equivalent to the error variations at the later stage. At this time, the threshold  1040  precisely reflects the variation of noise (2). Therefore, by determining whether the noise (3) exists using this threshold, it is possible to appropriately determine the end of repetition without excessively removing data points or without leaving noises. This method for determining the end of repetition may be used in the embodiment 2. 
       Embodiment 3: Modified Example 
       [0083]    In step S 210 , data after the time t_middle of the acquired chronological light intensity data is selected as the low noise range. However, the low noise range may be selected on other basis. For example, a threshold may be configured with respect to the light intensity value of the acquired chronological Sight intensity data, and data with light intensity value at or above the threshold may be selected. Alternatively, a plurality of low noise ranges is configured; standard deviations of errors between the chronological data and the approximated curve are calculated for each of the low noise ranges; one of the standard deviations may be selected as a standard deviation of noise of the low noise range, or a standard deviation of noise of the low noise range may be calculated using the plurality of standard deviations. In addition, the method for selecting the low noise range may be changed for each of the test items. Further, the method for selecting the low noise range may be changed for each of the repetition. 
         [0084]    In step S 230 , in order to remove the noises with upward convex shape, noise data points are detected considering the positive and negative errors, as in the embodiment 1. However, when analyzing blood clotting time according to scattered light, it is necessary to remove noise data points with downward convex shape. Alternatively, both of noise data points above and below the approximated curve may be detected as noise data points. In this case, a threshold may be set with respect to an absolute difference between the approximated curve and the chronological data points. If the absolute error exceeds the threshold, the corresponding data point may be handled as a noise data point. 
         [0085]    In step S 240 , it is determined whether the approximated curve is precisely estimated by comparing the number of data points with the threshold. However, the basis of determination in step S 240  is not limited to it. For example, a threshold may be configured with respect to the proportion of number of noise data points to the total number of the chronological light intensity data points. Alternatively, a threshold may be configured with respect to a standard deviation of the error. In addition, those thresholds may be configured according to the standard deviation calculated in step S 210 . 
       Embodiment 3: Summary 
       [0086]    As discussed, thus far, the automated analysis device according to the embodiment 4 improves the accuracy for determining the end of repetition by configuring the threshold according to the chronological light intensity data at the later stage. In addition, as described with  FIG. 10 , it is possible to remove local noises only while leaving steady micro noises. 
         [0087]    The present invention is not limited to the embodiments, and various modified examples are included. The embodiments are described in detail to describe the present invention in an easily understood manner, and the embodiments are not necessarily limited to the embodiments that include all configurations described above. Part of the configuration of an embodiment can be replaced by the configuration of another embodiment. The configuration of an embodiment can be added to the configuration of another embodiment. Addition, deletion, arid replacement of other configurations are also possible for part of the configurations of the embodiments. 
       REFERENCE SIGNS LIST 
       [0088]      101 : sample dispenser 
         [0089]      102 : sample disc 
         [0090]      103 : sample container 
         [0091]      104 : reaction container 
         [0092]      105 : sample syringe pump 
         [0093]      106 : reagent dispenser 
         [0094]      107 : reagent disc 
         [0095]      108 : reagent container 
         [0096]      109 : reagent heater 
         [0097]      111 : reagent container stock 
         [0098]      112 : reagent container carrier 
         [0099]      113 : detector 
         [0100]      114 : reagent container locator 
         [0101]      115 : light source 
         [0102]      116 : detector 
         [0103]      117 : reagent container disposer 
         [0104]      118 : operational computer 
         [0105]      119 : storage unit 
         [0106]      120 : input unit 
         [0107]      121 : controller 
         [0108]      122 : A/D converter 
         [0109]      123 : interface