JP5734295B2 - 組織活性の部位を推定するためのシステムおよび方法 - Google Patents
組織活性の部位を推定するためのシステムおよび方法 Download PDFInfo
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- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
- A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
- A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
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- A61N1/36182—Direction of the electrical field, e.g. with sleeve around stimulating electrode
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- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
- A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
- A61N1/372—Arrangements in connection with the implantation of stimulators
- A61N1/37211—Means for communicating with stimulators
- A61N1/37252—Details of algorithms or data aspects of communication system, e.g. handshaking, transmitting specific data or segmenting data
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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- A61N1/02—Details
- A61N1/04—Electrodes
- A61N1/05—Electrodes for implantation or insertion into the body, e.g. heart electrode
- A61N1/0526—Head electrodes
- A61N1/0529—Electrodes for brain stimulation
- A61N1/0534—Electrodes for deep brain stimulation
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- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
- A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
- A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
- A61N1/3606—Implantable neurostimulators for stimulating central or peripheral nerve system adapted for a particular treatment
- A61N1/36082—Cognitive or psychiatric applications, e.g. dementia or Alzheimer's disease
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
Description
神経系の電気刺激は、様々な疾患の治療処置をもたらしてきた。たとえば、電気刺激は、脊髄を刺激することなどによって、疼痛処理のために施されてきた。また、電気刺激は、人工内耳の状況下で聴力を補強するために実施されてきた。脳深部刺激(DBS:deep brain stimulation)は、たとえば、パーキンソン病および筋失調症を含む、様々な症状を処置するための確立された治療法になっている。また、脳深部刺激は、数例をあげると、臨床的鬱病、強迫性障害、てんかんなど、他のいくつかの症状を処置するために用いられてきた。
電極に関して、その異なるタイプの電極について人工ニューラルネットワークに再学習させる必要なしに、VTAを推定するように、学習させることができる。
(a)活性の容積を画定する1または複数のパラメータ方程式を備える工程であって、1または複数のパラメータ方程式のためのパラメータは、刺激パラメータを含む入力ベクトルの関数として与えられる工程と;(b)刺激パラメータのための値を含む入力データを受け取り、そして入力データを使用して入力ベクトルを定義する工程と;(c)前記関数に前記入力ベクトルを適用し、そして前記1または複数のパラメータ方程式のための前記パラメータを取得する工程と;(d)活性の容積を算出するために、パラメータ方程式を解く工程と、を含む。算出された活性の容積は、表示器スクリーン上に表示することができる。
(a)電極の電場モデルおよび神経組織モデルを備える工程と;(b)前記電場モデルを前記神経組織モデルに結合することによって、刺激パラメータおよび電極構成パラメータの多くの異なるセットについて、活性の容積を取得する工程と;(c)前記活性の容積に幾何形状を適合させる工程であって、前記幾何形状は、1または複数のパラメータ方程式によって画定され、そして算出学習アルゴリズムを使用して、刺激パラメータおよび電極構成パラメータの前記異なるセットを、前記活性の容積に適合される幾何形状を表す、前記1または複数のパラメータ方程式のための前記パラメータに、相関させる関数を設計する工程と、を含む。別の実施形態では、本発明によって、持続性コンピュータ可読記憶媒体が提供され、その媒体は、そのパラメータが、刺激パラメータおよび電極構成パラメータを含む入力ベクトルの関数として与えられる、1または複数のパラメータ方程式を使用して、活性の容積を決定するためのインストラクションを含み、その関数は、この方法によって取得される。
ることができる。それゆえ、VTAモデル24は、1または複数のパラメータ方程式26のセットを含むことができ、それは、入力データ16に基づき、出力されるVTA12を計算する上で使用するために、個別に、またはそのいずれもの組み合わせで、人工ニューラルネットワーク18中で利用することができる。
Transaction on Neural Networks,Vol.2,No.2,252−262に述べられている。人工ニューラルネットワーク18の層中で利用することができる線形伝達関数の例は、ジョニック エス(Jonic S.),ヤンコビック ティ(Jankovic T.),ガジック ブイ(Gajic V.),ポポビック ディ(Popovic D.)(March 1999)”Three machine learning techniques for automatic d
etermination of rules to control locomotion”,IEEE Trans.On Biomedical Engineering,Vol.46,No.3,300−310に述べられている。
Method”in Practical Optimization,London:Academic Press,136 −137、レーベンバーグ ケー(Levenberg K.)(1944)”A Method for the Solution of Certain Problems in Least Squares”,Quart.Appl.Math.Vol.2,164−168、マルカルト ディ(Marquardt D.)(1963)”An Algorithm for Least−Squares Estimation of Nonlinear Parameters”,SIAMJ.Appl.Math.Vol.11,431−441に開示されている。
推定器14を学習させるために利用することができる。利用できるケースのサブセットは、過剰な適合化を避けるために検証データとして使用されることができ、さらに別のサブセットまたは残されたケースは、一般化誤差を推定するために利用されることができる。ニューラルネットワークの重みおよびバイアスは、ランダムに初期化されることができる。各人工ニューラルネットワーク18は、正規化適合平均二乗誤差(MSE:mean squared error)が、所定の値(たとえば、10−5より小さい)に達するまで、検証誤差が、適合化誤差に対して増加するまで、またはエポックの最大数(たとえば、1000)の間、学習させられうる。
うに動作するモデルセレクタ46を含むことができる。モデルセレクタ46は、たとえばユーザインタフェース32を介して受け取ったユーザ入力に応答して、マニュアルで実行されうる。あるいは、モデルセレクタ46は、入力データの基づき、パラメータ方程式の適切な1または複数を選択するようにプログラムされる自動的な工程として、実行されうる。たとえばモデルセレクタ46は、制約付き最適化アルゴリズムを用いて、多くの(たとえば、3つの)構成要素から形成される費用関数を最小化し、かつパラメータ方程式26とVTAに対応する線維活性の広がりの間の最大限の適合化を保証することができる。さらなる例として、第1の費用関数の構成要素は、我々のパラメータ方程式の最先端縁部(水平面と垂直面の両方上)と2D等高線の間の差に対応することができる。第2の構成要素は、パラメータ方程式によってカバーされる領域と2D等高線の間の周辺長さの差に対応することができる。第3の構成要素は、パラメータ方程式によってカバーされる領域と2Dアクティブ線維等高線によってカバーされる領域の差に対応することができる。
Methods for Constrained Optimization,”Mathematical Programming:The State of the
Art,(エー バッケン(A.Bachem),エム グロシュル(M.Grotschel)およびビー コルト(B.Korte)編)Springer Verlag.288−311、フレチャ アール(Fletcher R.),(1987)”Practical Methods of Optimization,”John Wiley and Sons、ジル ピー アール(Gill P.R.),マリー ダブル(Murray W.),ライト エム エイチ(Wright M.H.)(1981)”Levenberg−Marquardt Method”in Practical Optimization,London:Academic Press,136 −137、ホック ダブル(Hock W.),シュトコウスキー ケー(Schittkowski K.),(1983年)”A Comparative Performance Evaluation of 27 Nonlinear Programming Codes”、Computing,Vol.30,335に詳細に述べられている。当業者は、適切なパラメータ方程式26の選択のために利用することができる、他の市販の、および所有権のあるツールおよびソフトウェアを理解し認識されるであろう。
たとえば入力データのセットを決定するようにシステムを設定するための入力モードを選択するために使用されうる。入力データのセットは、所望の目標VTAを提供するために利用されうる。またシステム10は、目標セレクタ36を含むことができる。目標セレクタ36は、活性化されるべき組織の所望の目標容積38を、画定するおよび/または設定するために利用されうる目標容積38は、2−Dおよび/または3−D表示に対応するなどのユーザインタフェースを介して選択されることができ、2−Dおよび/または3−D表示は、対応する表示器40上に表示されうる。たとえば表示器40は、目標容積がその中に存在する、脳などの解剖部位を、3次元または2次元で表示できる。表示器上に表示される3−Dモデルは、脳構造の一般モデルに対応することができる。あるいは表示器40上に表示されるモデルは、対応する画像診断法(たとえば、CTスキャン、またはMRIなど)を使用して生成することができるような、患者特定モデルに対応することができる。
次の議論は、本明細書に述べる概念のための基盤となる実験および処置を含む、発明者らの活動に関するものであり、人工ニューラルネットワーク中での使用に適した学習データのためにパラメータ方程式およびVTAを決定するために使用される処置を含む。
計算モデル化研究は、脳組織中に挿入されたバーチャル電極モデルを含んでいた。バーチャル電極は、メドトロニック(Medtronic)3389、3387および3391の脳深部刺激電極(図8を参照)を表すように、モデル化された。各バーチャル電極について、150を超える軸対称多重解像度有限要素モデル(FEM:finite element models)が、脳組織内の電場をモデル化するために、生成された。FEMは、コムソル(COMSOL)、(コムソル社(Comsol Inc.),米国マサチューセッツ州バーリントン(Burlington,MA)所在)およびSCIRun3.3(サイエンティフィック コンピューティング アンド イメージング インスティチュート(Scientific Computing and Imaging Institute),米国ユタ州ソルトレイクシティ(Salt Lake City,UT)所在)を使用して生成され、バーチャル電極の表現、電極を囲繞する脳組織の導電率、電極・組織界面でのキャパシタンス、電極まわりのカプセル化組織の薄層、および臨床用途で通常使用される刺激設定を含んでいた。
電場モデルは、ニューロン活性を決定するために、神経組織モデルに結合された。図9に示すように、神経組織モデルは、2,500を超える白質軸索線維の軌跡を有する。白質軸索線維は、電極シャフトに対して垂直に母材中で分布され、線維間分離は、垂直(背腹)および水平(内側横)軸に沿って0.2mmであった。軸索集団は、電極シャフトに対して横方向に約0.7〜11.0mmで、電極の先端の上に−7.0〜+32.0mmで位置付けられた。図4は、電極シャフトに隣接する母材中にどのように軸索が配列されうるかを示す別の図である。有髄軸索の多重区画モデルは、これら軸索のそれぞれを表すために、生成された。これらモデルのための軸索パラメータは、5.7μmに軸索についてMclntyreら(マッキンタイヤ(Mclntyre)ら、J Neurophysiol,2002,Feb;87(2):995−1006参照)に従って定義された。各軸索の軌跡を明確に画定するために求められる幾何形状は、Matlab(マスワークス社(MathWorks Inc.),米国マサチューセッツ州ナティック(Natick,MA)所在)を使用して決定された。軸索の垂直の方向付けのため、電極シャフトに対する活性の広がりを決定することができた。
向の距離を取得することによって、画定された。これは、軸対称のシミュレーションモデルであるので、パラメータ方程式によって生成される2D楕円形の外形を電極シャフト(z軸)のまわりで回転させることによって、3次元VTAを生成できた。図5および図6は、各アクティブ部位60中の線維64の広がりを測定することによって、決定されうるアクティブ部位60の例を示す。各アクティブ部位60の楕円形外形66は、電極コンタクト62に平行な平面上で、電極シャフト上に中心を置く2D等高線である。縁部66は、そのそれぞれのアクティブ部位60に最適に適合された楕円を画定する。図11は、電極コンタクト活性のための異なる構成下でのアクティブ部位60の別の例を示す。アクティブ部位60に適合される、パラメータ方程式の楕円形外形66は、アクティブ部位60のまわりの白線として示される。電極68上に、カソードであるアクティブコンタクト62を、空白の円として示し、アノードであるアクティブコンタクト62を、斜交平行線の円として示す。
30%は、検証およびニューラルネットワークの性能の査定のために使用された。人工ニューラルネットワークそれぞれの初期重みは、ランダムに初期化され、学習を終了させるために使用される停止基準は、10−5より小さい二乗平均誤差を含み、または500エポックまでとした。
*対応するC0−3コンタクトがアクティブである場合、e0−3を1に設定する、
*コンタクトC0−3中にアノードが存在しない場合、隣接するカソードがあるか否か決定する、
*隣接するカソードがある場合、最も低い隣接するカソードのコンタクト(複数可)に対応するアクティブe0−3に1を設定し、より高い隣接するカソードのコンタクト(複数可)に対応するアクティブe0−3に0を設定する。この疑似アルゴリズムは、何らかの重複が存在する場合、隣接するアクティブ部位が、どのようにして結合されうるのかの例を明示する。
正規化出力(Onorm)の算出が、一般化シグモンドベースの方程式
上記に説明したように、いくつかの場合、計算学習アルゴリズムは、人工ニューラルネットワークを使用する。人工ニューラルネットワークは、電極パラメータと、幾何形状に関するパラメータ方程式のパラメータの間のマッピングを推論するために、使用されうる。その幾何形状は、電極パラメータのセットと相互に関係付けられる活性の容積に適合される。入力としてこの学習データセットを使用すると、人工ニューラルネットワークの出力は、この関係をマッピングする関数を設計するために使用されうる。図10は、本発明において使用することができる人工ニューラルネットワークの例を示す。この人工ニューラルネットワークは、3つの層、すなわち入力層(左側のノード)、出力層(右側のノード)、および中間の隠れニューロン層(中間のノード)を有する。差し込み図は、スループットのパラメータを変更し且つニューラルネットワーク中のニューラル接続を変えるために、重み(Wn,h)およびバイアスが、どのように使用されるのか示す。
入力ベクトルの関数として与えられる、1または複数のパラメータ方程式を使用して、活性の容積を決定するためのインストラクションを含む。その入力ベクトルは、刺激パラメータおよび/または電極構成パラメータを含む。
また本発明の或る実施形態は、方法、システムおよびコンピュータプログラム製品のブロック図を参照して、本明細書で説明された。図のブロックおよび図中のブロックの組み合わせを、コンピュータ実行可能なインストラクションによって実施することができることは、理解されるであろう。これらのコンピュータ実行可能なインストラクションは、汎用コンピュータ、専用コンピュータ、または他のプログラマブルデータ処理装置(または、装置および回路の組み合わせ)の1または複数のプロセッサに提供される。プロセッサを介して実行するインストラクションがブロックまたは複数のブロック中に規定された機能を実現するような機械は、製造されうる。
込むために、ハードディスクドライブ507、磁気ディスクドライブ508を含んでもよい。ハードディスクドライブ507、磁気ディスクドライブ508および光ディスクドライブ510はそれぞれ、ハードディスクドライブインタフェース512、磁気ディスクドライブインタフェース513および光ディスクドライブインタフェース514それぞれによって、システムバス503に接続される。これらのドライブおよびそれらの付随するコンピュータ可読媒体は、コンピュータシステム500のために、データ、データ構造、およびコンピュータ実行可能なインストラクションを不揮発性で格納する。上記のコンピュータ可読媒体の記述がハードディスク、リムーバブル磁気ディスクおよびCDを参照しているとはいえ、磁気カセット、フラッシュメモリカード、デジタルビデオディスクなど、コンピュータが可読の他のタイプの持続性コンピュータ可読媒体も、様々な形式で、動作環境中に使用されることができる。さらに、かかる媒体は、いずれも、本発明の1または複数の部分を実施するためのコンピュータ実行可能なインストラクションを含むことができる。用語「持続性コンピュータ可読記憶媒体」は、すべてのコンピュータ可読記憶媒体を包含し、唯一の例外が、一時的な伝播信号である。
モデムを含むことができ、またはLAN上の通信サーバに接続されうる。モデムは、内部に有する、または外付けとすることができ、適切なポートインターフェースを介して、システムバス503に接続することができる。ネットワーク環境下では、コンピュータシステム500に関連して示したアプリケーションプログラム516またはプログラムデータ518、またはその一部分は、遠隔メモリ記憶装置540中に格納することができる。
Claims (22)
- コンピュータによって実行される、神経組織の活性の容積を決定するための方法であって、
刺激パラメータのための値を有する入力データを受取る工程と;
関数に前記刺激パラメータのための値を有する入力ベクトルを、入力として適用する工程であって、前記関数は、
a)計算学習アルゴリズムから生成された関数であり、前記計算学習アルゴリズムは、刺激パラメータの値および電極構成パラメータの値の領域を、前記刺激パラメータの値および電極構成パラメータの値の前記領域によって生成される活性の容積に、マッピングし、
b)1つまたは複数のパラメータ方程式のパラメータの値を出力する関数であり、前記1つまたは複数のパラメータ方程式は3次元幾何形状で表現される、工程と;
前記関数により出力された前記パラメータの値を用いて前記1つまたは複数のパラメータ方程式を解くことによって、前記活性の容積を算出する工程と;
算出された前記活性の容積を表示器スクリーン上に表示する工程と
を有する方法。 - 前記電極構成パラメータは、
(i)前記電極の高さと直径との少なくとも1つと;
(ii)前記電極の1以上の電極コンタクトの分布と
のうちの少なくとも1つを含む、請求項1に記載の方法。 - 前記1つまたは複数のパラメータ方程式の前記パラメータの値は、前記入力ベクトルの線形関数として与えられる、請求項1に記載の方法。
- 前記入力ベクトルはさらに、電極構成パラメータを含み、
前記入力データはさらに、前記電極構成パラメータのための値を含む、請求項1に記載の方法。 - 請求項1に記載の方法であって、さらに、
電極の電場モデルを神経組織モデルに結合し、複数の異なるセットの刺激パラメータの値および電極構成パラメータの値に対する複数の活性の容積を得る、結合工程と;
1つの3次元幾何形状を前記複数の活性の容積に適合させる工程であって、同3次元幾何形状は前記1つまたは複数のパラメータ方程式により定義される、適合工程と;
前記計算学習アルゴリズムを使用し、前記関数を設計する工程であって、前記関数は、入力として、刺激パラメータの値および電極構成パラメータの値を組み入れる、使用工程とを含む、方法。 - 前記適合工程は最適化アルゴリズムを用いて実行される、請求項5に記載の方法。
- 前記3次元幾何形状は楕円体である、請求項5に記載の方法。
- 前記計算学習アルゴリズムは1つまたは複数の人工ニューラルネットワークを用いる、請求項5に記載の方法。
- 前記3次元幾何形状は楕円体形状である、請求項1に記載の方法。
- 刺激パラメータの値および電極構成パラメータの値の前記領域によって生成される前記活性の容積は、前記1つまたは複数のパラメータ方程式によって画定される3次元幾何形状として表される、請求項1に記載の方法。
- 前記活性の容積は、神経組織中に挿入される電極の電場モデルを、神経組織モデルに結合することにより取得される、請求項1に記載の方法。
- 前記計算学習アルゴリズムは、1つまたは複数の人工ニューラルネットワークを生成する、請求項1に記載の方法。
- 新しい学習データに基づき、前記1つまたは複数の人工ニューラルネットワークを、現在の学習させた状態から再学習させた状態へと再学習させ、同1つまたは複数の人工ニューラルネットワークの領域を拡大する工程を更に有する、請求項12に記載の方法。
- 算出された前記活性の容積と目標の活性の容積とを相関させ、所与の患者のための理論的に最適な刺激パラメータ設定の指示を提供する工程を更に有する、請求項1に記載の方法。
- 前記入力ベクトルは、パルス幅と、電極コンタクトインピーダンスと、カプセル化組織伝導率と、電圧と、電極コンタクト構成とのうちの1つまたは複数を含む、請求項1に記載の方法。
- 算出された前記活性の容積は、前記1つまたは複数のパラメータ方程式の解から直接取得される、請求項1に記載の方法。
- 工程を実施するようにプログラムされるコンピュータシステムであって、前記工程は、
刺激パラメータのための値を含む入力データを受け取る工程と;
関数に前記刺激パラメータのための値を有する入力ベクトルを適用する工程であって、前記関数は、
a)計算学習アルゴリズムから生成された関数であり、前記計算学習アルゴリズムは、刺激パラメータの値および電極構成パラメータの値の領域を、前記刺激パラメータの値および電極構成パラメータの値の前記領域によって生成される活性の容積に、マッピングすることと;
b)1つまたは複数のパラメータ方程式のパラメータの値を出力する関数であり、前記1つまたは複数のパラメータ方程式は3次元幾何形状で表現される、工程と;
前記関数により出力された前記パラメータの値を用いて前記1つまたは複数のパラメータ方程式を解くことによって、前記活性の容積を算出する工程と
を有する、コンピュータシステム。 - コンピュータに手順を実行させるためのコンピュータ読取可能なプログラムを記録する持続性コンピュータ可読媒体であって、前記手順は、
刺激パラメータのための値を含む入力データを受け取る手順と;
関数に前記刺激パラメータのための値を有する入力ベクトルを適用する手順であって、前記関数は、
a)計算学習アルゴリズムから生成された関数であり、前記計算学習アルゴリズムは、刺激パラメータの値および電極構成パラメータの値の領域を、前記刺激パラメータの値および電極構成パラメータの値の前記領域によって生成される活性の容積に、マッピングすることと;
b)1つまたは複数のパラメータ方程式のパラメータの値を出力する関数であり、前記1つまたは複数のパラメータ方程式は3次元幾何形状で表現される、手順と;
前記関数により出力された前記パラメータの値を用いて前記1つまたは複数のパラメータ方程式を解くことによって、前記活性の容積を算出する手順と
を有する、持続性コンピュータ可読記憶媒体。 - 請求項21に記載の持続性コンピュータ可読記憶媒体であって、
前記関数は、
電極の電場モデルを神経組織モデルに結合し、複数の異なるセットの刺激パラメータの値および電極構成パラメータの値に対する複数の活性の容積を得る、手順と;
3次元幾何形状を前記複数の活性の容積に適合させる工程であって、同3次元幾何形状は前記1つまたは複数のパラメータ方程式により定義される、手順と;
前記計算学習アルゴリズムを使用し、前記関数を設計する工程であって、前記関数は、入力として、刺激パラメータの値および電極構成パラメータの値を組み入れる、手順とにより取得される、持続性コンピュータ可読記憶媒体。
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JP2016209644A (ja) * | 2011-03-24 | 2016-12-15 | カリフォルニア インスティテュート オブ テクノロジー | 神経刺激装置 |
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WO2011025865A1 (en) | 2011-03-03 |
CA2772330A1 (en) | 2011-03-03 |
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AU2010286603A1 (en) | 2012-03-22 |
US20130218819A1 (en) | 2013-08-22 |
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