WO2022067071A1 - Systèmes d'enregistrement et d'analyse de signaux d'électroencéphalogramme pour la détection de troubles du cerveau - Google Patents

Systèmes d'enregistrement et d'analyse de signaux d'électroencéphalogramme pour la détection de troubles du cerveau Download PDF

Info

Publication number
WO2022067071A1
WO2022067071A1 PCT/US2021/052007 US2021052007W WO2022067071A1 WO 2022067071 A1 WO2022067071 A1 WO 2022067071A1 US 2021052007 W US2021052007 W US 2021052007W WO 2022067071 A1 WO2022067071 A1 WO 2022067071A1
Authority
WO
WIPO (PCT)
Prior art keywords
eeg
information transfer
disease
brain
condition
Prior art date
Application number
PCT/US2021/052007
Other languages
English (en)
Inventor
Todd ZORICK
Original Assignee
Lundquist Institute For Biomedical Innovation At Harbor-Ucla Medical Center
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Lundquist Institute For Biomedical Innovation At Harbor-Ucla Medical Center filed Critical Lundquist Institute For Biomedical Innovation At Harbor-Ucla Medical Center
Publication of WO2022067071A1 publication Critical patent/WO2022067071A1/fr

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor

Definitions

  • the present technology provides systems and methods that detects and analyze electroencephalography (EEG) signals, which is helpful for making diagnosis of diseases or conditions of the brain.
  • EEG electroencephalography
  • the analysis uses two approaches, multifractal detrended fluctuation analysis (MF-DFA) and information transfer modeling (ITM), separately or in combination.
  • MF-DFA multifractal detrended fluctuation analysis
  • ITM information transfer modeling
  • FIG. 1 illustrates equipment for recording electroencephalography (EEG) signals from a subject.
  • EEG electroencephalography
  • FIG. 4 shows correlations of FT- and MF-DFA EEG-derived CART model for TPM with actual TPM scores.
  • a specific length of EEG signal may be acquired from each lead.
  • the length may be 5 seconds, 10 seconds, 15 seconds, 20 seconds, 25 seconds, 30 seconds, 40 seconds, 50 seconds, 60 seconds, 90 second or 120 seconds, without limitation.
  • EEG signals from all leads are used for further analysis.
  • a subset of selected leads are used.
  • the EEG signals from one or more of the leads are used as reference (e.g., at a mastoid).
  • the EEG signals are analyzed with multifractal detrended fluctuation analysis (MF-DFA).
  • MF-DFA multifractal detrended fluctuation analysis
  • one embodiment provides a computer- implemented method for detecting a disease or condition in the brain of a human subject.
  • the method entails recording, with a plurality of electrodes placed on the scalp of the human subject, a period of electroencephalography (EEG) signals from each of the electrodes, extracting, from the EEG signals from each electrode, signal parameters, calculating, from the signal parameters, a multifractal spectrum, and correlating the multifractal spectrum to a reference multifractal spectrum associated with a disease or condition in the brain, thereby identifying the human subject as having the disease or condition.
  • EEG electroencephalography
  • a reference multifractal spectrum is not required. Instead, the calculated multifractal spectrum may be fit into a prediction model with suitable parameters.
  • the computer system 600 may include a user interface module to implement a GUI that may be stored in a mass storage device as executable software codes that are executed by the computing device(s).
  • This and other modules may include, by way of example, components, such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.
  • non-transitory media refers to any media that store data and/or instructions that cause a machine to operate in a specific fashion. Such non- transitory media may comprise non-volatile media and/or volatile media.
  • Non-volatile media includes, for example, optical or magnetic disks, such as storage device 610.
  • Volatile media includes dynamic memory, such as main memory 606.
  • a network link typically provides data communication through one or more networks to other data devices.
  • a network link may provide a connection through local network to a host computer or to data equipment operated by an Internet Service Provider (ISP).
  • ISP Internet Service Provider
  • the ISP in turn provides data communication services through the world-wide packet data communication network now commonly referred to as the “Internet”.
  • Internet Internet
  • Local network and Internet both use electrical, electromagnetic or optical signals that carry digital data streams.
  • the signals through the various networks and the signals on network link and through communication interface 618, which carry the digital data to and from computer system 600, are example forms of transmission media.
  • the computer system 600 can send messages and receive data, including program code, through the network(s), network link and communication interface 618.
  • a server might transmit a requested code for an application program through the Internet, the ISP, the local network and the communication interface 618.
  • lead 1 lower right frontal
  • lead 9 middle left frontal
  • lead 24 left temporal
  • lead 52 right temporal
  • lead 6 (middle frontal), leads 26/36 (left temporal to midline parietal), and lead 3 (middle right frontal) were parameters.
  • leads 19/31 left frontal to left parietal
  • lead 3 (middle right frontal)
  • lead 36 (midline parietal)
  • lead 56 (lateral right frontal) were parameters.
  • lead 3 (middle right frontal)
  • lead 40 right parietal
  • lead 9 (middle left frontal) were CART model parameters.
  • Example 1 tested a further improvement from Example 1.
  • the EEG were separately analyzed with ITM and MF-DFA, and the parameters were then combined in a machine learning model (support vector machine) to find the best predictors.
  • machine learning model support vector machine

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Physics & Mathematics (AREA)
  • Surgery (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Psychiatry (AREA)
  • Animal Behavior & Ethology (AREA)
  • Pathology (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physiology (AREA)
  • Signal Processing (AREA)
  • Psychology (AREA)
  • Evolutionary Computation (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

Sont décrits des systèmes et des procédés pour acquérir et analyser des signaux d'électro-encéphalographie (EEG) et utiliser les résultats d'analyse pour calculer des paramètres d'évaluation de l'état cérébral tels que l'accomplissement (ACH) associé au rapport de précision moyen (MAR), au premier temps de déplacement (FMT), au rapport de violation de règle par item (RVPI) et au total de violation de règle (TRV). Ces paramètres peuvent ensuite être utilisés pour diagnostiquer des états et des maladies du cerveau tels qu'une lésion cérébrale traumatique (TBI), la maladie d'Alzheimer (AD), un trouble cognitif léger (MCI), une lésion de lobe frontal, un trouble déficitaire de l'attention avec ou sans hyperactivité, un trouble de l'apprentissage spécifique, un trouble de l'humeur, un trouble bipolaire, des troubles du spectre autistique, un syndrome d'alcool foetal, un trouble neuro-inflammatoire et un spina bifida.
PCT/US2021/052007 2020-09-25 2021-09-24 Systèmes d'enregistrement et d'analyse de signaux d'électroencéphalogramme pour la détection de troubles du cerveau WO2022067071A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202063083652P 2020-09-25 2020-09-25
US63/083,652 2020-09-25

Publications (1)

Publication Number Publication Date
WO2022067071A1 true WO2022067071A1 (fr) 2022-03-31

Family

ID=80846905

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2021/052007 WO2022067071A1 (fr) 2020-09-25 2021-09-24 Systèmes d'enregistrement et d'analyse de signaux d'électroencéphalogramme pour la détection de troubles du cerveau

Country Status (1)

Country Link
WO (1) WO2022067071A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115770044A (zh) * 2022-11-17 2023-03-10 天津大学 基于脑电相位幅值耦合网络的情绪识别方法及装置

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120296569A1 (en) * 2010-01-18 2012-11-22 Elminda Ltd. Method and system for weighted analysis of neurophysiological data
US20160106331A1 (en) * 2013-04-22 2016-04-21 The Regents Of The University Of California Fractal index analysis of human electroencephalogram signals
WO2016110804A1 (fr) * 2015-01-06 2016-07-14 David Burton Systèmes de surveillance pouvant être mobiles et portes

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120296569A1 (en) * 2010-01-18 2012-11-22 Elminda Ltd. Method and system for weighted analysis of neurophysiological data
US20160106331A1 (en) * 2013-04-22 2016-04-21 The Regents Of The University Of California Fractal index analysis of human electroencephalogram signals
WO2016110804A1 (fr) * 2015-01-06 2016-07-14 David Burton Systèmes de surveillance pouvant être mobiles et portes

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115770044A (zh) * 2022-11-17 2023-03-10 天津大学 基于脑电相位幅值耦合网络的情绪识别方法及装置
CN115770044B (zh) * 2022-11-17 2023-06-13 天津大学 基于脑电相位幅值耦合网络的情绪识别方法及装置

Similar Documents

Publication Publication Date Title
JP4987338B2 (ja) 認知能減退を予測するためのシステムおよび方法
Park et al. Epileptic seizure detection for multi-channel EEG with deep convolutional neural network
Mumtaz et al. A wavelet-based technique to predict treatment outcome for major depressive disorder
JP7240789B2 (ja) 脳症／せん妄のスクリーニングおよびモニタリングのためのシステム
Bairagi EEG signal analysis for early diagnosis of Alzheimer disease using spectral and wavelet based features
JP2005514096A (ja) Ｅｅｇバイスペクトルを用いて神経学的症状を評価する系および方法
US20090264785A1 (en) Method and Apparatus For Assessing Brain Function Using Diffusion Geometric Analysis
Chen et al. DCCA cross-correlation coefficients reveals the change of both synchronization and oscillation in EEG of Alzheimer disease patients
JP2005514096A5 (fr)
Zeng et al. Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes
Rajendran et al. EEG based evaluation of examination stress and test anxiety among college students
US20180279938A1 (en) Method of diagnosing dementia and apparatus for performing the same
Kerr et al. Automated diagnosis of epilepsy using EEG power spectrum
Lin et al. Early prediction of medication refractoriness in children with idiopathic epilepsy based on scalp EEG analysis
Wong et al. EEG datasets for seizure detection and prediction—A review
Ruiz-Gómez et al. Measuring alterations of spontaneous EEG neural coupling in Alzheimer's disease and mild cognitive impairment by means of cross-entropy metrics
Yaacob et al. Application of artificial intelligence techniques for brain-computer interface in mental fatigue detection: a systematic review (2011-2022)
Vicchietti et al. Computational methods of EEG signals analysis for Alzheimer’s disease classification
WO2022067071A1 (fr) Systèmes d'enregistrement et d'analyse de signaux d'électroencéphalogramme pour la détection de troubles du cerveau
Perez-Valero et al. Supporting the detection of early Alzheimer’s disease with a four-channel EEG analysis
Ponten et al. Feasibility of online seizure detection with continuous EEG monitoring in the intensive care unit
Koster et al. Sensitivity of magnetoencephalography as a diagnostic tool for epilepsy: a prospective study
Bernabei et al. A full-stack application for detecting seizures and reducing data during continuous electroencephalogram monitoring
Chen et al. Interictal EEG and ECG for SUDEP risk assessment: a retrospective multicenter cohort study
Pattnaik et al. The seizure severity score: a quantitative tool for comparing seizures and their response to therapy

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21873539

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21873539

Country of ref document: EP

Kind code of ref document: A1