JP4543594B2 - Brain function test apparatus and brain function test system - Google Patents

Description

【００３８】
ここで上述の各検査によって得られた眼球運動を評価する指標や、神経心理学検査で用いる指標は一般に複数あるが、それらを組み合わせて多変量解析の計算に用いるのが本発明の特徴である。
【００３９】
本実施形態の多変量演算手段３は、多変量解析における判別分析手法によって、眼球運動検査手段１及び神経心理検査手段２から入力された種々の指標より判別値を選出する。この判別値は通常一つであることが多いが、入力された指標をいくつかのグループに分類して、各グループに各々一つの判別値を求めることによって、結果的に全部の指標から複数の判別値を求めることも可能である。本実施形態では判別値が一つであることとする。多変量解析による判別分析は、複数の指標の情報を集約して、一つの判別値に変換し、その判別値を代表値とし、さらにその判別値を用いて何らかの判定を行う手法である。この他の多変量解析手法としては、主成分分析手法がある。これは複数の指標から、より少ない指標へと変換し、元来ある多くの指標からより少ない情報に集約する手法であり、先の判別分析手法の判定以外の前処理を一般化した手法と言える。以下本実施形態では、判別分析手法を基に判別値を導出する方法を用いるが、主成分分析手法における第１主成分又は複数の主成分を代表値として本実施形態における判別値に置き換えても勿論良く、本発明は実施形態に特に限定されるものではない。
【００４０】
尚多変量解析手法に関しては、［「医学統計学ハンドブック」、宮原英夫、単語俊郎（編）（朝倉書店、１９９５）］、［「多変量データ解析入門」、杉山高一、（朝倉書店、１９８３）］、［「非線形多変量解析−ニューラルネットによるアプローチー」（朝倉書店、１９９６）］等を参照する。
【００４１】
さて本実施形態に用いる、多変量演算手段３の機能としては眼球運動計算部１３で求めた被験者の眼球運動に関する指標、及び、正否・得点計算部２２で求めた神経心理学検査で用いる指標を用い、より少ない指標に変換して当該被験者の判別値ｗ１を計算する多変量解析計算部３０と、眼球運動計算部１３及び正否・得点計算部２２で求めてデータベース４に格納した多数の被験者の指標を呼び出して多変量解析計算を行い、多数の被験者の判別値ｗ２を計算する多変量解析計算部３１とからなる。
【００４２】
尚本実施形態の多変量演算手段３は眼球運動計算部１３及び正否・得点計算部２２と同一のパーソナルコンピュータ６で実現しているが、夫々別のパーソナルコンピュータで実現してもよいし、パーソナルコンピュータでなくＤＳＰ（デジタルシグナルプロセッサ）やマイクロコンピュータを用いても実現可能であり、本発明の具体的実現手段としては実施形態に特に限定されるものではない。
【００４３】
データベース４は、眼球運動に関する個々の変量を表す指標、神経心理学検査で用いる指標、これらの指標を用いて多変量演算によって得られる判別値、更に当該被験者又は他の被験者の年齢、性別、既往症、現在の疾患、測定日時、測定場所、測定場所の照度や温度等の環境条件、知能テストの結果等、脳機能検査に必要な被験者情報や計測環境情報からなる情報を含めた全ての情報をデータとして指標とともに蓄積格納するもので、実施形態においては、当該被験者の眼球運動指標及び神経心理指標や、判別値ｗ１、ｗ２、更に当該被験者の情報や計測環境情報からなる情報などのデータ登録するようになっている。このようにデータベース４によりデータを蓄積することによって、より多くの被験者のデータが蓄積され信頼性の高いデータベースを構築できるようなっている。
【００４４】
尚本実施形態のデータベース４は多変量演算手段３及び眼球運動計算部１３と同一のパーソナルコンピュータ６で実現しているが、多変量演算手段３と同様に必ずしも同一のパーソナルコンピュータで実現する必要はない。
【００４５】
出力手段５はディスプレイや、プリンタ等の表示装置から構成され、当該被験者の判別値ｗ１と、他の被験者の判別値ｗ２を表示し、当該被験者の判別値ｗ１が他の被験者の判別値ｗ２と比較して相対的にどのような値を示しているかが判るようになっている。ここで、出力手段５による表示方法としては、グラフなどを利用して図示することも可能であるし、単純に数値のみを表示するようにしても良い。他の被験者の判別値ｗ２としては他の被験者の一つの判別値だけでなく、複数の被験者の判別値の平均値であっても良く、またある特定の被験者群の判別値の平均値であっても良い。つまり当該被験者の判別値と比較を行いたい被験者の判別値としては、当該被験者の判別値以外であればどのような対象でも良い。更に他の被験者の指標としては、当該被験者の過去に得られた判別値であっても良い。また図１では示していないが、出力手段５を構成する表示装置には判別値ｗ１，ｗ２だけでなく、指標そのものやその他のデータを表示しても良い。
【００４６】
本発明の目的は、眼球運動を評価する指標と神経心理学検査に用いる指標と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。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。また健常群と患者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。
【００４７】
上述し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）。
【００４８】
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。したがって、当該被験者の判別値ｗが健常者群Ｘに近い距離に位置するならば健常者と判定し、患者群Ｙに近い距離に位置するならば患者と判定するなどの判定処理を行うこともできる。また、これらの距離の比例配分をとることによって、疾患の確率というような単一の数値として表示することもできる。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。
【００４９】
このように、多変量演算手段３で多変量演算処理を行い、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も少なくなるので判断が容易になる。
【００５０】
（実施形態２）
図１１に本実施形態にかかる脳機能検査システムのシステム構成を示す。本システムは、被験者の脳機能を検査する脳機能検査装置７と、脳機能検査装置７にインターネット５０（通信ネットワーク）を介して接続されるサーバ８とで構成され、サーバ８を運営する運営者は本システムを用いて脳機能検査サービスを提供する。尚、サーバ８を運営する運営者は脳検査事業の運営も兼ねているが、サーバ管理と脳検査事業とを別々の事業者で行っても良い。
【００５１】
脳機能検査装置７は、眼球運動検査手段１と、神経心理検査手段２と、出力手段５とで構成される。
【００５２】
眼球運動検査手段１は、水平方向および垂直方向の眼球運動を評価する指標を算出するために設けたもので、発光素子１０、撮像カメラ１１、画像処理回路１２、眼球運動計算部１３、映像表示部１４により構成される。
【００５３】
発光素子１０は、暗環境下においても明瞭な眼球映像が得られるようにするための照明であり、例えば発光波長が人間に感知されない赤外線波長領域にある赤外光ＬＥＤを用いれば、発光素子１０の光が被験者に感知されることはなく、被験者の不安を軽減することができる。
【００５４】
また撮像カメラ１１は被験者の眼球Ｍを撮影するためのものであり、ＣＣＤカメラやＣＭＯＳセンサ等により構成することが可能である。
【００５５】
画像処理回路１２は撮像カメラ１１で撮像された図２に示すような画像から眼球部分を抽出し、眼球Ｍの運動を求めている。すなわち、画像処理回路１２で撮像カメラ１１の画像を画像処理することによって、眼球Ｍの水平位置および垂直位置が求められる。そして、眼球運動計算部１３が、画像処理回路１２により得られた眼球Ｍの位置データを逐次計算することによって、眼球Ｍの水平方向における運動速度及び運動加速度、垂直方向における運動速度及び運動加速度などの指標を計算して求めている。ここで、眼球運動計算部１３はパーソナルコンピュータ６の演算機能により構成される。
【００５６】
映像表示部１４は例えばディスプレイ装置からなり、後述する視覚認知検査や運動機能検査を行う際に脳の視覚皮質の活動状態を反映する視覚系機能のテスト行う際に、検査用の映像を被験者に提示するためのものである。
【００５７】
一方、神経心理検査手段２は、被験者に対して神経心理学検査を行い、神経心理学検査で用いる複数の指標を導出するために設けたもので、入力部２１と、正否・得点計算部２２と、課題提示部２３とで構成される。
【００５８】
上述のように神経心理学検査としては種々のものが痴呆症の検査に利用されており、神経心理検査手段２では、課題提示部２３が表１に示すような問答やアンケートをディスプレイ装置からなる出力手段５に表示させ、被験者に対して回答を促す。被験者は、キーボードやマウスや音声入力装置からなる入力部２１を用いて回答を入力し、この回答をもとに正否・得点計算部２２が被験者の知能を示す得点を計算する。ここに、パーソナルコンピュータ６には、被験者に対して提示した問答やアンケートに対する回答から得点を計算するプログラムが組み込まれており、上述した正否・得点計算部２２はパーソナルコンピュータ６の演算機能により構成される。
【００５９】
パーソナルコンピュータ６の通信機能により構成される出力手段（第１の出力手段）は、眼球運動計算部１３及び正否・得点計算部２２により算出された指標をインターネット５０を介してサーバ８に送信する。また、パーソナルコンピュータ６の通信機能により構成される受信手段は、サーバ８からインターネット５０を介して送信された当該被験者の判別値ｗ１や、他の被験者の判別値ｗ２などのデータを受け取る。
【００６０】
出力手段５は、ディスプレイ装置やプリンタなどの表示装置によって構成され、パーソナルコンピュータ６が受け取った当該被験者の判別値ｗ１と、他の被験者の判別値ｗ２を表示し、当該被験者の判別値ｗ１が他の被験者の判別値ｗ２と比較して相対的にどのような値を示しているかが判るようになっている。尚、他の被験者の判別値ｗ２としては他の被験者の一つの判別値だけでなく、複数の被験者の判別値の平均値であっても良く、またある特定の被験者群の判別値の平均値であっても良い。つまり当該被験者の判別値と比較を行いたい被験者の判別値としては、当該被験者の判別値以外であればどのような対象でも良い。更に当該被験者の判別値ｗ１と比較する判別値としては、当該被験者の過去に得られた判別値であっても良い。また、出力手段８を構成する表示装置には判別値ｗ１，ｗ２だけでなく、指標そのものやその他のデータを表示しても良い。
【００６１】
一方、サーバ４０は多変量演算手段３とデータベース４とを有し、パーソナルコンピュータなどで構成される。
【００６２】
サーバ４０の通信機能で構成される受信部は、脳機能検査装置７からインターネット５０を介して送信された指標を受信し、またサーバ４０の通信機能で構成される送信部（第２の出力手段）は、多変量演算手段３で求めた当該被験者の判別値Ｗ１や多数の被験者の判別値Ｗ２をインターネット５０を介して脳機能検査装置７に送信する。
【００６３】
多変量演算手段３は、眼球運動検査手段１の眼球運動計算部１３で求めた被験者の眼球運動に関する複数の指標、及び、正否・得点計算部２２で求めた神経心理学検査で用いる複数の指標を用いてより少ない指標に変換し、当該被験者の判別値ｗ１を計算する多変量解析計算部３０と、眼球運動計算部１３及び正否・得点計算部２２で求めてデータベース４に格納した多数の被験者の眼球運動指標及び神経心理指標を呼び出して多変量解析計算を行い、多数の被験者の判別値ｗ２を計算する多変量解析計算部３１とからなる。
【００６４】
データベース４はサーバ８の記憶装置に構築されており、当該被験者を含む複数の被験者の眼球運動指標と神経心理指標、及び、これらの指標を用いた多変量解析計算により得られる判別値ｗ１，ｗ２、更には当該被験者を含む複数の被験者の識別ＩＤ、例えばＩＰアドレスのような通信ネットワーク上の通信連絡先、年齢、性別、既往症、現在の疾患、測定日時、測定場所、測定場所の照度や温度等の環境条件、知能テストの結果等、脳機能検査に必要な被験者情報や計測環境情報からなる情報が格納されている。なお、本実施形態においては当該被験者の指標データやその情報をデータベース４に新規データとして追加するようになっており、脳機能検査を行う毎にその検査結果が新規データとしてデータベース４に蓄積されるので、より多くの被験者のデータが蓄積され、信頼性の高いデータベース４を構築できるようになっている。ここに、データベース４により、被験者情報を記憶する被験者情報記憶手段や、多変量演算によって得られる指標と比較するための基準となるデータを記憶する判定基準記憶手段が構成される。
【００６５】
本システムで脳機能検査を行う場合、検査場所に配置された眼球運動検査手段１を用いて検査者又は被験者自身が検査を行うと、眼球運動計算部１３が眼球の運動を評価する指標を算出するとともに、正否・得点計算部２２が神経心理学検査で用いる指標を算出する。そして、パーソナルコンピュータ６のキーボードのような入力装置を用いて当該被験者に割り当てられた識別ＩＤを入力すると、パーソナルコンピュータ６は当該被験者の識別ＩＤとその測定データをインターネット５０を介してサーバ８に送信する。一方、サーバ８では、インターネット５０を介して脳機能検査装置７から送信された被験者の識別ＩＤ及びその測定データを受信し、受信したデータを多変量解析計算部３０が多変量演算してより少ない指標に変換する。次に、サーバ８は、多変量演算によって得られる指標と比較するための基準となるデータをデータベース４から読み出して、当該被験者の指標と基準となる指標とを比較して比較結果を作成する。その後、サーバ８は、当該被験者の識別ＩＤをもとにデータベース４から当該被験者のＩＰアドレスを読み出して、当該被験者の指標と基準となる指標とを比較した比較結果を表示したＨＴＭＬファイルを作成し、このＨＴＭＬファイルをインターネット５０を介してパーソナルコンピュータ６に送信する。そしてパーソナルコンピュータ６ではサーバ８から送信されたＨＴＭＬファイルを受信し、その比較結果をディスプレイ装置やプリンタなどからなる出力手段８に出力させており、出力手段８の表示から検査者或いは被験者が検査結果を容易に読み取ることができる。
【００６６】
ここに、脳機能検査装置７から送信された眼球運動検査手段１及び神経心理検査手段２の測定データを多変量演算手法を用いてより少ない指標に変換する多変量演算手段３と、多変量演算によって得られる指標とデータベース４に記憶されたデータを比較して比較結果を求める比較手段とは、サーバ８を構成するコンピュータの演算機能により実現される。また、複数の被験者の識別ＩＤと通信ネットワーク上の通信連絡先とを含む被験者情報が記憶された被験者情報記憶手段と、多変量演算によって得られる指標と比較するための基準となるデータを記憶する比較基準記憶手段とはサーバ８を構成するコンピュータの記憶装置により実現される。また、通信ネットワークを介して送信された被験者ＩＤと当該被験者の測定データを受信する受信手段と、被験者ＩＤをもとに被験者情報記憶手段から当該被験者の通信連絡先を読み出し、通信ネットワークを介して通信連絡先に比較手段の比較結果を送信する送信手段とは、サーバ８を構成するコンピュータの通信機能により実現される。そして、サーバ８の記憶装置には、コンピュータに上述の動作を実行させるための動作プログラムが予め記憶されている。
【００６７】
ところで、上述した各実施形態の多変量演算手段３では判別分析を用いたが、次のような方法による多変量演算を行っても良い。
【００６８】
つまりその一の方法としては、図１２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。
【００６９】
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、「固視時間が長い」若しくは反対に「固視時間が短い」が考えられる。そして脳機能を検査する場合、「固視時間が短い」且つ「固視回数が少ない」ような場合の方が、「固視時間が短い」だけの場合や、「固視回数が少ない」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。
【００７０】
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。例えば固視時間が時間ｔ１より短い場合や、更に固視時間が時間ｔ２（＞ｔ１）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。
【００７１】
また、上述した各実施形態では、眼球運動検査手段１により得られた眼球運動指標と、神経心理検査手段２により得られた神経心理指標の両方を多変量演算手段３の入力として多変量演算を行っているが、眼球運動指標又は神経心理指標の何れかを多変量演算手段３の入力として多変量演算を行うようにしても良く、多変量演算を行ってより少ない指標に変換し、変換された指標を用いて脳機能の検査を行うことによって、より精度の高い脳機能検査を行うことができる。また、眼球運動以外の生体データ（例えば血液中のタンパクの量や脳髄液の成分などの計測データ）を眼球運動指標又は神経心理指標に組み合わせて、多変量演算手段３の入力としても良く、このような生体データを組み合わせて脳機能の検査を行うことにより、より精度の高い脳機能の検査を行うことができる。
【００７２】
【発明の効果】
上述のように、請求項１の発明は、被験者の眼前に配置された映像表示部と、被験者が探索結果を入力するための入力装置と、入力装置を用いて入力された情報をもとに、被験者の眼球の運動を評価する複数の指標を求める眼球運動検査手段と、前記眼球運動検査手段により得られた複数の指標を多変量演算手法を用いてより少ない指標に変換する多変量演算手段と、該変換された指標を表示する出力手段とを備え、眼球運動検査手段は、映像表示部の画面に、一定の時間間隔で画面上の位置が変化する複数の対象物であって、画面上の位置が不規則に変化する対象物と一定の方向に一定量だけ位置が変化する対象物を含む複数の対象物を表示させた状態で、映像表示部の画面から一定の方向に一定量だけ位置が変化する対象物を探索した結果を被験者が入力装置を用いて入力した情報と検査内容とに基づいて対象物の識別能力を評価し、識別能力の評価結果を上記指標として求めることを特徴とし、眼球運動という副作用のない簡易な方法で脳機能の検査が行え、さらに多変量演算を行って複数の指標をより少ない指標に変換することにより、検査精度を高めることができるという効果がある。
【００７３】
請求項２の発明は、被験者の眼前に配置された映像表示部と、被験者が探索結果を入力するための入力装置と、入力装置を用いて入力された情報をもとに、被験者の眼球の運動を評価する複数の指標を求める眼球運動検査手段と、前記眼球運動検査手段により得られた複数の指標を多変量演算手法を用いてより少ない指標に変換する多変量演算手段と、該変換された指標を表示する出力手段とを備え、眼球運動検査手段は、複数個の文字を映像表示部の画面上のランダムな位置に表示させた状態で、画面上に表示された複数個の文字から特定の文字を探索した結果を被験者が入力装置を用いて入力した情報と眼球運動の計測結果とに基づいて上記指標を求めることを特徴とし、眼球運動という副作用のない簡易な方法で脳機能の検査が行え、さらに多変量演算を行って複数の指標をより少ない指標に変換することにより、検査精度を高めることができるという効果がある。
【００７４】
請求項３の発明は、請求項１又は２の何れかの発明において、多変量演算手段は、眼球運動検査手段により得られた指標と、被験者に対して神経心理学検査を行うことによって得られた神経心理学検査で用いる指標とを用いて、より少ない指標に変換する多変量演算を行うことを特徴とし、眼球運動の検査や神経心理学検査という副作用のない簡易な方法で脳機能の検査が行え、さらに眼球運動の検査と神経心理学検査とを組み合わせ、多変量演算を行って複数の指標をより少ない指標に変換することにより、検査精度を高めることができるという効果がある。
【００７５】
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、被験者の生体データを計測する生体データ検査手段を備え、多変量演算手段は、眼球運動検査手段および生体データ検査手段により得られた複数の指標を用いてより少ない指標に変換することを特徴とし、眼球運動を評価する指標と生体データとを組み合わせることによって、より精度の高い脳機能の検査が行える。
【００７６】
請求項５の発明は、請求項１乃至４の発明において、多変量演算手段は、多変量解析の判別分析を用いることで、複数の指標をより少ない指標に変換することを特徴とし、請求項１乃至４の発明と同様の効果を奏する。
【００７７】
請求項６の発明は、請求項１乃至４の発明において、多変量演算手段は、多変量解析の判別分析を用いることで、複数の指標をより少ない指標に変換することを特徴とし、請求項１乃至４の発明と同様の効果を奏する。
【００７８】
請求項７の発明は、請求項１乃至４の発明において、多変量演算手段は、ニューラルネットワークを用いることで、複数の指標をより少ない指標に変換することを特徴とし、請求項１乃至４の発明と同様の効果を奏する。
【００７９】
請求項８の発明は、請求項１乃至４の発明において、多変量演算手段は、ファジィ理論を用いることで、複数の指標をより少ない指標に変換することを特徴とし、請求項１乃至４の発明と同様の効果を奏する。
【００８０】
請求項９の発明は、請求項１乃至４の発明において、多変量演算手段は、エキスパートシステムを用いることで、複数の指標をより少ない指標に変換することを特徴とし、請求項１乃至４の発明と同様の効果を奏する。
【００８１】
請求項１０の発明は、請求項１乃至９の何れか１項に記載の脳機能検査装置と、当該脳機能検査装置に通信ネットワークを介して接続されるサーバとで構成される脳機能検査システムであって、前記脳機能検査装置は、被験者の眼球の運動を評価する指標を求める眼球運動検査手段と、被験者に対して神経心理学検査を行うことにより神経心理学検査で用いる指標を求める神経心理検査手段と、両検査手段により得られた指標を通信ネットワークを介してサーバに出力する第１の出力手段とを備え、前記サーバは、予め基準となる指標を格納しているデータベースと、脳機能検査装置から入力された被験者の指標とデータベースに記憶されている基準となるデータとを比較する比較手段と、比較手段の比較結果を通信ネットワークを介して脳機能検査装置に出力する第２の出力手段とを備えて成ることを特徴とし、眼球の運動を評価する指標又は視覚認知機能を評価する指標に、神経心理学検査の結果を組み合わせることにより、より精度の高い脳機能の検査を行うことができ、さらに脳機能検査装置とサーバとをネットワークを介して接続し、脳機能検査装置では被験者のデータを測定するだけで、サーバ側にデータベースを持たせているので、脳機能検査装置側の構成を簡単にできるという利点がある。
【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】 同上に用いる眼球運動指標を得るために撮像カメラにより撮像した眼球画像の例図である。
【図３】 同上による滑動性眼球運動検査の説明図である。
【図４】 同上による立体視検査の説明図である。
【図５】 同上による指標探索検査の説明図である。
【図６】 同上による移動性指標探索検査の説明図である。
【図７】 同上による文字探索検査の説明図である。
【図８】 同上による散文黙読検査の説明図である。
【図９】 同上による色模様傍観検査の説明図である。
【図１０】 同上に用いる判別値の計算方法の説明図である。
【図１１】 本発明の実施形態２の全体構成図である。
【図１２】 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 眼球運動検査手段
２ 神経心理検査手段
３ 多変量演算手段
５ 出力手段
３１，３２ 多変量解析計算部
ｗ１，ｗ２ 判別値[0001]
BACKGROUND OF THE INVENTION
  The present invention can be used to examine the degree of aging of brain function, brain diseases such as autonomic nervous system disease, dementia and Alzheimer's disease.Brain function test apparatus and brain function test systemAbout.
[0002]
[Prior art]
  Conventionally, a definitive diagnosis method for dementia has not been established, and comprehensive diagnosis is performed in consideration of various test results. One of the methods for testing dementia is a neuropsychological test in which a subject is asked and answered, and is widely used because it does not require a special test tool and can be easily performed. For example, the Japanese-language neuropsychological tests include the revised Hasegawa Intelligence Rating Scale (HDS-R), the National Seken-style Dementia Screening Test, the N-type Psychiatric Function Test, the Kana Hiroi Test, and the Mini Mental State Examination (MMSE). There are two types of intelligence assessment methods, such as MMSE (mini mental state examination) and ADAS (Alzheimer's Disease Assessment Scale Cognitive Subscale). In addition, by using CDR (Clinical Dementia Rating) taking into account behavioral evaluation, the presence or absence of dementia and its severity can be diagnosed.
[0003]
  Since such a neuropsychological test can be easily performed, it is suitable for a screening test performed before a detailed test that requires a large amount of money and time, and there is an advantage that it can be performed by an examiner other than a medical worker.
[0004]
[Problems to be solved by the invention]
  However, in the above-mentioned neuropsychological examination, the cooperation of the subject is indispensable, the examination result may be affected by the approach, and the examination result may change depending on how the examiner asks the question, There is also a possibility that the inspection results may be biased due to the subjective inspection method.
[0005]
  In addition, research papers have reported that there is a significant difference between Alzheimer-type dementia patients and healthy subjects, such as eye movements, which are noninvasive measurement targets. It has also been suggested that Alzheimer-type dementia, which has been difficult to diagnose in the past, can be tested. When using non-invasive tests such as eye movements for testing Alzheimer-type dementia, the eye movements themselves can be easily tested, so it is not always necessary for health care professionals to perform tests. Therefore, it is expected to be applied to self-care that is examined by the subject himself / herself. However, when the examination is performed using the eye movement index as it is, it is expected that the accuracy of the examination is limited, and it is still difficult to determine the dementia and further determine the aging degree of the brain function.
[0006]
  The present invention has been made in view of the above-described problems, and the object of the present invention is to accurately test brain functions with a simple method.Brain function test apparatus and brain function test systemIs to provide.
[0007]
[Means for Solving the Problems]
  In order to achieve the above object, in the invention of claim 1,A video display unit arranged in front of the subject's eyes, an input device for the subject to input search results, and a plurality of indicators for evaluating the movement of the eyeball of the subject based on information input using the input device A multivariate computing means for converting a plurality of indices obtained by the eye movement examining means into fewer indices using a multivariate computing technique, and an output means for displaying the transformed indices The eye movement inspection means includes a plurality of objects whose positions on the screen change on the screen of the video display unit at regular time intervals, and objects whose positions on the screen change irregularly. The result of searching for an object whose position changes by a certain amount in a certain direction from the screen of the image display unit in a state where a plurality of objects including an object whose position changes by a certain amount in a certain direction is displayed. Subject inputs using input device Information and on the basis of the test content to evaluate the discriminating power of the object, obtains the evaluation result of the identification capability as the indexThe brain function can be examined by a simple method having no side effect of eye movement, and the test accuracy can be improved by performing multivariate computation to convert a plurality of indices into fewer indices.
[0008]
  In the invention of claim 2,A video display unit arranged in front of the subject's eyes, an input device for the subject to input search results, and a plurality of indicators for evaluating the movement of the eyeball of the subject based on information input using the input device A multivariate computing means for converting a plurality of indices obtained by the eye movement examining means into fewer indices using a multivariate computing technique, and an output means for displaying the transformed indices The eye movement inspection means is a result of searching for a specific character from the plurality of characters displayed on the screen in a state where the plurality of characters are displayed at random positions on the screen of the video display unit. Is obtained based on the information input by the subject using the input device and the measurement result of eye movementThe brain function can be examined by a simple method having no side effect of eye movement, and the test accuracy can be improved by performing multivariate computation to convert a plurality of indices into fewer indices.
[0009]
  In invention of Claim 3, in either invention of Claim 1 or 2,The multivariate calculation means converts the index obtained by the eye movement examination means and the index used in the neuropsychological examination obtained by performing the neuropsychological examination on the subject into fewer indices. Perform multivariate operationsIt is possible to test brain function by a simple method without side effects such as eye movement test and neuropsychological test, and also by combining multi-variate calculation with eye movement test and neuropsychological test. The inspection accuracy can be increased by converting the index of 1 into fewer indices.
[0010]
  In the invention of claim 4, claim 1 is provided.Or 2In any of the inventions,Biological data inspection means for measuring biological data is provided, and the multivariate calculation means converts the number of indices obtained by the eye movement inspection means and the biological data inspection means into fewer indices.The brain function can be examined with higher accuracy by combining an index for evaluating eye movement and biological data.
[0011]
  In invention of Claim 5, in invention of Claims 1 thru | or 4,Multivariate computing means can convert multiple indicators into fewer indicators by using discriminant analysis of multivariate analysisAnd has the same effect as that of the first to fourth aspects of the invention.
[0012]
  In invention of Claim 6, in invention of Claims 1-4,Multivariate calculation means can convert multiple indicators into fewer indicators by using secondary discrimination of multivariate analysis.And has the same effect as that of the first to fourth aspects of the invention.
[0013]
  In the invention of claim 7, in the invention of claims 1 to 4,Multivariate computing means converts multiple indices into fewer indices by using a neural networkAnd has the same effect as that of the first to fourth aspects of the invention.
[0014]
  In invention of Claim 8, in invention of Claim 1 thru | or 4,Multivariate computing means can convert multiple indicators into fewer indicators by using fuzzy theory.And has the same effect as that of the first to fourth aspects of the invention.
[0015]
  In invention of Claim 9, in invention of Claims 1 thru | or 4,Multivariate computing means converts multiple indicators into fewer indicators by using an expert systemAnd has the same effect as that of the first to fourth aspects of the invention.
[0016]
  In the invention of claim 10, it is described in any one of claims 1 to 9.The brain function testing system comprises a brain function testing device and a server connected to the brain function testing device via a communication network, wherein the brain function testing device evaluates eye movements of a subject. An eye movement inspection means for obtaining an index, a neuropsychological examination means for obtaining an index to be used in a neuropsychological examination by performing a neuropsychological examination on a subject, and an index obtained by both examination means via a communication network First output means for outputting to a server, wherein the server stores a reference index in advance, a test subject index input from the brain function testing apparatus, and a reference stored in the database. And a second output means for outputting the comparison result of the comparison means to the brain function testing apparatus via the communication network.By combining the results of neuropsychological testing with an index that evaluates eye movement or an index that evaluates visual cognitive function, it is possible to test brain functions with higher accuracy. By connecting the testing device and the server via a network, the brain function testing device only has to measure the subject's data, and because the server has a database, the configuration on the brain function testing device side can be simplified. Have an advantage.
[0017]
DETAILED DESCRIPTION OF THE INVENTION
  The present invention will be described with reference to embodiments.
[0018]
  (Embodiment 1)
  FIG. 1 shows an overall configuration diagram in the present embodiment. The apparatus according to the present embodiment includes an eye movement test unit 1, a neuropsychological test unit 2, a multivariate calculation unit 3, a database 4, and an output unit 5.
[0019]
  The eye movement inspection means 1 is provided to calculate an index for evaluating the eye movement in the horizontal direction and the vertical direction. The light emitting element 10, the imaging camera 11, the image processing circuit 12, the eye movement calculation unit 13, and a video display are provided. The unit 14 is configured.
[0020]
  The light emitting element 10 is illumination for obtaining a clear eyeball image even in a dark environment. For example, when an infrared LED having an emission wavelength in an infrared wavelength region that is not perceived by humans is used, the light emitting element 10 is used. The light is not perceived by the subject, and the subject's anxiety can be reduced.
[0021]
  The imaging camera 11 is for photographing the eyeball M of the subject, and can be constituted by a CCD camera, a CMOS sensor, or the like.
[0022]
  The image processing circuit 12 extracts an eyeball portion from the image shown in FIG. 2 captured by the imaging camera 11 and obtains the motion of the eyeball M. That is, the horizontal position and the vertical position of the eyeball M are obtained by image processing the image of the imaging camera 11 by the image processing circuit 12. Then, the eye movement calculation unit 13 sequentially calculates the position data of the eyeball M obtained by the image processing circuit 12, so that the movement speed and movement acceleration in the horizontal direction of the eyeball M, the movement speed and movement acceleration in the vertical direction, etc. It is calculated by calculating the index. Here, the eye movement calculation unit 13 is configured by the calculation function of the personal computer 6 constituting the multivariate calculation means 3 and the database 4 described above.
[0023]
  The video display unit 14 is composed of, for example, a display device, and when performing a visual system function that reflects the activity state of the visual cortex of the brain when performing a visual cognitive test or a motor function test, which will be described later, the test video is displayed to the subject. It is for presentation.
[0024]
  In this embodiment, the eye movement test means 1 is used to perform a visual cognitive test for measuring eye movement that occurs when a human advanced cognitive function is requested or a motor function test for measuring only a simple motor function. Yes. For example, visual cognitive test methods include sliding eye movement test, stereoscopic vision test, index search test, color pattern side-view test, and motor function test methods include mobility index cognitive test, character search test, prose There are methods such as silent reading. And it has been published in research papers etc. that there is a significant difference between patients with Alzheimer-type dementia and healthy individuals in the indicators obtained by these tests (Reference 1: G. Zaccara et al. "Smooth-pursuit eye movements: alternation in Alzheimer's disease"; Journal of the Neurological Sciences; 112; pp.81-92; 1992, Reference 2: S. Corkin et al., "Visual Dysfunction in Alzheimer's Disease: Relation to Normal Aging "; Annals of Neuro1ogy; vol.29; No.1; 41-52; 1991, Ref 3: LFM.Scinto et al.," Impairment of spatially directed attention in patients with probable Alzheimer's disease as measured by eye movements "; Arch Neurol; 1994; Vol.51; pp.682-688, Reference 4: Gray L. Trick et al., "Visua1 sensitivity to motion: Age-related changes and deficits in seniledementia of the Alzheimer type"; NEUROLOGY 1991; 41; 1437 -1440, Reference 5: A. Rosler et al., "Alterations of visual search strategy in Alzheimer's disease and aging: Neuropsychology"; 2000; Vol.14 ; No.3; pp.398-408, Ref. 6: KS.Lueck et al., "Eye movement abnormalities during reading in patients with Alzheimer disease"; Neuropsychiatry, Neuropsychology, and Behavioral Neurology; 2000; Vol.13; No.2 pp. 77-82, Reference 7: A. Moser et al., “Eye movement dysfunction in dementia of the Alzheimer typr”; Dementia; 1995; No. 6; pp. 264-268).
[0025]
  In any of the visual recognition test and the motor function test, the measurement object is the movement of the eyeball, and it is possible to calculate the position of the eyeball, the motion speed, the motion acceleration, or the like as an objective index in each test, It can be used as an input variable of the multivariate calculation means 3.
[0026]
  Here, each inspection method described above will be briefly described with reference to FIGS.
[0027]
  First, the sliding eye movement test will be described with reference to FIG. In this examination, the subject A is instructed to gaze at the light emitting element 15 that is lit, and the plurality of light emitting elements 15 arranged in front of the eye of the subject A are turned on in order. The movement of the eyeball M of the subject A is measured, and indices such as the speed at which the eyeball M can follow and the follow-up delay of the eyeball M with respect to the light emitting element 15 that is turned on are derived. In this test, an index indicating the ability to track a moving object is obtained, and the above-mentioned document 1 describes a statistically significant tracking ability between a group of patients with Alzheimer-type dementia and a group of healthy subjects. It is reported that there is a capacity difference (p = 0.001).
[0028]
  FIG. 4 is an explanatory diagram of the stereoscopic inspection. As a means for realizing stereoscopic vision, there is a binocular stereoscopic method that generates a sense of depth by presenting two types of images with parallax using human interpupillary distance to each of the left and right eyes. In the form, the image display unit 14 is used to present two types of images with parallax applied to the left and right eyes of the subject A. Then, the subject A is instructed to pay attention to the protruding circle among the three circles C1 to C3 in the image 14a, and the eye movement calculation unit 13 determines whether or not the answer is correct from the position of the eye M. In this examination, the minimum parallax that is correct is derived as an evaluation index, and the above-mentioned document 2 has a statistically significant difference between the Alzheimer type dementia patient group and the healthy subject group. (P <0.05).
[0029]
  FIG. 5 is an explanatory diagram of the index search inspection. In this examination, a plurality of areas are set in the image 14a presented by the video display unit 14, and the ability to search for a target appearing in any one of the areas is measured. Reference 3 described above describes Alzheimer-type dementia. There is a statistically significant difference between the group of patients and the group of healthy subjects. In this examination, first, the subject is gazeed at the center (CT) of the screen 14a, and then circular marks M1 to M4 are placed in any one of the areas provided at the four corners of the screen 14a for a certain time (for example, about 0). (5 seconds) only. In FIG. 5, four marks M1 to M4 are displayed on the image 14a. However, when actually inspecting, only one of the four marks M1 to M4 is displayed. Here, the examiner presents the subject A to watch the mark appearing in any of the four corners with respect to the subject A. In the eye movement calculation unit 13, the eyeball M measured by the image processing circuit 12 is presented. An index relating to eye movement is derived from the position of. The evaluation items include the correct answer rate (p <0.014) and the delay time (p <0.005).
[0030]
  6A and 6B are explanatory diagrams of the mobility index search test. This test measures the ability to identify an object whose position changes by a certain amount in a certain direction from a plurality of objects whose position on the screen changes irregularly at certain time intervals. Reference 4 described above reports that there is a statistically significant difference in the ability to discriminate between the group of subjects with Alzheimer-type dementia and the group of healthy subjects (p = 0.001). When this inspection is performed, a plurality of round objects (hereinafter referred to as dots) C are arranged at arbitrary positions on the background screen by the video display unit 14 as shown in FIGS. The displayed image is displayed, and the position of each dot C is changed after a certain time (for example, 1 second). Here, most of the plurality of dots C are moved in an irregular direction, and only a part is moved from the previous position in a fixed direction by a fixed amount. Such a dot C is particularly a coherent dot C. 'I say. In FIGS. 6A and 6B, the coherent dot C ′ is hatched and the other dots (referred to as random dots) C are indicated by white circles. However, in the inspection image, the same color and luminance are displayed. Therefore, the coherent dot C ′ cannot be identified from characteristics such as color and luminance. FIG. 6A shows a state where the ratio of coherent dots C ′ is 10%, and FIG. 6B shows a state where the ratio of coherent dots C ′ is 50%.
[0031]
  At this time, the subject A identifies the coherent dot C ′ from the inspection image shown on the screen 14a of the video display unit 14, and uses the input device (joystick or the like) of the personal computer 6 to determine the moving direction. The eye movement calculation unit 13 derives an index relating to eye movement based on the information input by the subject using the input device and the examination content. For example, the eye movement calculation unit 13 evaluates the discrimination ability based on the ratio (coherence value) of coherent dots C ′ when a correct answer of 75% or more is obtained. In addition, when the video display unit 14 switches the inspection video, the coherent dot C ′ may be randomly selected from the plurality of dots C each time, but the same dot is coherent during the entire inspection. A dot C ′ may be used, and the coherent dot C ′ may be moved by a certain amount in a certain direction at a certain time interval. In this case, the eyeball moves according to the moving direction of the coherent dot C ′. By measuring the movement of the eyeball, the identification ability of the subject can be automatically measured, and the brain function can be automatically determined.
[0032]
  FIG. 7 is an explanatory diagram of the character search inspection. In this test, the ability to search for a specific character (for example, a number) from a plurality of characters is measured, and the above-mentioned document 5 describes the statistics between a group of subjects with Alzheimer-type dementia and a group of healthy subjects. It is reported that there is a significant difference in the ability to distinguish luminance differences. In this examination, first, the subject is gazeed at the center of the screen 14a of the video display unit 14, and then, as shown in FIG. 7, a plurality of characters (for example, alphabets) and one piece are placed at random positions on the screen 14a. A test video with numbers arranged is presented to the subject. At this time, the person in charge of the test gave the subject the task “Look for a number among letters and enter the number.” When the number was found for this task, A number is input by operating an input device (for example, a keyboard) of the personal computer 6. In this embodiment, the eyeball image captured by the imaging camera 11 is processed by the image processing circuit 12 to measure the movement of the eyeball. The eye movement calculation unit 13 uses the measurement result of the image processing circuit 12 and the input device. Based on the information input by the subject, an index relating to the ability to search for characters is derived. Then, the above test is executed a plurality of times to derive an index relating to eye movement. As evaluation items, accuracy rate (p <0.0001), reaction time (p <0.0001), fixation time (p <0.0001), number of fixations (p <0.0001), fixation There are visual duration (p = 0.0003), operation time (p <0.0001), and the like.
[0033]
  FIG. 8 is an explanatory diagram of the prose silent reading test. In this examination, an index indicating the movement history of the subject's eyeball when displaying a video showing prose in front of the subject's eyes is measured, and the above-mentioned document 6 describes a group of subjects with Alzheimer-type dementia and healthy subjects. Statistically significant differences have been reported between groups. In this examination, the video display unit 14 presents a video showing prose E to the subject as shown in FIG. 8, and causes the subject to silently read the prose E, and the eyeball M is read when the text is silently read. Measure what kind of exercise you are doing. That is, the image processing circuit 3 measures the movement of the eyeball by processing the image captured by the imaging camera 11 with the image processing circuit 12, and the eye movement calculation unit 13 is based on the measurement result of the image processing circuit 12. Then, an index indicating the movement history of the eyeball of the subject is derived. In addition, as an index indicating the movement history of the eyeball, the amount read in a predetermined time (reading amount, p <0.001), forward saccade (p <0.001), backward saccade (p <0.05), solid Visual duration (p = 0.0001) and the like.
[0034]
  FIG. 9 is an explanatory diagram of a color pattern side-view inspection. In this examination, an index indicating the movement history of the subject's eyeball when displaying an image composed of a color pattern in front of the subject's eyes is measured. Reference 7 described above includes a group of subjects with Alzheimer-type dementia and healthy subjects. Statistically significant differences have been reported between groups. In this examination, after first instructing the subject to view the entire screen 14a of the video display unit 14, the video display unit 14 presents the video composed of the color pattern PT to the subject as shown in FIG. The subject is viewing this image, and the movement of the eyeball M is measured when viewing the color pattern PT. That is, the image processing circuit 12 measures the movement of the eyeball by processing the image captured by the imaging camera 11 with the image processing circuit 12, and the eye movement calculation unit 13 is based on the measurement result of the image processing circuit 12. Then, an index indicating the movement history of the eyeball of the subject is derived. In addition, as an index indicating the movement history of the eyeball, there are a saccade number (p <0.05), a fixation duration (p <0.05), and the like.
[0035]
  On the other hand, the neuropsychological examination means 2 is provided for conducting a neuropsychological examination on the subject and deriving an index used in the neuropsychological examination, and includes an input unit 21, a correct / score calculation unit 22, The task presentation unit 23 is configured.
[0036]
  As described in the prior art, various neuropsychological tests are used for dementia testing. In the neuropsychological test means 2, the task presentation unit 23 is shown in Table 1 (Revised Hasegawa Simple Intelligence Evaluation Scale). Question and answers as shown in the Japan Medical Association edition, Senile Dementia Medical Treatment Manual) are displayed on the output means 5 comprising a display device to prompt the subject to answer. The subject inputs an answer using the input unit 21 including a keyboard, a mouse, and a voice input device, and the correct / score calculation unit 22 calculates a score indicating the subject's intelligence based on the answer. Here, the personal computer 6 incorporates a program for calculating the score from the question / answer presented to the subject and the answer to the questionnaire, and the above-described correct / score calculation unit 22 is constituted by the calculation function of the personal computer 6. The
[0037]
[Table 1]

[0038]
  Here, there are generally a plurality of indices for evaluating eye movements obtained by the above-described examinations and indices used for neuropsychological examinations, but the feature of the present invention is to use them in combination for calculation of multivariate analysis. .
[0039]
  The multivariate computing means 3 of this embodiment selects discriminant values from various indices input from the eye movement examination means 1 and the neuropsychological examination means 2 by a discriminant analysis method in multivariate analysis. This discriminant value is usually one, but by classifying the input indicators into several groups and finding one discriminant value for each group, as a result, a plurality of indicators It is also possible to obtain a discrimination value. In this embodiment, it is assumed that there is one discriminant value. Discriminant analysis by 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. Other multivariate analysis methods include principal component analysis methods. 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.
[0040]
  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.
[0041]
  The functions of the multivariate calculation means 3 used in the present embodiment include an index relating to the eye movement of the subject obtained by the eye movement calculation unit 13 and an index used in the neuropsychological examination obtained by the correct / score calculation unit 22. The multivariate analysis calculation unit 30 that calculates the discriminant value w1 of the subject by converting to a smaller index, and the eye movement calculation unit 13 and the correct / score calculation unit 22 determine the number of subjects stored in the database 4. A multivariate analysis calculation unit 31 that performs multivariate analysis calculation by calling an index and calculates discriminant values w2 of a large number of subjects.
[0042]
  The multivariate calculation means 3 of the present embodiment is realized by the same personal computer 6 as the eye movement calculation unit 13 and the correctness / score calculation unit 22, but may be realized by a separate personal computer or a personal computer. The present invention can be realized by using a DSP (digital signal processor) or a microcomputer instead of the computer, and the specific implementation means of the present invention is not particularly limited to the embodiment.
[0043]
  The database 4 includes an index representing individual variables relating to eye movements, an index used in a neuropsychological examination, a discriminant value obtained by multivariate calculation using these indices, and the age, sex, and past symptoms of the subject or other subjects. All information including information on subject information and measurement environment information necessary for brain function tests, such as current disease, measurement date and time, measurement location, environmental conditions such as illuminance and temperature of measurement location, intelligence test results, etc. In the embodiment, data is registered such as eye movement index and neuropsychological index of the subject, discriminant values w1 and w2, and information including the subject information and measurement environment information. It is like that. By accumulating data in the database 4 in this way, more subject data are accumulated and a highly reliable database can be constructed.
[0044]
  Although the database 4 of the present embodiment is realized by the same personal computer 6 as the multivariate calculation means 3 and the eye movement calculation unit 13, it is not necessarily required to be realized by the same personal computer as the multivariate calculation means 3. Absent.
[0045]
  The output means 5 is composed of a display device such as a display or a printer, and displays the discriminant value w1 of the subject and the discriminant value w2 of the other subject, and the discriminant value w1 of the subject is the discriminant value w2 of the other subject. By comparison, it is possible to see what value is relatively shown. Here, the display method by the output means 5 can be illustrated using a graph or the like, or only numerical values may be simply displayed. 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, as an index of another subject, a discriminant value obtained in the past of the subject may be used. Although not shown in FIG. 1, not only the discriminant values w1 and w2 but also the indicators themselves and other data may be displayed on the display device constituting the output means 5.
[0046]
  An object of the present invention is to derive a value with a smaller p-value by combining an index for evaluating eye movement and an index used for neuropsychological examination. 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. In addition to the method of determining whether the result is A or B as in the case of determining whether the patient belongs to the normal group or the patient group, the distance is closer to which group and the extent of the distance. By knowing the result as a continuous quantity such as, 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.
[0047]
  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).
[0048]
  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. Therefore, if the discriminant value w of the subject is located at a distance close to the healthy person group X, it is determined as a healthy person, and if it is located at a distance close to the patient group Y, a determination process such as determining as a patient is performed. it can. Also, by taking a proportional distribution of these distances, it can be displayed as a single numerical value such as the probability of disease. 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. Show.
[0049]
  Thus, it is possible to increase the significant difference between both groups (X) and (Y) by performing multivariate computation processing in the multivariate computation means 3 and consolidating several indices into one value. In addition, since the information to be output is reduced, the determination becomes easy.
[0050]
  (Embodiment 2)
  FIG. 11 shows a system configuration of the brain function testing system according to the present embodiment. This system is composed of a brain function testing device 7 for testing the brain function of a subject and a server 8 connected to the brain function testing device 7 via the Internet 50 (communication network). Provides brain function testing services using this system. Note that the operator who operates the server 8 also serves as the brain test business, but the server management and the brain test business may be performed by different business operators.
[0051]
  The brain function test apparatus 7 includes eye movement test means 1, neuropsychological test means 2, and output means 5.
[0052]
  The eye movement inspection means 1 is provided to calculate an index for evaluating the eye movement in the horizontal direction and the vertical direction. The light emitting element 10, the imaging camera 11, the image processing circuit 12, the eye movement calculation unit 13, and a video display are provided. The unit 14 is configured.
[0053]
  The light emitting element 10 is illumination for obtaining a clear eyeball image even in a dark environment. For example, when an infrared LED having an emission wavelength in an infrared wavelength region that is not perceived by humans is used, the light emitting element 10 is used. The light is not perceived by the subject, and the subject's anxiety can be reduced.
[0054]
  The imaging camera 11 is for photographing the eyeball M of the subject, and can be constituted by a CCD camera, a CMOS sensor, or the like.
[0055]
  The image processing circuit 12 extracts an eyeball portion from the image shown in FIG. 2 captured by the imaging camera 11 and obtains the motion of the eyeball M. That is, the horizontal position and the vertical position of the eyeball M are obtained by image processing the image of the imaging camera 11 by the image processing circuit 12. Then, the eye movement calculation unit 13 sequentially calculates the position data of the eyeball M obtained by the image processing circuit 12, so that the movement speed and movement acceleration in the horizontal direction of the eyeball M, the movement speed and movement acceleration in the vertical direction, etc. It is calculated by calculating the index. Here, the eye movement calculation unit 13 is configured by the calculation function of the personal computer 6.
[0056]
  The video display unit 14 is composed of, for example, a display device, and when performing a visual system function that reflects the activity state of the visual cortex of the brain when performing a visual cognitive test or a motor function test, which will be described later, the test video is displayed to the subject. It is for presentation.
[0057]
  On the other hand, the neuropsychological examination means 2 is provided for conducting a neuropsychological examination on a subject and deriving a plurality of indices used in the neuropsychological examination. And a task presentation unit 23.
[0058]
  As described above, various neuropsychological tests are used for dementia, and in the neuropsychological test means 2, the task presentation unit 23 displays a question and answer and a questionnaire as shown in Table 1 using a display device. It is displayed on the output means 5 to prompt the subject to answer. The subject inputs an answer using the input unit 21 including a keyboard, a mouse, and a voice input device, and the correct / score calculation unit 22 calculates a score indicating the subject's intelligence based on the answer. Here, the personal computer 6 incorporates a program for calculating the score from the question / answer presented to the subject and the answer to the questionnaire, and the above-described correct / score calculation unit 22 is constituted by the calculation function of the personal computer 6. The
[0059]
  The output means (first output means) configured by the communication function of the personal computer 6 transmits the index calculated by the eye movement calculation unit 13 and the correctness / score calculation unit 22 to the server 8 via the Internet 50. The receiving means configured by the communication function of the personal computer 6 receives data such as the discriminant value w1 of the subject and the discriminant value w2 of other subjects transmitted from the server 8 via the Internet 50.
[0060]
  The output means 5 is constituted by a display device such as a display device or a printer, and displays the discriminant value w1 of the subject and the discriminant value w2 of the other subject received by the personal computer 6, and the discriminant value w1 of the subject is the other. Compared with the discriminant value w2 of the subject, it can be seen what value is relatively shown. Note that the discriminant value w2 of another subject may be not only one discriminant value of another subject but also an average value of discriminant values of a plurality of subjects, or an average value of discriminant values of a specific subject group It 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. Furthermore, the discriminant value to be compared with the discriminant value w1 of the subject may be a discriminant value obtained in the past of the subject. Further, not only the discriminant values w1 and w2 but also the indicators themselves and other data may be displayed on the display device constituting the output means 8.
[0061]
  On the other hand, the server 40 includes the multivariate calculation means 3 and the database 4 and is configured by a personal computer or the like.
[0062]
  The receiving unit configured with the communication function of the server 40 receives the index transmitted from the brain function testing apparatus 7 via the Internet 50, and also includes the transmitting unit (second output means) configured with the communication function of the server 40. ) Transmits the discriminant value W1 of the subject and the discriminant values W2 of a large number of subjects obtained by the multivariate calculation means 3 to the brain function test apparatus 7 via the Internet 50.
[0063]
  The multivariate calculation means 3 includes a plurality of indices relating to the eye movement of the subject obtained by the eye movement calculation section 13 of the eye movement inspection means 1 and a plurality of indices used in the neuropsychological examination obtained by the correctness / score calculation section 22. Are converted into fewer indices and calculated by the multivariate analysis calculation unit 30 for calculating the discriminant value w1 of the subject, the eye movement calculation unit 13 and the correct / score calculation unit 22 and stored in the database 4 And a multivariate analysis calculation unit 31 that performs multivariate analysis calculation by calling the eye movement index and the neuropsychological index, and calculates discrimination values w2 of a large number of subjects.
[0064]
  The database 4 is constructed in the storage device of the server 8, and eye movement indices and neuropsychological indices of a plurality of subjects including the subject, and discriminant values w1, w2 obtained by multivariate analysis calculation using these indices. Further, identification IDs of a plurality of subjects including the subject, for example, communication contacts on a communication network such as an IP address, age, sex, past illness, current disease, measurement date, measurement location, illuminance and temperature of the measurement location Information consisting of subject information and measurement environment information necessary for brain function tests, such as environmental conditions such as the above, results of intelligence tests, and the like are stored. In this embodiment, the index data of the subject and the information thereof are added as new data to the database 4, and the test result is accumulated in the database 4 as new data every time a brain function test is performed. Therefore, data of more subjects can be accumulated and a highly reliable database 4 can be constructed. Here, the database 4 constitutes subject information storage means for storing subject information and determination criterion storage means for storing data serving as a reference for comparison with an index obtained by multivariate calculation.
[0065]
  When a brain function test is performed with this system, when the examiner or the subject himself / herself performs an examination using the eye movement test means 1 arranged at the examination location, the eye movement calculation unit 13 calculates an index for evaluating the eye movement. In addition, the correctness / score calculating unit 22 calculates an index used in the neuropsychological examination. When the identification ID assigned to the subject is input using an input device such as a keyboard of the personal computer 6, the personal computer 6 transmits the identification ID of the subject and the measurement data to the server 8 via the Internet 50. To do. On the other hand, the server 8 receives the identification ID of the subject and the measurement data transmitted from the brain function test apparatus 7 via the Internet 50, and the multivariate analysis calculation unit 30 performs multivariate calculation on the received data to reduce the received data. Convert to indicator. Next, the server 8 reads out data serving as a reference for comparison with an index obtained by multivariate calculation from the database 4, compares the subject's index with the reference index, and creates a comparison result. Thereafter, the server 8 reads out the IP address of the subject from the database 4 based on the identification ID of the subject, and creates an HTML file that displays the comparison result of comparing the index of the subject with the reference index. The HTML file is transmitted to the personal computer 6 via the Internet 50. The personal computer 6 receives the HTML file transmitted from the server 8 and outputs the comparison result to the output means 8 composed of a display device, a printer, or the like. Can be easily read.
[0066]
  Here, the multivariate calculation means 3 for converting the measurement data of the eye movement test means 1 and the neuropsychological test means 2 transmitted from the brain function test apparatus 7 into fewer indices using the multivariate calculation method, and the multivariate calculation The comparison means for comparing the index obtained by the above and the data stored in the database 4 to obtain the comparison result is realized by the computing function of the computer constituting the server 8. In addition, subject information storage means storing subject information including identification IDs of a plurality of subjects and communication contacts on a communication network, and data serving as a reference for comparison with an index obtained by multivariate calculation are stored. The comparison reference storage means is realized by a storage device of a computer constituting the server 8. In addition, receiving means for receiving the subject ID and the measurement data of the subject transmitted through the communication network, reading the subject's communication contact information from the subject information storage means based on the subject ID, and via the communication network The transmission means for transmitting the comparison result of the comparison means to the communication contact is realized by the communication function of the computer constituting the server 8. The storage device of the server 8 stores in advance an operation program for causing the computer to execute the above-described operation.
[0067]
  By the way, although the multivariate computation means 3 of each embodiment described above uses discriminant analysis, multivariate computation may be performed by the following method.
[0068]
  That is, as one of the methods, a method using a neural network having a three-layer structure including, for example, 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.
[0069]
  Another method is to use fuzzy theory. For example, when considering attributes related to the index, “long fixation time” or “short fixation time” can be considered. And when examining brain function, when “fixation time is short” and “fixation times are low”, only when “fixation time is short” or “fixation times are low” The brain function is higher than the case. 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.
[0070]
  Yet another method is to use an expert system. For example, a condition is set for each index such as when the fixation time is shorter than time t1, or when the fixation time is longer than time t2 (> t1), and the result when the condition is satisfied is set. To do. During the examination, the brain function is determined by applying each index observed by the subject to each condition.
[0071]
  Further, in each of the above-described embodiments, multivariate computation is performed using both the eye movement index obtained by the eye movement examination means 1 and the neuropsychological index obtained by the neuropsychological examination means 2 as inputs of the multivariate computation means 3. However, the multivariate calculation may be performed using either the eye movement index or the neuropsychological index as an input to the multivariate calculation means 3, or the multivariate calculation is performed to convert the index into fewer indexes. By testing the brain function using the index, it is possible to perform a more accurate brain function test. In addition, biological data other than eye movements (for example, measurement data such as the amount of protein in the blood and components of cerebrospinal fluid) may be combined with an eye movement index or a neuropsychological index to be input to the multivariate calculation means 3. By testing the brain function by combining such biometric data, it is possible to test the brain function with higher accuracy.
[0072]
【The invention's effect】
  As described above, the invention of claim 1A video display unit arranged in front of the subject's eyes, an input device for the subject to input search results, and a plurality of indicators for evaluating the movement of the eyeball of the subject based on information input using the input device A multivariate computing means for converting a plurality of indices obtained by the eye movement examining means into fewer indices using a multivariate computing technique, and an output means for displaying the transformed indices The eye movement inspection means includes a plurality of objects whose positions on the screen change on the screen of the video display unit at regular time intervals, and objects whose positions on the screen change irregularly. The result of searching for an object whose position changes by a certain amount in a certain direction from the screen of the image display unit in a state where a plurality of objects including an object whose position changes by a certain amount in a certain direction is displayed. Subject inputs using input device Information and on the basis of the test content to evaluate the discriminating power of the object, obtains the evaluation result of the identification capability as the indexThe brain function can be examined by a simple method without the side effect of eye movement, and the test accuracy can be improved by converting multiple indices into fewer indices by performing multivariate computation. effective.
[0073]
  The invention of claim 2A video display unit arranged in front of the subject's eyes, an input device for the subject to input search results, and a plurality of indicators for evaluating the movement of the eyeball of the subject based on information input using the input device A multivariate computing means for converting a plurality of indices obtained by the eye movement examining means into fewer indices using a multivariate computing technique, and an output means for displaying the transformed indices The eye movement inspection means is a result of searching for a specific character from the plurality of characters displayed on the screen in a state where the plurality of characters are displayed at random positions on the screen of the video display unit. Is obtained based on the information input by the subject using the input device and the measurement result of eye movementThe brain function can be examined by a simple method without the side effect of eye movement, and the test accuracy can be improved by converting multiple indices into fewer indices by performing multivariate computation. effective.
[0074]
  The invention of claim 3 is the invention of claim 1 or 2,The multivariate calculation means converts the index obtained by the eye movement examination means and the index used in the neuropsychological examination obtained by performing the neuropsychological examination on the subject into fewer indices. Perform multivariate operationsIt is possible to test brain function by a simple method without side effects such as eye movement test and neuropsychological test, and also by combining multi-variate calculation with eye movement test and neuropsychological test. There is an effect that the inspection accuracy can be improved by converting the index of 1 into a smaller number of indices.
[0075]
  The invention of claim 4 is the invention of claim 1.Or 2In any of the inventions,Biological data inspection means for measuring the biological data of the subject is provided, and the multivariate calculation means converts the number of indices obtained by the eye movement inspection means and the biological data inspection means into fewer indices.The brain function can be examined with higher accuracy by combining an index for evaluating eye movement and biological data.
[0076]
  The invention of claim 5 is the invention of claims 1 to 4,Multivariate calculation means converts multiple indicators into fewer indicators by using discriminant analysis of multivariate analysisThe same effects as those of the first to fourth aspects of the invention can be achieved.
[0077]
  The invention of claim 6 is the invention of claims 1 to 4,Multivariate calculation means converts multiple indicators into fewer indicators by using discriminant analysis of multivariate analysisThe same effects as those of the first to fourth aspects of the invention can be achieved.
[0078]
  The invention of claim 7 is the invention of claims 1 to 4,Multivariate computing means converts multiple indices into fewer indices by using a neural networkThe same effects as those of the first to fourth aspects of the invention can be achieved.
[0079]
  The invention of claim 8 is the invention of claims 1 to 4,Multivariate arithmetic means transforms multiple indices into fewer indices by using fuzzy theoryThe same effects as those of the first to fourth aspects of the invention can be achieved.
[0080]
  The invention of claim 9 is the invention of claims 1 to 4,Multivariate computing means converts multiple indicators into fewer indicators by using an expert systemThe same effects as those of the first to fourth aspects of the invention can be achieved.
[0081]
  The invention of claim 10A brain function testing system comprising: the brain function testing device according to any one of claims 1 to 9; and a server connected to the brain function testing device via a communication network. The test apparatus includes an eye movement test means for obtaining an index for evaluating the eye movement of the subject, a neuropsychological test means for obtaining an index to be used in a neuropsychological test by performing a neuropsychological test on the subject, and both tests First output means for outputting an index obtained by the means to a server via a communication network, wherein the server is preliminarily input from a database storing a reference index and a brain function test apparatus Comparison means for comparing the index of the subject and the reference data stored in the database, and outputting the comparison result of the comparison means to the brain function testing apparatus via the communication network Comprising a second output meansBy combining the results of neuropsychological testing with an index that evaluates eye movement or an index that evaluates visual cognitive function, it is possible to test brain functions with higher accuracy. By connecting the testing device and the server via a network, the brain function testing device only has to measure the subject's data, and because the server has a database, the configuration on the brain function testing device side can be simplified. Have an advantage.
[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 view of an eyeball image captured by an imaging camera to obtain an eye movement index used in the above.
FIG. 3 is an explanatory diagram of a sliding eye movement test according to the above.
FIG. 4 is an explanatory diagram of a stereoscopic inspection according to the above.
FIG. 5 is an explanatory diagram of an index search inspection according to the above.
FIG. 6 is an explanatory diagram of mobility index search inspection according to the above.
FIG. 7 is an explanatory diagram of a character search inspection according to the above.
FIG. 8 is an explanatory diagram of a prose reading test according to the above.
FIG. 9 is an explanatory diagram of a color pattern side-by-side inspection according to the above.
FIG. 10 is an explanatory diagram of a discriminant value calculation method used for the above.
FIG. 11 is an overall configuration diagram of Embodiment 2 of the present invention.
FIG. 12 is a model diagram of a neural network.
[Explanation of symbols]
  1 Eye movement test means
  2 Neuropsychological examination means
  3 Multivariate calculation means
  5 Output means
  31, 32 Multivariate analysis calculator
  w1, w2 discriminant value

Claims (10)

A video display unit arranged in front of the subject's eyes, an input device for the subject to input search results, and a plurality of indicators for evaluating the movement of the eyeball of the subject based on information input using the input device A multivariate computing means for converting a plurality of indices obtained by the eye movement examining means into fewer indices using a multivariate computing technique, and an output means for displaying the transformed indices The eye movement inspection means includes a plurality of objects whose positions on the screen change on the screen of the video display unit at regular time intervals, and objects whose positions on the screen change irregularly. The result of searching for an object whose position changes by a certain amount in a certain direction from the screen of the image display unit in a state where a plurality of objects including an object whose position changes by a certain amount in a certain direction is displayed. Subject inputs using input device Information and to evaluate the discriminating power of the object on the basis of the examination content, brain function testing apparatus and obtains the evaluation result of the identification capability as the index. A video display unit arranged in front of the subject's eyes, an input device for the subject to input search results, and a plurality of indicators for evaluating the movement of the eyeball of the subject based on information input using the input device A multivariate computing means for converting a plurality of indices obtained by the eye movement examining means into fewer indices using a multivariate computing technique, and an output means for displaying the transformed indices The eye movement inspection means is a result of searching for a specific character from the plurality of characters displayed on the screen in a state where the plurality of characters are displayed at random positions on the screen of the video display unit. The brain function test apparatus is characterized in that the index is obtained based on information input by a subject using an input device and a measurement result of eye movement. The multivariate calculation means converts the index obtained by the eye movement examination means and the index used in the neuropsychological examination obtained by performing the neuropsychological examination on the subject into fewer indices. 3. The brain function testing device according to any one of 1 and 2, wherein multivariate calculation is performed. Biological data inspection means for measuring biological data of a subject is provided, and the multivariate calculation means converts to a smaller number of indices using a plurality of indices obtained by the eye movement inspection means and the biological data inspection means. The brain function testing apparatus according to claim 1 or 2. 5. The brain function testing apparatus according to claim 1, wherein the multivariate calculation means converts a plurality of indices into fewer indices by using a discriminant analysis of multivariate analysis. 5. The brain function testing device according to claim 1, wherein the multivariate calculation means converts a plurality of indices into fewer indices by using secondary discrimination of multivariate analysis. . 5. The brain function testing apparatus according to claim 1, wherein the multivariate calculation means converts a plurality of indices into fewer indices by using a neural network. The brain function testing device according to any one of claims 1 to 4, wherein the multivariate computing means converts a plurality of indices into fewer indices by using fuzzy theory. 5. The brain function testing apparatus according to claim 1, wherein the multivariate calculation means converts a plurality of indices into fewer indices by using an expert system. A brain function testing system comprising: the brain function testing device according to any one of claims 1 to 9; and a server connected to the brain function testing device via a communication network. The test apparatus includes an eye movement test means for obtaining an index for evaluating the eye movement of the subject, a neuropsychological test means for obtaining an index to be used in a neuropsychological test by performing a neuropsychological test on the subject, and both tests First output means for outputting an index obtained by the means to a server via a communication network, wherein the server is preliminarily input from a database storing a reference index and a brain function test apparatus Comparison means for comparing the index of the subject and the reference data stored in the database, and outputting the comparison result of the comparison means to the brain function testing apparatus via the communication network Brain function testing system characterized by comprising a second output means.

Images