DK2040608T3 - Analyse af eeg-signaler for detektering af hypoglykæmi - Google Patents

Analyse af eeg-signaler for detektering af hypoglykæmi Download PDF

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DK2040608T3
DK2040608T3 DK07729990.7T DK07729990T DK2040608T3 DK 2040608 T3 DK2040608 T3 DK 2040608T3 DK 07729990 T DK07729990 T DK 07729990T DK 2040608 T3 DK2040608 T3 DK 2040608T3
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eeg signals
eeg
signals
hypoglycemia
time
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DK07729990.7T
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Henning Beck-Nielsen
Rasmus Elsborg Madsen
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Hypo Safe As
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
    • 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]
    • 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]
    • A61B5/37—Intracranial electroencephalography [IC-EEG], e.g. electrocorticography [ECoG]
    • 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
    • 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
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms
    • 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

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  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Medical Informatics (AREA)
  • Surgery (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Veterinary Medicine (AREA)
  • Molecular Biology (AREA)
  • Public Health (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Psychiatry (AREA)
  • Psychology (AREA)
  • Optics & Photonics (AREA)
  • Emergency Medicine (AREA)
  • Neurosurgery (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
  • Debugging And Monitoring (AREA)

Claims (15)

1. Fremgangsmåde til analyse af EEG-signaler for at detektere træk deri, der indikerer hypoglykæmi, hvilken fremgangsmåde omfatter: - inddeling af EEG-signaler i en sekvens af tidssegmenter, - for hvert tidssegment bestemmelse af, om et mønster af EEG-signaler er til stede, der indikerer hypoglykæmi, og når et mønster af EEG-signaler, der indikerer hypoglykæmi, bestemmes at være til stede i et tidssegment, registrering heraf som en hændelse, - integrering af antallet af hændelser, der er registreret i løbet af et udvalgt antal forudgående tidssegmenter, der sammen udgør en valgt tidsperiode, og - bestemmelse af, at EEG-signaleme indikerer, at hypoglykæmi er til stede baseret på integrationen.
2. Fremgangsmåde ifølge krav 1, hvor det bestemmes, at EEG-signaleme indikerer, at hypoglykæmi er til stede, når det integrerede antal hændelser overstiger et forhåndsbestemt tærskelantal, og/eller når der er et tærskelniveau for matching mellem en kurven for integration over tid og en tidligere fastsat ideel model af kurven, der indikerer hypoglykæmi.
3. Fremgangsmåde ifølge krav 1, hvor integrationen udføres som en vægtet integration, hvor hændelser detekteret i tidssegmenter endvidere tilbage i tiden far en en mindre vægtning end hændelser detekteret i nyere tidssegmenter.
4. Fremgangsmåde ifølge et hvilket som helst af de foregående krav, hvor bestemmelse af, om et mønster af EEG-signaler er til stede i et tidssegment, der indikerer hypoglykæmi, sker ved til signalerne at anvende en tidligere trænet Bayes-klassifikator, en support-vektor-maskine (SVM), en relevans-vektormaskine (RVM), en Gaussisk procesklassifikator, en klassifikator baseret på Fishers diskriminant, en forstærket klassifikator, en naiv Bayes-klassifikator, en K-nærmest-nabo—klassifikator, et binært beslutningstræ, en Parzen-vindue-klassifikator eller et neuralt netværk.
5. Fremgangsmåde ifølge krav 4, hvor der i løbet af en periode med monitorering af EEG-signaler foretages en omvurdering af den Gaussiske models middelværdi- og kovariansparametre over tid.
6. Fremgangsmåde ifølge et hvilket som helst af de foregående krav, hvor rå EEG-signaler udsættes for spektralanalyse for deraf at udlede middelværdier for effekt i mindst fire frekvensbånd, hvorpå bestemmelsen af, om et mønster af EEG-signaler er til stede, der indikerer hypoglykæmi i et tidssegment af signalerne, er baseret.
7. Fremgangsmåde ifølge krav 6, hvor de mindst fire frekvensbånd dækker frekvenser ffa 2Hz til og 32Hz, hvilke frekvensbånd indbefatter bånd på omtrent (2Hz-5Hz), (5Hz-8Hz), (8Hz-llHz), (11Hz14Hz) og (14Hz-32Hz), hvor samtlige grænser for frekvensbåndene kan variere med op til 20 %.
8. Fremgangsmåde ifølge et hvilket som helst af de foregående krav, hvor EEG-signaleme er opnået fra én eller flere elektroder, der er placeret inden for det afgrænsede område af FC3-C1-CP3-C5-positioneme og/eller i det ækvivalente område omkring C4.
9. Fremgangsmåde ifølge krav 8, hvor EEG-signaleme er opnået fra én eller flere elektroder placeret i mindst omtrent standard EEG-elektrodepositioneme C3 og/eller C4 eller P3 og/eller P4.
10. Fremgangsmåde ifølge krav 8 eller krav 9, hvor EEG-signaleme måles i forhold til signaler opnået fra en referenceelektrode i omtrent standard EEG-elektrodeposition Cz eller Pz.
11. Fremgangsmåde ifølge et hvilket som helst af de foregående krav, hvor EEG-signaler bestemmes til at indikere hypoglykæmi ved at fastslå, at et tærskelniveau for matching mellem integrationskurven over tid og en tidligere bestemt ideel model af kurven, der indikerer hypoglykæmi, eksisterer, hvor matching udføres ved hjælp af en matematisk konvolution af de målte og ideelle kurver.
12. Fremgangsmåde ifølge et hvilket som helst af de foregående krav, hvilken fremgangsmåde endvidere omfatter aktivering af en alarm efter bestemmelse af, at EEG-signaleme indikerer hypoglykæmi.
13. Apparat til anvendelse i en fremgangsmåde ifølge et hvilket som helst af de foregående krav og omfattende et forprogrammerede databehandlingsmidler omfattende input til modtagelse af EEG-signaler fra EEG-elektroder og midler til - inddeling af EEG-signaleme i en sekvens af tidssegmenter, - for hvert tidssegment bestemmelse af, om et mønster af EEG-signaler er til stede, der indikerer hypoglykæmi, og når et mønster af EEG-signaler, der indikerer hypoglykæmi, bestemmes at være til stede i et tidssegment, registrering heraf som en hændelse, - integrering af antallet af hændelser registreret i løbet af et udvalgt antal forudgående tidssegmenter, der sammen udgør en valgt tidsperiode, - bestemmelse af, at EEG-signaleme indikerer, at hypoglykæmi er til stede, når det integrerede antal hændelser overstiger et forhåndsbestemt tærskelantal, og - tilvejebringelse af et output, der indikerer, at tærskelantallet er overskredet.
14. Apparat ifølge krav 13, hvor outputtet antager form af en alarm.
15. Apparat ifølge krav 13 eller krav 14, hvilket apparat endvidere omfatter én eller flere elektroder til anbringelse på en bruger for modtagelse af EEG-signaler.
DK07729990.7T 2006-06-15 2007-06-07 Analyse af eeg-signaler for detektering af hypoglykæmi DK2040608T3 (da)

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GBGB0611872.3A GB0611872D0 (en) 2006-06-15 2006-06-15 Analysis of EEG signals to detect hypoglycaemia
PCT/EP2007/055628 WO2007144307A2 (en) 2006-06-15 2007-06-07 Analysis of eeg signals to detect hypoglycaemia

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US (1) US8298140B2 (da)
EP (1) EP2040608B1 (da)
JP (1) JP5269777B2 (da)
CN (1) CN101896115B (da)
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US8298140B2 (en) 2012-10-30
WO2007144307A2 (en) 2007-12-21
EP2040608B1 (en) 2015-03-25
CN101896115B (zh) 2020-09-01
CN101896115A (zh) 2010-11-24
WO2007144307A3 (en) 2010-06-17
JP2010500052A (ja) 2010-01-07
GB0611872D0 (en) 2006-07-26
EP2040608A2 (en) 2009-04-01
US20090287107A1 (en) 2009-11-19
JP5269777B2 (ja) 2013-08-21

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