JP2012516210A - 脳機能の確率的客観的査定のための方法および装置 - Google Patents
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Abstract
Description
a. 信号を複数の信号成分に変換する
b. 成分のフラクタル次元を算出する
c. そのフラクタル次元に基づいて、雑音成分を識別する
d. 識別された雑音成分を自動的に減衰させる
e. 逆変換を使用して、雑音除去された信号を再構築する
入力アナログ脳電気信号は、最初に、デジタル化され、次いで、高速フーリエ変換、独立成分分析(ICA)ベースの変換、ウェーブレット変換、ウェーブレットパケット変換等、線形または非線形信号変換方法を使用して、その構成的係数に分解される。次いで、係数のフラクタル次元が、変換領域内で計算され、既定閾値より高いフラクタル次元を有する係数は、減衰される。次いで、損なわれていない、リスケールされた係数は、逆信号変換を使用してリミックスされ、信号特徴を抽出し、抽出された特徴を分類するためにさらに処理される雑音除去された信号を発生させる。
G(1)=exp(Z.WA+CA)、G(2)=exp(Z.WB+CB)
式中、Zは、任意の対象に対して算出された、年齢退行され、z−変換された特徴のベクトルを表す。gB=100−gAであるため、gAのみ、本書の残りでは取り扱われ、「判別出力/スコア」と称され、g(または、z−変換された特徴の関数であることを強調するために、g(Z))によって単純に表されることに留意されたい。WAおよびWBは、基準データベースから導出される、2つの加重ベクトルを表し、CAおよびCBは、一般的に、バイアスまたは閾値加重と呼ばれる、2つの定数である。加重および定数は、線形判別関数を全体的に定義し、クラス間の「最良」の分離をもたらすように、トレーニングルーチンを使用して、事前に選択される。異なる単極性および/または双極性一変量ならびに多変量特徴のための加重は、正常脳状態を有する第1の複数の個人の脳電気活動を示す母集団規範的データ、または異常脳状態を有する第2の複数の個人の脳電気活動を示す母集団基準データから成る、データベース等、格納された母集団基準データベースから予測され得る。同様に、加重は、異常脳状態の有無下で生成された対象独自の脳電気活動データのデータベースから選択され得る。いくつかの実施形態では、分類タスクは、1つ以上の線形判別関数を使用して行われてもよく、そのような場合、判別出力は、多数決規則を使用して、組み合わせられ得る。
Claims (32)
- 対象の脳状態を査定する方法であって、
少なくとも1つの電極チャネルを使用して、該脳から電気信号を取得するステップと、
該取得した信号から定量的特徴を抽出するステップと、
該抽出された特徴を1つ以上の脳状態に分類するステップと、
該分類の正確性を示す確率的指数を算出するステップと
を含む、方法。 - 異なる分類タスクに対応する複数の確率的指数が算出される、請求項1に記載の方法。
- 前記複数の確率的指数は、多次元ベクトルとして報告される、請求項2に記載の方法。
- 前記抽出された特徴を分類するステップは、基準データベースを使用して行われる、請求項2に記載の方法。
- 前記基準データベースは、脳異常の有無下の複数の個人からの電気脳活動信号/データを備える、請求項4に記載の方法。
- 前記基準データベース内の脳電気活動データから生成された確率的指数をグラフでプロットすることによって、指数マップを導出するステップをさらに含む、請求項4に記載の方法。
- 新しいデータを前記指数マップに組み込むことによって、該指数マップ内の決定境界を修正するステップをさらに含む、請求項6に記載の方法。
- 前記多次元ベクトルは、前記指数マップ上にプロットされる、請求項7に記載の方法。
- 前記算出された指数は、正常対異常分類の正確性を示す、脳異常指数(BAI)である、請求項1に記載の方法。
- 前記算出された指数は、限局性対拡散性分類の正確性を示す、脳限局性指数(BFI)である、請求項1に記載の方法。
- 前記脳からの電気信号は、自発的電気活動を含む、請求項1に記載の方法。
- 前記脳からの電気信号は、誘発電位を含む、請求項1に記載の方法。
- 前記脳からの電気信号は、自発的電気活動および誘発電位を含む、請求項1に記載の方法。
- 前記特徴抽出ステップは、拡散幾何分析を使用して行われる、請求項1に記載の方法。
- 前記特徴抽出ステップは、ウェーブレットパケット変換を使用して行われる、請求項1に記載の方法。
- 前記特徴抽出ステップは、高速フーリエ変換を使用して行われる、請求項1に記載の方法。
- 前記分類は、2値分類器を使用して行われる、請求項1に記載の方法。
- 前記分類器は、線形判別関数分類器である、請求項17に記載の方法。
- 前記確率的指数は、判別分類スコアから算出される、請求項18に記載の方法。
- 受信者動作特性(ROC)曲線をプロットするステップをさらに含む、請求項19に記載の方法。
- 脳状態の査定は、可搬型携帯デバイスを使用して行われる、請求項1に記載の方法。
- 対象の脳状態を査定するための装置であって、
脳電気信号を取得するための少なくとも1つの電極を備えるヘッドセットと、
ベースユニットと
を備え、該ベースユニットはプロセッサを備え、該プロセッサは、
メモリ内に格納された1つ以上の動作命令を利用して、該脳電気信号から特徴抽出を行うことと、該抽出された信号特徴を1つ以上の脳状態に分類することと、分類正確性の確率的指数を算出することとを行うように構成される、装置。 - 前記プロセッサは、前記対象の脳状態の客観的査定を出力するように構成される、請求項22に記載の装置。
- 前記プロセッサは、前記確率的指数を出力するように構成される、請求項23に記載の装置。
- ディスプレイをさらに備え、前記プロセッサによって行われる1つ以上の動作の結果が、表示される、請求項22に記載の装置。
- 前記確率的指数は、水平指数バーとして、前記ディスプレイ上に報告される、請求項25に記載の装置。
- 前記確率的指数は、多次元ベクトルとして、前記ディスプレイ上に報告される、請求項25に記載の装置。
- 前記確率的指数から生成される指数マップが、前記ディスプレイ上に報告される、請求項27に記載の装置。
- 前記多次元ベクトルは、前記指数マップ上に重畳される、請求項28に記載の装置。
- 前記指数マップは、前記メモリ内に格納される、請求項28に記載の装置。
- 前記指数マップは、異なる脳状態に対応する複数の領域に区切られる、請求項28に記載の装置。
- 前記プロセッサは、ユーザから命令を受け、前記指数マップ上の脳状態に対応する決定境界を修正するように構成される、請求項30に記載の装置。
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US8364254B2 (en) | 2009-01-28 | 2013-01-29 | Brainscope Company, Inc. | Method and device for probabilistic objective assessment of brain function |
US10321840B2 (en) | 2009-08-14 | 2019-06-18 | Brainscope Company, Inc. | Development of fully-automated classifier builders for neurodiagnostic applications |
US20110087125A1 (en) * | 2009-10-09 | 2011-04-14 | Elvir Causevic | System and method for pain monitoring at the point-of-care |
US9271651B2 (en) * | 2009-11-30 | 2016-03-01 | General Electric Company | System and method for integrated quantifiable detection, diagnosis and monitoring of disease using patient related time trend data |
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