JP3721980B2 - Brain function testing device - Google Patents

Description

【００６１】
一般に有意検定を行うとき、そのｐ値を０．０１以下又は０．０５以下のとき有意差があると検定することが多い。
【００６２】
表１に示すように、ｐ値に対して０．０５を閾値とすれば、図３で対象とした瞳孔反応の指標の中で潜時最大縮瞳速度到達時間、最大縮瞳加速度到達時間、初期瞳孔径、縮瞳量、縮瞳率、最大縮瞳速度、最大散瞳速度に健常高齢者群とＡＤ／ＳＤＡＴ群の間に統計的有意差がみられた。これらの中で最も統計的有意差が大きい指標はｐ値が０．０００２の縮瞳量である。ｐ値が小さければ小さいほど両群の差が大きいと考えられるので、以下では、両群の差について述べるとき、ｐ値をその差の大きさを表す指標として考える。
【００６３】
本発明の目的は、いくつかの指標を組み合わせることによって、よりｐ値が小さくなるような値を導出することである。先にも述べたように、ｐ値が小さいと両群の差は大きくなり、ある任意の被験者がどちらの群に属するかを判定するときの判定精度は向上すると期待できる。また健常群とＡＤ／ＳＤＡＴ群のどちらの群に属するかの判定のように結果がＡかＢかのように明確に分けられる判定方法だけでなく、どちらの群により近くてその距離はどの程度であるかなどの連続量としての結果を知ることにより今後の疾患発症可能性を予測することが期待でき、対策を施すことなどの対処法も考えることが可能になると期待できる。
【００６４】
以上に述べたように、本実施形態では複数の指標から１つの判別値を導出する。判別値の導出方法は多変量解析における判別分析における二次判別手法による（参考文献＝宮原英夫、丹後俊郎（編）「医学統計学ハンドブック」、朝倉書房、１９９５）。
【００６５】
図５は判別値ｗの計算方法例を示す。この図示例ではステップＳ１である任意の被験者Ｚに関するデータｚ＝（ｚ１，ｚ２，ｚ３…）、つまり複数の指標（ｚ１…）を用いて健常者群Ｘと患者群Ｙのそれぞれに対してマハラノビスの平方距離Ｄｘ２，Ｄｙ２を計算し、ステップＳ２でその差（Ｄｘ２−Ｄｙ２）を計算してその計算値を判別値ｗとする。この判別値ｗの値によってどちらの群に近いかがわかる。尚図中ｘ＝（ｘ１，ｘ２，ｘ３…）は健常者データを示し、ｘ１…は指標を、またｙ＝（ｙ１，ｙ２，ｙ３…）は健常者データを示し、ｙ１…は指標を示す。
【００６６】
図６に本実施形態の多変量演算手段２における判別値ｗの導出と、出力手段４の出力との関係を示す。
【００６７】
多変量演算手段２では、瞳孔指標として潜時、初期瞳孔径、縮瞳量、最大縮瞳速度、最大散瞳速度、最大縮瞳加速度、最大縮瞳速度到達時間、最大縮瞳加速度到達時間を用いている。
【００６８】
そして健常高齢者群５７名（Ｘ）とＡＤ／ＳＤＡＴ群５５名（Ｙ）の中から予めどちらの群に属しているかが判明している１名をサンプリングし、その１名の瞳孔指標と両群（Ｘ），（Ｙ）とのマハラノビスの平方距離Ｄｘ２，Ｄｙ２を求めて判別値ｗを計算する。
【００６９】
この操作を全てのサンプル（被験者）に対して実施し、その結果に基づいた判別値ｗのヒストグラムを出力手段４により健常高齢者群（Ｘ）と、ＡＤ／ＳＤＡＴ群（Ｙ）毎に表示する。出力手段４で表示されるヒストグラム中、上側のヒストグラムが健常高齢者５７名の判別値ｗで、下側のヒストグラムがＡＤ／ＳＤＡＴ群５５名の判別値ｗをそれぞれ示している。
【００７０】
ここで求めた平均判別値の差を示すｐ値は３．８２×１０^-7であり、表１で示される各指標のｐ値よりもかなり小さくなっていることがわかる。このようにいくつかの指標を１つの値に集約することにより、両群（Ｘ）（Ｙ）の有意差を大きくすることが可能となり、また出力される情報（この場合は図６のグラフ）も少なくなるので判断が容易になる。
【００７１】
図６では健常高齢者群（Ｘ）とＡＤ／ＳＤＡＴ群（Ｙ）の判別値ｗを示しているだけであるが、このグラフに当該被験者の判別値ｗを重畳させることにより、当該被験者がどちらの群（Ｘ），（Ｙ）に近いかを知ることが可能である。当該被験者の判別値ｗを求める式が
ｗ＝1475-15.175×(潜時)+0.661×(初期瞳孔径)+2.652×(縮瞳量)+0.618×(最大縮瞳速度)+0.215×(最大散瞳速度)+0.025×(最大縮瞳加速度)-14.404×(最大縮瞳速度到達時間)+27.83×(最大縮瞳加速度到達時間)
であり、各指標の１次式になっている。この式は一般に判別式又は判別関数と呼ばれているが、対象とする両群（Ｘ），（Ｙ）における考慮した指標の分散共分散行列が等しい場合は、上式に示すように１次式になる。
【００７２】
これに対し分散共分散行列が異なるときは、判別式は２次式になる。簡単化のため分散共分散行列を等しいと仮定するか否かは対象とする被験者群に応じて適宜決定すればよい。
【００７３】
図６で示す出力手段４の表示例は一例であって棒グラフで示したが、表示の仕方は様々に存在する。どのような表示方法であっても当該被験者の他の被験者に対する相対的位置が明示される方法であれば良く、本実施形態に特に限定されるものではない。
（実施形態２）
前記実施形態１では判別値ｗの計算結果に基づいて当該被験者の他の被験者に対する相対的位置が判るように出力手段４に表示することで、医者などが被験者の脳機能の判定を行うものであったが、本実施形態は図１に示される実施形態１の構成に、図７に示すように更に判定手段１４を加えたものである。判定手段１７は、データベース手段３より得られるデータに基づいて判定基準を作成する。判定基準は脳機能の判定を行う際の目安となるものである。当該被験者の判別値ｗ１とデータベース手段３によって保存されているデータ（判別値ｗ２）とを比較し、判定基準に基づいて当該被験者の脳機能の判定を行う。
【００７４】
図８に判定手段１４の判定例について示す。図８では上述のように求めた健常高齢者群（Ｘ）と、ＡＤ／ＳＤＡＴ群（Ｙ）の度数分布における０を判定基準として、判別値ｗ１が０より大きい場合は健常と判定し、判別値ｗ１が０より小さい場合は痴呆と判定する。判定結果は出力手段４を構成する表示装置に表示される。この場合の表示装置はディスプレイでもよいしプリンタでもよい．また音声でも判定結果を出力することが可能である。例えば音がある場合は痴呆（ＡＤ／ＳＤＡＴ）と判定し、音がない場合は健常とするなどである。
【００７５】
また判別基準としては必ずしもその値を０とする必要はない。全健常者に対して健常者を痴呆と誤判定する率、すなわち偽陽性率を小さくしたければ判定基準の値を負にすればよい。このように要求条件に応じて判定基準は変更可能である。判定基準を予め設定することにより、自動的な検査が可能となる。
【００７６】
以上では脳機能検査のうち疾患としてアルツハイマー型痴呆症（アルツハイマー病を含む）を取り上げたが、脳機能の老化度合い、自律神経系の活動度合い、又は脳血管性痴呆患者等他の痴呆症についても全く同様に検査・判定することが可能である。
【００７７】
特に脳血管性痴呆患者とアルツハイマー型痴呆患者は健常高齢者に対してわずかに潜時が大きくなっていることと、最大散瞳速度を用いることで、健常若年者及び健常高齢者と、脳血管性痴呆患者及びアルツハイマー型痴呆患者との弁別が可能であることと、最大縮瞳時間に関しては、健常若年者と他の被験者群との差は見られるが、健常高齢者、脳血管性痴呆患者及びアルツハイマー型痴呆患者の間には殆ど差がみられないこととを本発明者らは認識しており、これらの瞳孔指標に基づいて脳血管性痴呆患者を弁別することができるのは勿論である（特願２０００−０４７０１１号参照）。また老化度合いと自律神経系に関しては石川氏らの「普及型電子瞳孔計イリスコーダ（Ｃ−２５１４）について」（神経眼科、Vol10,No.2,pp.106-110、1993）に述べられている。
【００７８】
また前記実施形態１，２において多変量演算手段２で用いる瞳孔指標として、縮瞳時間、散瞳時間を加えても良く、またこの両者を組み合わせても良い。
【００７９】
また前記多変量演算手段２では判別分析を用いたが、次のように方法による多変量演算を行っても良い。
【００８０】
つまりその一の方法としては、図９に示すような例えば入力ユニット、中間ユニット、出力ユニットからなる３層構造のニューラルネットワークを用いる方法であり、この場合複数の指標から一つ、若しくは複数の値を算出し、指標とその指標による望ましい出力値の対をネットワークに与える学習課程で、ネットワーク構造を決定し、その学習で得られるネットワークに複数の指標を与えることにより出力値を得ることができる。また学習においては指標とその指標による望ましい出力値の対をネットワークに与えるが、このときネットワークの出力が望ましい出力値に近づくように結合重みの値の調整を行う。このような指標の提示を繰り返すことにより、最終的にはネットワークにどの指標を与えても望ましい出力値を示すようになる。
【００８１】
また別の方法としては、ファジィ理論を用いる方法である。例えば、指標に関する属性を考えると、「潜時が短い」若しくは反対に「潜時が長い」が考えられる。そして脳機能を検査する場合、「潜時が短い」且つ「最大縮瞳速度が速い」ような場合の方が、「潜時が短い」だけの場合や、「最大縮瞳速度」だけの場合よりも、脳機能が高い。このようにｎ個の属性に対して（２＾ｎ−１）通りの評価がある。ファジィ理論ではこのようなものをファジィ集合と呼ぶが、このようなファジィ集合を予め用意しておき、検査の際に、被験者から観測される指標から脳機能を判定するのである。
【００８２】
更に他の方法としては、エキスパートシステムを用いる方法である。例えば潜時が０．２ｍ秒より短い場合や、更に潜時が０．２５ｍ秒より長い場合等と言った具合に各指標に条件を設け、更にその条件が成立するときの結果を設定する。そして検査の際に、被験者から観察される各指標を夫々条件に当てはめて脳機能を判定するのである。
【００９０】
【発明の効果】
請求項１の発明は、被験者の瞳孔の大きさを検出して脳機能の検査を行う脳機能検査装置において、前記被験者の瞳孔の静特性又は動特性に関する指標を導出する瞳孔指標算出手段と、該瞳孔指標算出手段によって得られた瞳孔の静特性又は動特性に関する複数の指標を用いてより少ない指標に変換する多変量演算手段と、該多変量演算手段により得られる指標に関する情報を出力する出力手段とを備えたので、瞳孔対光反応という副作用のない簡易な方法で脳機能の検査が行え、脳疾患の早期診断や臨床診断などの分野に用いることができる脳機能検査装置を実現できるという効果がある。
【００９１】
請求項２の発明は、請求項１の発明において、前記多変量演算によって得られる被験者の瞳孔の静特性又は動特性に関する指標と比較するための基準となるデータを格納しているデータベース手段を具備しているので、健常者群又は患者群のどちらの群により近くてその距離はどの程度であるかなどの連続量としての結果を知ることにより今後の疾患発症可能性を予測することができる脳機能検査装置を実現することができる。
【００９２】
請求項３の発明は、請求項４の発明において、前記出力手段として、前記多変量演算手段より得られる当該被験者の指標と、前記データベース手段より得られるデータとを比較可能なように出力する出力手段を用いたので、出力手段で出力されるデータを比べることで、容易に当該被験者の脳機能の判定が行える。
【００９３】
請求項４の発明は、請求項１又は２の何れかの発明において、前記多変量演算手段より得られる当該被験者の指標と前記データベース手段に記憶されている基準となるデータとを比較して当該被験者の脳機能に関する判定を行う判定手段を具備してあるので、脳機能の判定を自動化した脳機能検査装置を実現できる。
【００９４】
請求項５の発明は、請求項１乃至４の何れかの発明において、前記瞳孔の静特性又は動特性に関する指標として、潜時、縮瞳時間、散瞳時間、初期瞳孔径、縮瞳量、縮瞳率、縮瞳速度、最大縮瞳速度、散瞳速度、最大散瞳速度、縮瞳加速度、最大縮瞳加速度、最大縮瞳速度到達時間、最大散瞳速度到達時間、最大縮瞳加速度到達時間の内の少なくとも２つの指標を用いる脳機能や脳疾患の有無の検査や判定が確実に行える。
【００９５】
請求項６の発明は、請求項１乃至４の何れかの発明において、前記瞳孔の静特性又は動特性に関する指標として、縮瞳時間と散瞳時間の両方を用いるので、縮瞳成分と散瞳成分の両方を単一の指標で考慮することが可能となる。
【００９６】
請求項７の発明は、請求項４の発明において、前記判定手段として、脳の老化度合い又は自律神経の活動度合い又は痴呆性又はアルツハイマー病の何れかの判定を行う判定手段を用いるので、特に脳の老化度合い又は自律神経の活動度合い又は痴呆性又はアルツハイマー病か否かの何れかの判定を自動化した脳機能検査装置を実現できる。
【００９７】
請求項８の発明では、請求項１乃至７の何れかの発明において、前記多変量演算手段は、判別分析、二次判別、ニューラルネットワーク、ファジィ理論、エキスパートシステムの何れかの多変量演算手法を用いているので、請求項１乃至７の効果が得られる脳機能検査装置を実現できる。
【図面の簡単な説明】
【図１】本発明の実施形態１の全体構成図である。
【図２】同上に用いる瞳孔指標を得るためにＣＣＤカメラにより撮像した瞳孔画像の例図である。
【図３】同上に用いる瞳孔指標を得るために被験者の瞳孔に光刺激を与えた場合の被験者の瞳孔径の変動の測定結果を示すグラフである。
【図４】（ａ）は同上に用いる瞳孔指標の一つである潜時に関する被験者（健常高齢者の場合）の分布を示すグラフである。（ｂ）は同上に用いる瞳孔指標の一つである潜時に関する被験者（アルツハイマー型痴呆高齢者の場合）の分布を示すグラフである。
【図５】同上に用いる判別値の計算方法の説明図である。
【図６】同上の多変量演算手段の判別値導出例と、出力手段の出力例との関係説明図である。
【図７】本発明の実施形態２の全体構成図である。
【図８】同上に用いる判定手段の説明図である。
【図９】ニューラルネットワークのモデル図である。
【符号の説明】
１ 瞳孔指標算出手段
２ 多変量演算手段
３ データベース手段
４ 出力手段
５ パーソナルコンピュータ
１０ 発光素子
１１ 撮像カメラ
１２ 画像処理回路
１３ 瞳孔指標計算部
２０，２１ 多変量解析計算機能
Ｍ 瞳孔
ｗ１，ｗ２ 判別値[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a brain function test method and a brain function test apparatus using pupillary reaction, and in particular, a brain function such as aging degree of autonomic nervous system disease, dementia, and Alzheimer's disease by subject's pupillary light reaction. Can check for disease Brain The present invention relates to a function inspection device.
[0002]
[Prior art]
It is generally said that the size of the pupil decreases with age (for example, Mr. Ishikawa et al., “About the popular electronic pupillometer Iriscorder (C-2514)”, Neuro-Ophthalmology, Vol. 10, No. 2, pp. 106 -110, 1993). The smooth muscle that regulates the size of the pupil is controlled by the autonomic nervous system, and the size of the pupil and the pupil response reflect the activity state of the autonomic nervous system. Although the cause is unknown, an association between Alzheimer's disease, which is believed to be closely related to brain function, and the pupil has been pointed out.
[0003]
LFMScinto and colleagues have reported that it is possible to diagnose Alzheimer's disease by measuring the magnification of the pupil before and after applying mydriatic (“A potential noninvasive neurobiological test for Alzheimer's disease”, Science, 266, pp.1051-1054, 1994). This method (hereinafter referred to as the instillation method) differs from the interview format such as the revised Hasegawa Simple Intelligence Evaluation Scale (HDS-R), which has been used in the conventional dementia assessment, and has objective data such as the size of the pupil. However, there is a problem that it takes about 30 minutes to measure and cannot be applied to some subjects with eye diseases. On the other hand, it has also been reported that the pupillary photoresponse is different between healthy subjects and dementia patients (Fumi et al., “Study on simple test method for dementia using pupillary photoreaction”, medical electronics and biotechnology) , Vol.36, No.3, pp.210-214, 1998). The pupillary light reaction has the advantage of being completed in a short time without any side effects compared to the eye drop method.
[0004]
[Problems to be solved by the invention]
However, in the pupillary light reaction described in the report of Mr. Fumi et al., Only three indicators of miosis rate, miosis time, and mydriatic rate are extracted, and the state of pupil change is sufficiently It's hard to say that you're capturing. In addition, the pupillary photoreaction is similar to other physiological indices, and there are large individual differences, and it is considered difficult to determine dementia and the degree of autonomic nervous system activity and brain function aging with only a few indices. It is done.
[0005]
As a solution to this problem, it is conceivable to increase an index indicating the state of pupil reaction. Although it can be expected that the determination accuracy is improved by increasing this index, on the other hand, it is very difficult to determine which index the determination result should be adopted when a determination result contradicting between the indexes is obtained. Also, there is an advantage that it is easier to make a judgment by using a small number of indices than by using a large number of indices.
[0006]
The present invention has been made in view of the above-mentioned problems, and its object is to focus on various indicators that characterize changes in the size of the pupil, and to perform multivariate computation on these multiple indicators. Can be used to easily test the degree of aging of the brain function, the degree of autonomic nervous system activity, the presence or absence of dementia, and Alzheimer's disease. Brain machine It is to provide a performance inspection device.
[0018]
[Means for Solving the Problems]
Claim 1 According to the invention, in the brain function test apparatus that detects the size of the pupil of the subject and tests the brain function, the pupil index calculation means for deriving an index related to the static characteristic or dynamic characteristic of the pupil of the subject, the pupil index Multivariate calculation means for converting to a smaller index using a plurality of indices related to the static or dynamic characteristics of the pupil obtained by the calculation means, and output means for outputting information about the index obtained by the multivariate calculation means It is characterized by having.
[0019]
Claim 2 In the invention of claim 1 According to the invention, there is provided database means for storing data serving as a reference for comparison with an index relating to the static characteristics or dynamic characteristics of the pupil of the subject obtained by the multivariate calculation.
[0020]
Claim 3 In the invention of claim 2 In the invention, the output means is an output means for outputting the subject's index obtained from the multivariate computing means and the data obtained from the database means so as to be comparable.
[0021]
Claim 4 In the invention of claim 2 Or 3 In the invention of claim 1, comprising a determination means for comparing the index of the subject obtained from the multivariate calculation means and the reference data stored in the database means to make a determination regarding the brain function of the subject. It is characterized by that.
[0022]
Claim 5 In the invention of claim 1 Thru 4 In any of the inventions, as an index relating to the static characteristics or dynamic characteristics of the pupil, latency, miosis time, mydriatic time, initial pupil diameter, miosis amount, miosis rate, miosis speed, maximum miosis speed Use of at least two indicators of mydriatic speed, maximum mydriatic speed, miosis acceleration, maximum miosis acceleration, maximum miosis speed arrival time, maximum mydriatic speed arrival time, and maximum mydriatic acceleration arrival time Features.
[0023]
Claim 6 In the invention of claim 1 Thru 4 In any one of the inventions, both the miosis time and the mydriatic time are used as an index related to the static characteristic or dynamic characteristic of the pupil.
[0024]
Claim 7 In the invention of claim 4 In the invention, the determination means is characterized by using a determination means for determining whether the brain is aging, the degree of autonomic nerve activity, dementia, or Alzheimer's disease.
[0025]
In the invention of claim 8, the claim of claim 1 Thru 7 In any one of the inventions, the multivariate calculation means uses any of multivariate calculation methods of discriminant analysis, secondary discrimination, neural network, fuzzy theory, and expert system.
[0026]
DETAILED DESCRIPTION OF THE INVENTION
The present invention will be described with reference to embodiments.
[0027]
(Embodiment 1)
FIG. 1 shows an overall configuration diagram in the present embodiment. The apparatus according to the present embodiment includes a pupil index calculation unit 1, a multivariate calculation unit 2, a database unit 3, and an output unit 4.
[0028]
The pupil index calculation means 1 is provided to calculate an index related to the static characteristic or dynamic characteristic of the pupil M, and is configured by a light emitting element 10, an imaging camera 11, an image processing circuit 12, and a pupil index calculation unit 13.
[0029]
The light emitting element 10 provides a light stimulus for inducing a light reaction to the pupil. Any light emitting element that emits visible light may be used. For example, a light emitting diode or a fluorescent tube that emits white light is used. Is possible. The imaging camera 11 is for photographing the pupil M of the subject and can be constituted by a CCD camera, a CMOS sensor, or the like. The image processing circuit 12 is means for extracting the pupil portion from the pupil image as shown in FIG. 2 captured by the imaging camera 11 and obtaining the size of the pupil M. The size of the pupil M may be a pupil area, a pupil diameter, or the like. In this embodiment, the pupil diameter is obtained as the size of the pupil M. Of course, using the pupil area does not impair generality. The pupil index calculation unit 13 is configured by the calculation function of the personal computer 5 that constitutes the multivariate calculation means 2 and the database means 3 described above, and various changes to be described later are made based on changes in the pupil diameter obtained from the image processing circuit 12. A pupil index related to static characteristics or dynamic characteristics is calculated and obtained.
[0030]
In FIG. 1, the imaging camera 11 and the image processing circuit 12 are used to obtain the pupil diameter. However, the pupil diameter can be obtained by photography or the like, and the present invention is limited to the embodiment. However, the means is not particularly limited.
[0031]
Here, there are generally a plurality of pupil indices obtained by calculation, and it is a feature of the present invention that they are used for calculation of multivariate analysis.
[0032]
The multivariate computing means 2 of the present embodiment selects discriminant values from various pupil indices by a discriminant analysis method in multivariate analysis. Usually, this discriminant value is often one, but by classifying the pupil index into several groups, one discriminant value is obtained for each group, resulting in a plurality of discriminants from all pupil indexes. It is also possible to obtain a value. In this embodiment, it is assumed that there is one discriminant value. Discriminant analysis based on multivariate analysis is a technique in which information of a plurality of indices is aggregated and converted into one discriminant value, the discriminant value is used as a representative value, and some determination is performed using the discriminant value. As another multivariate analysis method, there is a principal component analysis method. This is a method that converts multiple indicators into fewer indicators and aggregates many of the original indicators into less information, and can be said to be a generalized method of preprocessing other than the judgment of the previous discriminant analysis method. . Hereinafter, in this embodiment, a method for deriving a discriminant value based on the discriminant analysis method is used. However, the first principal component or a plurality of principal components in the principal component analysis method may be replaced with the discriminant value in this embodiment as a representative value. Of course, the present invention is not particularly limited to the embodiment.
[0033]
Regarding the multivariate analysis methods, ["Medical Statistics Handbook", Hideo Miyahara, Toshiro Word (Edit) (Asakura Shoten, 1995)], ["Introduction to Multivariate Data Analysis", Koichi Sugiyama, (Asakura Shoten, 1983) )], [“Nonlinear Multivariate Analysis—Neural Network Approach” (Asakura Shoten, 1996)] and the like.
[0034]
Now, as the function of the multivariate calculation means 2 used in the present embodiment, the subject is converted into a smaller number of indices by using a plurality of indices relating to the static characteristics or dynamic characteristics of the pupil M of the subject obtained by the pupil index calculation unit 13. The multivariate analysis calculation function 20 for calculating the discriminant value w1 of the subject and the pupil index of the large number of subjects obtained by the pupil index calculation unit 13 and stored in the database means 3 are called to perform the multivariate analysis calculation, thereby distinguishing the large number of subjects It comprises a multivariate analysis calculation function 21 for calculating the value w2.
[0035]
The multivariate calculation means 2 of the present embodiment is realized by the same personal computer 5 as the pupil index calculation unit 13, but may be realized by a separate personal computer or a DSP (digital signal) instead of a personal computer. The present invention can also be realized using a processor) or a microcomputer, and the specific implementation means of the present invention is not particularly limited to the embodiment.
[0036]
The database means 3 is an index representing individual variables related to the pupillary reaction, a discriminant value obtained by multivariate calculation using the index, and the age, sex, past disease, current disease, measurement date and time of the subject or other subjects. Accumulate and store all information including information DA including subject information and measurement environment information necessary for brain function tests, such as the location, environmental conditions such as illuminance and temperature at the measurement location, and results of intelligence tests, together with the pupil index. Therefore, in the embodiment, data such as the pupil index of the subject, the discrimination values w1 and w2, and information DA including the subject information and measurement environment information are registered. By accumulating data by the database means 3 in this way, more subject data are accumulated and a highly reliable database can be constructed.
[0037]
The database means 3 of the present embodiment is realized by the same personal computer 5 as the multivariate calculation means 2 and the pupil index calculation unit 13. However, like the multivariate calculation means 2, it must be realized by the same personal computer. There is no.
[0038]
The output means 4 is composed of a display device such as a display or a printer, and displays the subject's discriminant value w1 and other subject's discriminant value w2, and the subject's discriminant value w1 is the other subject's discriminant value w2. By comparison, it is possible to see what value is relatively shown. The discriminant value w2 of another subject is not limited to one discriminant value of another subject, but may be an average value of discriminant values of a plurality of subjects, or an average value of discriminant values of a specific subject group. May be. In other words, as the discriminant value of the subject to be compared with the discriminant value of the subject, any target other than the discriminant value of the subject may be used. Further, the pupil index of another subject may be a discriminant value obtained in the past of the subject. Although not shown in FIG. 1, not only the discriminant values w1 and w2 but also the pupil index and other data may be displayed on the display device constituting the output unit 4.
[0039]
Next, the appearance of the pupil M shown in FIG. 2, how the pupil diameter varies, and various indices relating to the pupil response will be described with reference to FIG. 3. From the pupil diameter variation, various indexes relating to the static characteristics or dynamic characteristics of the pupil M can be obtained. Here, the index relating to the static characteristics of the pupil M refers to an index that does not depend on light stimulation, and the initial pupil diameter corresponds to this in FIG.
[0040]
The irradiation time of the light stimulus in FIG. 3 is a flash light of 0.10 seconds. The lighting time of the flash light may be shorter or longer than 0.10 seconds, and is not particularly limited.
[0041]
As an index of the pupillary reaction, which will be described later, the latency is about 0.2 seconds to 0.3 seconds, and if the flash light is shorter than this, the measurement condition is set such that there is no longer light stimulation during the pupillary reaction. It is possible. Hereinafter, the pupil index and measurement data shown in this embodiment are all obtained under the condition that the duration of light stimulation is 0.10 seconds.
[0042]
As shown in FIG. 3, the pupillary response to flash light for a short time generally contracts slightly after the start of light stimulation (this is miosis), and expands after the pupil becomes the smallest (diffuses this). Called pupils). Various indices as shown in FIG. 3 can be defined for such pupil reaction. These indices include initial pupil diameter, latency, miosis time, mydriatic time, miosis amount, miosis rate, miosis speed, mydriatic speed, miosis acceleration, maximum miosis speed arrival time, maximum dilation. A pupil velocity arrival time, a maximum miosis acceleration arrival time, etc. can be considered. Among these, the initial pupil diameter is an index relating to static characteristics independent of the presence or absence of light stimulation, and the other is an index relating to dynamic characteristics induced by light stimulation.
[0043]
The initial pupil diameter may be the size of the pupil M at an arbitrary point in time before light stimulus irradiation. The time average of the size of the pupil M in an arbitrary time interval before light stimulus irradiation may be used as the initial pupil diameter. Further, even after light stimulation irradiation, the initial pupil diameter may be the pupil M size at an arbitrary time point before the start of miosis or the time average of the pupil M size at an arbitrary time interval.
[0044]
Latency is the time from the start of irradiation with a light stimulus until the appearance of a pupil response. That is, in general, even when a light stimulus is applied, the pupil M does not react immediately, and a slight time delay occurs. This is a delay due to photoelectric conversion to convert the light into an electrical signal for transmission through the optic nerve after it reaches the retina, a delay due to the secretion of a chemical called a neurotransmitter that transmits the gap between the nerve cells, and A delay occurs in the pupillary reaction due to accumulation of delays from the distal end of the efferent nerve to driving of the muscle called smooth muscle, which is an effector.
[0045]
The miosis time is a time interval from a certain time point during miosis in the pupillary reaction to a certain time point. Of the miosis time, the time from the start of miosis to the end of miosis is particularly referred to as the maximum miosis time. The contracted size from the initial pupil diameter to the end of the miosis is referred to as the miosis amount, but the time required to reduce the pupil from the start of miosis to 50% of the miosis amount is particularly referred to as the 50% miosis time. The same applies to the 10% miosis time and the 90% miosis time, and the term 10% -90% miosis time indicates the time interval from the time when the 10% miosis occurs to the time when the 90% miosis occurs. The miosis time is a general term for various times required for these miosis.
[0046]
Mydriatic time refers to the time interval from a certain point in the pupil to a certain point in the pupil response. The contracted size from the initial pupil diameter to the end of the miosis is called the miosis amount, and the time required to return to 50% of the miosis amount is particularly called the 50% mydriatic time. Mydriatic time is a general term for various times required for mydriasis.
[0047]
The time required for returning to 50% from the time when 50% of the miosis is reduced is referred to as a 50% -50% time interval. The 50% -50% time interval is an index that can be said to be a combination of both the miosis time and mydriatic time indices.
[0048]
The miosis amount is a contracted size from the initial pupil diameter to the end of the miosis.
[0049]
The miosis ratio is an amount obtained by dividing the miosis amount by the initial pupil diameter.
[0050]
The miosis speed is a change in the amount of miosis per unit time, but various miosis speeds can be defined depending on how the unit time is taken. The average miosis speed from the time of 10% miosis to the time of 90% miosis from the initial pupil diameter is 10% -90% miosis speed. The maximum miosis speed is the maximum miosis speed in the temporal change of the pupil diameter during the miosis. The value obtained by dividing the maximum pupillary velocity by the amount of miosis is called% miosis (reference: Riku Utsumi, Tetsuya Sugiyama, Yuji Miyashita, Shin Moriya, and Satoru Tokuoka: “% miosis amount,% velocity "Research on photoreaction by new analytical method", Bulletin of Japanese Ophthalmology, Vol42, No.2, pp.223-228.1991). This can be said to be an index of speed considering that there is a correlation between the maximum miosis speed and the amount of miosis. The miosis speed is a general term for speeds in the miosis that can be specifically defined.
[0051]
The mydriatic velocity is the amount of change in the amount of mydriatic per unit time, but various mydriatic velocities can be defined depending on how the unit time is taken, as in the case of the mydriatic velocity.
[0052]
The maximum mydriatic velocity is the maximum mydriatic velocity in the temporal change of the pupil diameter during mydriasis. The% mydriatic speed is exactly the same as the% mydriatic speed, which is the maximum mydriatic speed divided by the amount of miosis. The mydriatic velocity is a general term for the velocity in the miosis that can be specifically defined.
[0053]
The miosis acceleration is the amount of change in the miosis speed per unit time. Similar to the miosis speed, various miosis acceleration can be defined depending on the unit time. The maximum miosis acceleration is the maximum miosis acceleration in the temporal change of the pupil diameter during miosis.
[0054]
The maximum miosis speed arrival time is the time required to reach the maximum miosis speed from the start of miosis.
[0055]
The maximum mydriatic speed arrival time may be either the time required to reach the maximum mydriatic speed from the start of miosis or the time required to reach the maximum mydriatic speed from the start of mydriasis. Let's take the former definition.
[0056]
The maximum miosis acceleration arrival time is the time required to reach the maximum miosis acceleration from the start of miosis.
[0057]
In the definition of the index described above, the light stimulus is considered as the flash light, but step light that continues the lighting state with a constant light amount after the light lighting is also considered as the light stimulation condition. In the case of step light, the index relating to the pupil reaction shown in FIG. However, in that case, as the amount of light increases, it tends to be difficult to find a time point at which the pupil M becomes the smallest.
[0058]
FIG. 4A shows a histogram of the latency of 57 healthy elderly people, and FIG. 4B shows a histogram of the latency of 55 Alzheimer-type dementia patients (hereinafter simply referred to as AD / SDAT). The average age of healthy elderly is 74.7 years old and the standard deviation is 8.40 years old. The average age of AD / SDAT group is 77.1 years old and the standard deviation is 9.24 years old. There was no statistically significant difference between ages (p value: p = 0.167). Although there was a statistically significant difference between the average latencies of both groups (p = 0.0011), there is considerable overlap in the histograms of both groups, as shown in FIGS. I understand that.
[0059]
Table 1 shows the magnitude relationship between the average values of the two subject groups regarding the index relating to the static or dynamic characteristics of the pupil including the latency, and the value of the statistical significance level regarding the average value of each index in both groups.
[0060]
[Table 1]

[0061]
In general, when performing a significance test, it is often tested that there is a significant difference when the p-value is 0.01 or less or 0.05 or less.
[0062]
As shown in Table 1, when 0.05 is set as a threshold value for the p value, the latency maximum pupillary velocity arrival time, maximum pupillary acceleration acceleration arrival time among the pupil reaction indices targeted in FIG. There were statistically significant differences between the healthy elderly group and the AD / SDAT group in the initial pupil diameter, miosis amount, miosis rate, maximum miosis rate, and maximum mydriatic velocity. Among these, the index with the largest statistical significance is the amount of miosis with a p-value of 0.0002. Since it is considered that the smaller the p value, the larger the difference between the two groups. In the following, when describing the difference between the two groups, the p value is considered as an index representing the magnitude of the difference.
[0063]
An object of the present invention is to derive a value that makes the p-value smaller by combining several indices. As described above, when the p value is small, the difference between the two groups becomes large, and it can be expected that the determination accuracy when determining which group a given subject belongs to will be improved. Moreover, not only the determination method in which the result is clearly divided as A or B as in the determination of which of the healthy group and AD / SDAT group, but also how close is the distance to which group? By knowing the result as a continuous amount such as whether or not, it can be expected to predict the possibility of future disease development, and it can be expected that countermeasures such as taking countermeasures can also be considered.
[0064]
As described above, in the present embodiment, one discriminant value is derived from a plurality of indices. The method for deriving the discriminant value is based on the secondary discriminant method in discriminant analysis in multivariate analysis (reference literature = Hideo Miyahara and Toshiro Tango (ed.) “Medical Statistics Handbook”, Asakura Shobo, 1995).
[0065]
FIG. 5 shows an example of a method for calculating the discriminant value w. In this illustrated example, data z = (z1, z2, z3...) Relating to an arbitrary subject Z in step S1, that is, a Mahalanobis for each of the healthy group X and the patient group Y using a plurality of indices (z1...). Are calculated, and the difference (Dx2−Dy2) is calculated in step S2, and the calculated value is set as a discriminant value w. It can be seen which group is closer to the discriminant value w. In the figure, x = (x1, x2, x3...) Indicates healthy person data, x1... Indicates an index, y = (y1, y2, y3...) Indicates healthy person data, and y1. .
[0066]
FIG. 6 shows the relationship between the derivation of the discriminant value w in the multivariate calculation means 2 of the present embodiment and the output of the output means 4.
[0067]
In the multivariate calculation means 2, the latency, initial pupil diameter, pupil size, maximum pupil velocity, maximum pupil velocity, maximum pupil velocity, maximum pupil velocity, maximum pupil velocity arrival time, and maximum pupil velocity acceleration arrival time are used as pupil indices. Used.
[0068]
And sampling the one person it belongs to advance both groups is known from the healthy elderly group 57 patients (X) and AD / SDAT group 55 patients (Y), both the pupil indication of one person The discriminant value w is calculated by obtaining the Mahalanobis square distances Dx2, Dy2 with the groups (X), (Y).
[0069]
This operation is performed for all samples (subjects), and a histogram of the discriminant value w based on the result is displayed for each group of healthy elderly groups (X) and AD / SDAT groups (Y) by the output means 4. . In the histogram displayed by the output means 4, the upper histogram shows the discriminant value w of 57 healthy elderly people, and the lower histogram shows the discriminant value w of 55 AD / SDAT groups.
[0070]
The p-value indicating the difference between the average discrimination values obtained here is 3.82 × 10 ^-7 It can be seen that it is considerably smaller than the p value of each index shown in Table 1. By aggregating several indices into one value in this way, it becomes possible to increase the significant difference between the two groups (X) (Y), and the information to be output (in this case, the graph of FIG. 6). This makes it easier to make judgments.
[0071]
FIG. 6 only shows the discriminant value w of the healthy elderly group (X) and the AD / SDAT group (Y), but by superimposing the discriminant value w of the subject on this graph, It is possible to know whether it is close to the group (X), (Y). The formula for determining the discriminant value w of the subject is
w = 1475-15.175 x (latency) + 0.661 x (initial pupil diameter) + 2.652 x (miosis amount) + 0.618 x (maximum pupil velocity) + 0.215 x (maximum mydriatic velocity) + 0.025 x (maximum reduction) (Pupil acceleration) -14.404 × (Maximum pupillary velocity arrival time) + 27.83 × (Maximum pupillary acceleration arrival time)
It is a linear expression of each index. This formula is generally called a discriminant or discriminant function, but when the variance-covariance matrix of the index considered in both groups (X) and (Y) is equal, the first order as shown in the above formula It becomes an expression.
[0072]
On the other hand, when the variance-covariance matrix is different, the discriminant is a quadratic equation. Whether or not the variance-covariance matrix is assumed to be equal for simplification may be determined as appropriate according to the subject group.
[0073]
The display example of the output means 4 shown in FIG. 6 is an example and is shown by a bar graph, but there are various ways of display. Any display method may be used as long as the relative position of the subject with respect to the other subject is specified, and the present invention is not particularly limited to this embodiment.
(Embodiment 2)
In the first embodiment, a doctor or the like determines the brain function of the subject by displaying the output means 4 so that the relative position of the subject with respect to the other subject can be known based on the calculation result of the discriminant value w. However, in the present embodiment, a determination unit 14 is further added to the configuration of the first embodiment shown in FIG. 1 as shown in FIG. The determination unit 17 creates a determination criterion based on data obtained from the database unit 3. The criterion is a standard for determining brain function. The discriminant value w1 of the subject is compared with the data (discriminant value w2) stored by the database means 3, and the brain function of the subject is judged based on the judgment criteria.
[0074]
FIG. 8 shows a determination example of the determination unit 14. In FIG. 8, 0 is determined in the frequency distribution of the healthy elderly group (X) and the AD / SDAT group (Y) obtained as described above, and when the discriminant value w1 is greater than 0, it is determined to be healthy and discriminated. When the value w1 is smaller than 0, it is determined as dementia. The determination result is displayed on a display device constituting the output means 4. The display device in this case may be a display or a printer. In addition, the determination result can be output by voice. For example, when there is a sound, it is determined as dementia (AD / SDAT), and when there is no sound, it is determined to be normal.
[0075]
Further, it is not always necessary to set the value to 0 as a discrimination criterion. If the rate at which a healthy person is erroneously determined as dementia, that is, the false positive rate, can be reduced with respect to all healthy persons, the determination criterion value may be negative. In this way, the determination criteria can be changed according to the required conditions. By setting the determination criterion in advance, an automatic inspection can be performed.
[0076]
In the above, Alzheimer's type dementia (including Alzheimer's disease) is taken up as a disease in the brain function test. It is possible to check and judge in exactly the same way.
[0077]
In particular, patients with cerebrovascular dementia and patients with Alzheimer-type dementia have a slightly larger latency for healthy elderly people and use the maximum mydriatic velocity, so that healthy young people and healthy elderly people Although it is possible to discriminate between patients with senile dementia and patients with Alzheimer-type dementia and the maximum miosis time, there is a difference between healthy young people and other subject groups, but healthy elderly people, patients with cerebrovascular dementia The present inventors have recognized that there is almost no difference between patients with Alzheimer-type dementia, and it is of course possible to discriminate patients with cerebrovascular dementia based on these pupil indices. Yes (see Japanese Patent Application No. 2000-047011). The degree of aging and the autonomic nervous system are described in "Dissemination type electronic pupillometer Iriscorder (C-2514)" (Neurology, Vol. 10, No. 2, pp. 106-110, 1993). .
[0078]
In addition, as the pupil index used by the multivariate calculation means 2 in the first and second embodiments, a miosis time and a mydriatic time may be added, or both may be combined.
[0079]
Although the multivariate calculation means 2 uses discriminant analysis, multivariate calculation may be performed by the following method.
[0080]
That is, as one of the methods, for example, a method using a three-layer neural network including an input unit, an intermediate unit, and an output unit as shown in FIG. In the learning process in which the network and the desired output value pair based on the index are calculated, the network structure is determined, and the output value can be obtained by giving a plurality of indices to the network obtained by the learning. In learning, a pair of an index and a desired output value based on the index is given to the network. At this time, the value of the connection weight is adjusted so that the output of the network approaches the desired output value. By repeating the presentation of such an index, a desired output value is finally shown regardless of which index is given to the network.
[0081]
Another method is to use fuzzy theory. For example, when considering attributes related to indicators, “latency is short” or, conversely, “latency is long” can be considered. And when examining brain function, when “latency is short” and “maximum miosis speed is fast”, when “latency is short” or only “maximum miosis speed” Than brain function. Thus, there are (2 ^ n-1) evaluations for n attributes. Such a fuzzy set is called a fuzzy set in the fuzzy theory, but such a fuzzy set is prepared in advance, and the brain function is determined from an index observed by the subject at the time of examination.
[0082]
Yet another method is to use an expert system. For example, a condition is set for each index such as when the latency is shorter than 0.2 milliseconds, or when the latency is longer than 0.25 milliseconds, and a result when the condition is satisfied is set. During the examination, the brain function is determined by applying each index observed by the subject to each condition.
[0090]
【The invention's effect】
Claim 1 In the brain function testing apparatus for detecting the size of the pupil of the subject and examining the brain function, the pupil index calculating means for deriving an index related to the static characteristic or dynamic characteristic of the pupil of the subject, the pupil index Multivariate calculation means for converting to a smaller index using a plurality of indices related to the static or dynamic characteristics of the pupil obtained by the calculation means, and output means for outputting information about the index obtained by the multivariate calculation means Because it is equipped, the brain function can be examined by a simple method without the side effect of pupillary light reaction, and there is an effect that it is possible to realize a brain function testing apparatus that can be used in fields such as early diagnosis and clinical diagnosis of brain diseases. .
[0091]
Claim 2 The invention of claim 1 In the invention of the above, since it comprises database means for storing data serving as a reference for comparison with an index relating to the static characteristic or dynamic characteristic of the pupil of the subject obtained by the multivariate calculation, a group of healthy subjects or patients By knowing the result as a continuous quantity such as which group is closer and the distance is, it is possible to realize a brain function test apparatus that can predict the possibility of future disease development.
[0092]
Claim 3 The invention of claim 4 In the invention, since the output means is an output means for outputting the subject's index obtained from the multivariate calculation means and the data obtained from the database means so as to be comparable, the output means outputs the data. By comparing the obtained data, it is possible to easily determine the brain function of the subject.
[0093]
Claim 4 The invention of claim 1 or 2 In any one of the inventions, comprising: a determination unit that compares the subject's index obtained from the multivariate calculation unit and the reference data stored in the database unit to determine the brain function of the subject Therefore, it is possible to realize a brain function test apparatus that automatically determines the brain function.
[0094]
Claim 5 The invention of claim 1 Thru 4 In any of the inventions, as an index relating to the static characteristics or dynamic characteristics of the pupil, latency, miosis time, mydriatic time, initial pupil diameter, miosis amount, miosis rate, miosis speed, maximum miosis speed , Brain function using at least two indicators of mydriatic speed, maximum mydriatic speed, miosis acceleration, maximum miosis acceleration, maximum miosis speed arrival time, maximum mydriatic speed arrival time, and maximum mydriatic acceleration arrival time It is possible to reliably check and determine the presence or absence of brain disease.
[0095]
Claim 6 The invention of claim 1 Thru 4 In any of the inventions, both the miosis and mydriatic time are used as an index relating to the static characteristics or dynamic characteristics of the pupil, so that both the miotic and mydriatic components are considered with a single index. Is possible.
[0096]
Claim 7 The invention of claim 4 In the invention, since the determination means uses a determination means for determining either the degree of brain aging or the degree of autonomic nerve activity, dementia or Alzheimer's disease, in particular, the degree of brain aging or the degree of autonomic nerve activity or It is possible to realize a brain function test apparatus that automates the determination of whether dementia or Alzheimer's disease.
[0097]
Claim 8 In the invention of claim 1 Thru 7 In any one of the inventions, the multivariate calculation means uses any of multivariate calculation methods of discriminant analysis, secondary discrimination, neural network, fuzzy theory, and expert system. 1 Thru 7 It is possible to realize a brain function testing apparatus that can achieve the above effects.
[Brief description of the drawings]
FIG. 1 is an overall configuration diagram of a first embodiment of the present invention.
FIG. 2 is an example diagram of a pupil image captured by a CCD camera in order to obtain a pupil index used in the above.
FIG. 3 is a graph showing measurement results of fluctuations in the pupil diameter of a subject when optical stimulation is applied to the pupil of the subject in order to obtain the pupil index used in the above.
FIG. 4A is a graph showing the distribution of subjects (in the case of healthy elderly people) related to latency, which is one of the pupil indices used in the above. (B) is a graph showing the distribution of subjects (in the case of elderly people with Alzheimer-type dementia) related to latency, which is one of the pupil indices used in the above.
FIG. 5 is an explanatory diagram of a discriminant value calculation method used in the above.
FIG. 6 is a diagram for explaining the relationship between the discrimination value derivation example of the multivariate computing means and the output example of the output means.
FIG. 7 is an overall configuration diagram of Embodiment 2 of the present invention.
FIG. 8 is an explanatory diagram of determination means used in the above.
FIG. 9 is a model diagram of a neural network.
[Explanation of symbols]
1 Pupil index calculation means
2 Multivariate calculation means
3 Database means
4 Output means
5 Personal computer
10 Light emitting element
11 Imaging camera
12 Image processing circuit
13 Pupil index calculation part
20, 21 Multivariate analysis calculation function
M pupil
w1, w2 discriminant value

Claims (8)

In a brain function test apparatus that detects a size of a pupil of a subject and tests a brain function, the pupil index calculation means for deriving an index related to the static characteristics or dynamic characteristics of the pupil of the subject, and the pupil index calculation means A multivariate calculation means for converting to a smaller index using a plurality of indices related to the static or dynamic characteristics of the pupil, and an output means for outputting information about the index obtained by the multivariate calculation means. Characteristic brain function testing device. 2. The brain according to claim 1 , further comprising database means for storing data serving as a reference for comparison with an index relating to static characteristics or dynamic characteristics of the pupil of the subject obtained by the multivariate calculation. Functional inspection device. As the output means, said the index of the subject obtained from multivariate calculation means, according to claim 2, characterized by using an output means for outputting to allow comparison with the data obtained from said database means Brain function testing device . It comprises a judging means for making a judgment on the brain function of the subject by comparing the index of the subject obtained from the multivariate computing means with reference data stored in the database means. The brain function testing device according to claim 2 or 3 . As an index related to the static or dynamic characteristics of the pupil, latency, miosis time, mydriatic time, initial pupil diameter, miosis amount, miosis rate, miosis speed, maximum miosis speed, mydriatic speed, maximum mydriasis The at least two indices among pupil speed, miosis acceleration, maximum miosis acceleration, maximum miosis speed arrival time, maximum mydriatic speed arrival time, and maximum miosis acceleration arrival time are used. 4. The brain function testing device according to any one of 4 . The brain function test apparatus according to any one of claims 1 to 4, wherein both the miosis time and the mydriatic time are used as an index related to the static characteristics or dynamic characteristics of the pupil . 5. The brain function test apparatus according to claim 4 , wherein as the determination means, a determination means for determining whether the brain is aging, the degree of autonomic nerve activity, dementia or Alzheimer's disease is used . Wherein the multivariate calculation means, discriminant analysis, quadratic discriminant, neural networks, fuzzy logic, according to any one of claims 1 to 7, characterized in that it uses one of the multivariate calculation method of the expert system Brain function testing device.

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