EEG, MRI ৬
৬ ઑഅ߽ ࢚ҙҙ҅ܳ ਊೠ
ױ दझమ োҳ
ࢿഅ*, ӣبো*, ӣ* *ചৈҮ ஹೊఠҕҗ e-mail: {szh1109, ririiiing, taurusx}@naver.com
Study on a Diagnosis System using
Correlation between Schizophrenia and EEG, MRI data
Ji-Hyeon Seong, Do-Yeon Kim, Ji-Eun KimDept. of Computer Science and Engineering, Ewha Womans University ਃ ডড
ઑഅ߽(न࠙ৌૐ) ࢎҊ, х, п, ೯ز ١ ੋѺ ৈ۞ ஏݶী Ѧ ҟߧਤೠ ࢚ ࢚ ૐ࢚ਸ ੌਵఃח न ജ. बпೠ न ജীب ࠛҳೞҊ ৈ җ ױ ҅о ыઉ ঋই ױ ݆ ࠗ࠙ਸ ജ ࣿী ઓೞҊ ਵݴ, ۽ ੋ೧ ઑഅ߽ۄח ױਸ ߉Ҋ ܐߑߨਸ חؘ ө য়ے दр Ѧܽ. ী ࠄ োҳ ח EEG, MRI ؘఠ৬ ઑഅ߽ ࢚ҙҙ҅ܳ ਊೠ ઑഅ߽ ױ दझమਸ ઁউೞҊ ೠ. ࠄ दझమ MRI ؘఠ৬ ݠन۞ ঌҊ્ܻਸ ాೠ ઑഅ߽ ഛܫ ױҗ ೣԋ, EEG ؘఠ दпച ӝמਸ ઁҕೞח ࣗਝযܳ ѐߊೣਵ۽ ॄ ઑഅ߽ ױ җ ӔѢܳ ࢎীѱ ઁҕೞৈ ഛೠ ߽ ױਸ ݾ۽ ೠ. ױ റীח ജ ؘఠ ҅ ҙܻܳ ా೧ ݠन۞ ঌҊ્ܻ ण ؘఠ ഛࠁ ߂ ജ ࢚కܳ ࣘਵ۽ ҙܻ∙ҙ ೡ ࣻ ب۾ ೞৈ ܐ ࣗ ਝয۽ࢲ ઑഅ߽ ҅ ױ ߂ ҙܻ दझమਸ ҳ୷ೠ. ͢͟ 昢嵦͑ അ ࣁ҅ ੋҳ ড 0.7%о ઑഅ߽(न࠙ৌૐ)ਸ এҊ ਵݴ, Ӓ ࣻח ݒ֙ Բળ ૐоೞҊ . ઑഅ߽ ৮о ൨ٜ ݅ ઑӝ ܐܳ ೡ ҃ Ӓ ૐࣁо ഐؼ ࣻ য, ࡅܰҊ ഛೠ ױҗ Ӓ റ ࣘ ҃җ ҙਸ ా೧ ഐػ ࢚కܳ ਬ ೞח Ѫ ਃೞ. ೞ݅ ਭ ജҗ ׳ܻ न ജ ౠࢿ ࢚ ઑഅ߽ ৈ ҅ ױ दझమ হয, ױ द ജ ࣿҗ ೯ز ౠࢿ ҙী ࠗ࠙ਸ ઓೞҊ . ٸ ޙী য় ഛܫ ݒ ֫ਵݴ ജח ࣻ֙ী Ѣ नীѱ ݏ ח ܐߨਸ ѱ ػ. ౠ ઑഅ߽ ߊ߽ җ ਗੋਸ ޅ೧ ߊࢤೠ ࢎഥ ಞѼ ജٜ न ߽ਸ ऀӝҊ ܐܳ ӝೖೣਵ۽ॄ ߽ਸ ؊ ঈചदெ ߧદ۽ যӝب ೞח ١ ࢎഥ ޙઁܳ ঠӝೠ. ୭Ӕ 4 ର সഄݺ ࠻ؘఠ ߂ ੋҕמ(AI) ӝࣿਸ ӝ߈ ਵ۽ ܐ স ߸ചೞҊ . न҅ীࢲب न ജҗ ৈ۞ ܐ ؘఠ োҙࢿਸ ߋח োҳо ഝߊ ೯غҊ . ౠ ݠन۞ ঌҊ્ܻҗ MRI, EEG(ֱ) ؘఠܳ ਊೞ ৈ ઑഅ߽ ജ৬ Ѥъੋਸ ֫ ഛب۽ ࠙ܨೡ ࣻ ਸ ੑ ૐೠ োҳ ١ਵ۽ ઑഅ߽ী ೠ җ ױ оמࢿ ઁ दغ. MRI ܳ ਊೠ न҃ ࢚ োҳח ઑഅ߽ ߊ߽ റ ֱ ҳઑ∙ӝמ ߸ച োҙࢿਸ ߊѼೞҊ, EEG ח ܻࣁஶ٘ (ms) рѺ ஏਵ۽ ֱ ࣁೠ ߸ചܳ хೞৈ ੋр ੋध җী ೠ न҃ ࠙ࢳ بҳܳ ઁҕೞ.[1, 2] Ӓ۞ա ৬ э োҳٜ ઑഅ߽ ױ ߂ ܐܳ ਤ೧ ഝਊغ ޅೞҊ ױࣽ োҳী݅ Ӓח ೠ҅ . ী ࠄ োҳח ઑഅ߽җ ਬೠ ࢚ҙ ҙ҅о ח MRI, EEG ؘఠܳ ഝਊೞৈ ઑഅ߽ਸ زਵ۽ ױೞח दझమਸ ઁউೞҊ ೠ. ࠄ दझమ ࣗਝয۽ MRI ؘఠ৬ о दউ ۽ࣁझܳ ਊೠ ઑഅ߽ ഛܫ ױҗ ೣԋ, EEG ؘ ఠ दпച ӝמਸ ాೠ ળ Ѥъੋҗ ജ ࠺Ү ֱ Ӓې ܳ ઁҕೣਵ۽ॄ ജ ֱ ࢚కܳ ҙೡ ࣻ ب۾ ೠ. नҗ ޙীѱ ઑഅ߽ ױਸ ਤೠ ৈ۞ ёҙ ܳ ઁ ҕೣਵ۽ॄ য় оמࢿਸ Ҋ, ജח ഛೠ ױਵ۽ ࡅ ܲ दੌ ղী ೠ ܐܳ ߉ਸ ࣻ . ͣ͟ 決嵦洇͑ 愶凃͑ 2.1 MRI ৬ ઑഅ߽ ࢚ҙҙ҅ ୭Ӕ ݻ ֙р ۿन҃җ(҅न҃җ, computational neuroscience)ী ೠ җ҅ ҙब ࣘਵ۽ ૐо೮. ౠ ী ഝਊغח ࣻ ۿ(computational method) ܻ
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-2020 온라인 춘계학술발표대회 논문집 제27권 제1호 (-2020. 5)റ MRI(ӝҕݺ࢚) ؘఠ ࠙ࢳী ݆ ਊغҊ . MRI ؘఠܳ ࠙ࢳೞח Ѫ ੋр ֱ ౠਸ ౠೞҊ ࢸݺೞ ӝ ਤೠ ഄनੋ ࢤޛࠁ ߑߨ. ࣻ֙р न࢚҃ (neuroimaging) োҳח न ജ গ৬ ֱ ҳઑ∙ӝמ ߸ ച ઓ ࢎী ࢸٙ۱ ח োҙࢿਸ ഛ݀ೞৈ ઑഅ߽ ױ ࢜۽ উਸ ઁदೞ.[3] (Ӓܿ 1) MRI झபীࢲ ࢤغח ࣻ ؘఠ ౠ, fmri ৬ smri ܳ ਊೠ ജ ֱ ࢚క ࠙ࢳਸ ా೧ ઑഅ ߽ ֱী ח ೱী ೠ োҳٜ ೯ؽী ٮۄ, न҃ ࢚োҳ৬ MRI ؘఠ ܻ ӝࣿਸ ӝ߈ਵ۽ Ӓܿ 1 җ э ؘ ఠ ܻо оמ೧ .[4] ߸ജػ MRI ࣻ ؘఠܳ ਊೠ ݠन۞ ঌҊ્ܻ ઑഅ߽ ജ৬ Ѥъੋ ࠙ܨী ೠ োҳ ח ࢚ Ҋޖ. ח MRI ؘఠо ઑഅ߽җ ਬೠ ࢚ҙҙ҅о ח Ѫਸ ੑૐೣਵ۽ॄ ઑഅ߽ ഛܫ ױਸ оמೞب۾ ೠ. 2.2 EEG ৬ ઑഅ߽ ࢚ҙҙ҅ ܻࣁஶ٘(ms) ࣻળ ೧࢚بੋ EEG ؘఠח ੋী ೠ न҃ ਸ ࠙ࢳೞח хೠ بҳ. नࢤܻ োҳী ࢎ ਊغח EEG ഝز ਃ ୋبח ERP(ࢎѤҙ۲ਤ)۽, ౠ द рী ߊࢤೞח ߮(ӓ)ী ೠ ߸ച ળ ಁఢਵ۽ غݴ ಣӐ ഋীࢲ ஏػ ೖ ী ೧ ച ػ. (Ӓܿ 2) ERP ؘఠ ઙܨ ઑഅ߽ ജח ERP ؘఠ N100, P200 ١ ౠ द҅ৌী ࢲ Ѥъੋী ࠺೧ রઁػ ഋ Ӓې, ೖ ч Ѥъੋী ࠺೧ ڄযח ֱ ഋ աఋդ. ژೠ ജח ߮ী Ѥ ъੋࠁ וܻѱ ߈ೞח ౠਸ оઉ, Ӓܿ 2 द҅ৌীࢲ Ӓ ې ഋਵ۽ EEG ؘఠܳ दпചೞݶ ର ߂ ߮ ߈ ࣘبী ೠ Ѥъੋҗ ઑഅ߽ ജ ରܳ ഛੋೡ ࣻ . ח ઑഅ߽ ജ ֱ ࢚కী ೧ MRI ৬ח ܲ ܳ ઁҕೠ.[5] 2.3 ؘఠ Ѩૐ োҳীࢲ ࢎਊػ ؘఠח Kaggle ীࢲ оઉ৳ਵݴ, दझమী ഝਊೞӝ ਤ೧ ݠन۞ ഛبܳ Ѩૐ೧ ࠁও. ݢ Ѩૐী ࢎਊೠ MRI ؘఠח Ӓܿ 1 җ э fmri ীࢲ ࢤػ FNC ৬ smri ীࢲ ࢤػ SBM ࣻ ؘఠ(SZ-40, HC-47)۽, ݠन۞ ݽ؛ੋ оदউ ۽ࣁझী ण द ઑഅ߽ ജ৬ Ѥъੋਸ ಣ Ӑ 92% ഛب۽ ࠙ܨೡ ࣻ .[6] ERP ؘఠ(SZ-49, HC-32)ח EEG 64 ѐ ӓ օҗ Ӓ ৻ ҷ ࠗਤ 6 ѐ ӓ օ۽ ஏػ ؘఠ۽ Ӓܿ 2 ৬ э द҅ৌ ೖ чਸ ୶ೞח ؘఠ оҕ җਸ Ѣଢ଼ .[7] ೞ݅ Ѩૐ Ѿҗ, ݠन۞ ݽ؛ ߂ ಌࣆۿ(बக न҃ ݎ) ण द, ള۲ ഛبח ಣӐ 97%۽ ࣻೞ݅ Ѩૐ ഛ بח ಣӐ 59%۽ ծও. EEG ؘఠ ౠࢿ ࢚ ֢ૉ۽ ੋ೧ ݠन۞ ݽ؛ ण ীח ഛبо ૐоೞ݅ Ѩૐ ഛبח ڄযח җ ֫ ഛܫ۽ ߊࢤ೮ਵݴ, ֱ ஏ ജ҃ա ઑѤ(߮)ী ೱਸ ݆ ߉ӝ ٸޙী ؘఠী ٮܲ ݠन۞ ഛب ରо ѱ աఋլ. ী ݠन۞ਸ ഝਊೠ ױ ؘఠ۽ࢲח ࠗೞח Ѿۿী ب׳ೞ. ೞ݅ ؘఠܳ ഋ Ӓې۽ աఋչਸ ҃, ߮ী ٮܲ ജ ҳ ੋ ध җਸ ഛੋೡ ࣻ ӝ ٸޙী MRI ഛܫ ױҗח ܲ ܳ ઁҕೞחؘ ࠙ݺೠ о . ͤ ͤ͟ 柢枪癢͑ 儢殚͑ 3.1 ઁউ दझమ ҳઑ ୭Ӕ ݠन۞ ӝ߈ ܐ ױ ࠁઑ ࣗਝযо ݆ ѐߊ غҊ ח ୶ࣁա, नജী যࢲח ࠺ೠ प. ী MRI, EEG ৬ ݠन۞ ঌҊ્ܻਸ ਊೠ ઑഅ߽ ױ दझమਸ ઁউೞҊ ೠ.[8] ജ ࣿী ઓೞৈ ױਸ ೞח ӝઓ ߑधҗח ׳ܻ, ജ ֱܳ Ѩࢎೠ ёҙੋ MRI ؘఠܳ ਊ೧ ઑഅ߽ ױਸ ೞب۾ ೠ. ژೠ, ࠙ࢳೞӝ য۰ EEG ࣻ ؘఠ दпചܳ ా೧ ജ ੋध җী ٮܲ ֱ ࢚ కܳ ҙೞҊ, ױ Ѿҗ ؘఠ ୷ਸ ా೧ ߽ ҃җܳ ୶ ೠ. ܳ ా೧ ઑഅ߽ ױ ߂ ജ ҙܻ दझమ ҅ܳ ҳ୷ೞৈ ࢎ৬ ജ ݽفীѱ ಞܻࢿਸ ઁҕೠ. (Ӓܿ 3) दझమ ҳઑب
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-2020 온라인 춘계학술발표대회 논문집 제27권 제1호 (-2020. 5)Ӓܿ 3 दझమ ҳઑܳ աఋմ Ѫਵ۽ җ э ஹನք ٜ۽ ҳࢿػ.[9] - ജ ױ ݠन۞ ঌҊ્ܻ: MRI ؘఠ۽ ࢎ णػ оदউ ۽ࣁझ ঌҊ્ܻਵ۽ ߽ী ೠ ഛܫ ױਸ ղܻח ӝמਸ ࣻ೯ೠ. - ؘఠ दпച: EEG ؘఠܳ ౠ द҅ৌ(N100, P200)ী ࢲ ֱ ഋਵ۽ दпചೞৈ ઁҕೞݴ, ജ җѢ ױ Ѿҗܳ ԃࢶ Ӓې۽ աఋղয ؘఠ ࠙ࢳਸ ਊೞب ۾ ೠ. - ۄ٘ ؘఠ߬झ: ߽ਗ ؘఠ߬झ৬ ݠन۞ ؘ ఠ߬झ۽ աয ؘఠܳ ҙܻೠ. 3.2 ઑഅ߽ ױ (Ӓܿ 4) ઑഅ߽ ױ ۽ Ӓܿ 4 ח ജ ઑഅ߽ ৈࠗܳ ױೞӝਤೠ ۽. ݠन ۞ ݽ؛ GPtoolbox оदউ ۽ࣁझ(GP) ࠙ܨӝܳ ࢎ ਊೞਵݴ, ҙஏח ߬ܰ־(0,1) ࠙ನ۽ بػ. ৈӝࢲ о दউ ۽ࣁझۆ, ےؒ ߸ࣻ ਵ۽ пп оदউ ࠙ನ( ӏ ࠙ನ)ܳ оݴ ઁೠ ୭ച ߂ ܻী ਬਊೞ. ஏ ഛܫ ױਤ рѺਵ۽ ߸ജೞח दӒݽ٘ ೣࣻ(0,1)ܳ ӝ߈ ਵ۽ ೠ. ݽ؛ ജܳ ױೞӝ ী ؘఠী ೠ ण ৮ܐغয যঠ ೠ. ജ Ѩࢎ Ѿҗח ࣻ۽ࢲ ܻ غয दझమ ী ࢎਊغݴ, ױ Ѿҗח ߔ࠙ਯ(%)۽ ઁҕػ. 3.3 ۄ٘ ؘఠ߬झ (Ӓܿ 5) ઝ. ߽ਗ ؘఠ߬झ . ݠन۞ ؘఠ߬झ झః݃ ୭Ӕ ߽ਗ ਯ ؘఠ߬झ ҙܻ৬ ର, ࠁউ ъച ١ ࢲ࠺झо ઁҕغח ۄ٘ ӝࣿী ݾೞҊ . ౠ ജ ؘఠ ߂ ࠙ࢳ, ױҗ ܐ ࠁ Үܨ ١ীࢲ ۄ٘ ࣻਃо ֫ইҊ ਵݴ, पઁ ۄ٘ दझమਸ بੑ ೞח ߽ਗب טযաҊ ח ୶ࣁ.[10] ࠄ दझమ ۠ ߸ച ܳ ߈೧ ۄ٘ ؘఠ߬झܳ ҳ୷ೞৈ दझమҗ োѾਸ ా೧ ؘఠܳ ҙܻೠ. Ӓܿ 5 ৬ э ߽ਗ ؘఠ߬झח ࢎ, ജ ࠁ ҙܻ ߂ ജ MRI, EEG ؘఠܳ ೞҊ ݠ न۞ ؘఠ߬झח য় MRI ؘఠ৬ 0(Ѥъੋ), 1(ജ)۽ ҳ࠙ػ ജ Ѿҗ݅ ೞৈ ݠन۞ ݽ؛ णী݅ ࢎਊػ . ߽ਗ ؘఠ߬झ৬ ݠन۞ ण ਊ ؘఠ߬झ ࠙ ܻܳ ా೧ ജ ѐੋ ࠁܳ উೞѱ ࠁഐೣҗ زदী ݠन۞ ਸ ਤೠ ؘఠח ҕਬ оמೞب۾ ೞৈ, ੋҕמ ӝࣿਸ ਤ ೠ ܐ ؘఠ߬झ۽ࢲ ӝמೠ. ͥ ͥ͟ 柢枪癢͑ 割笊͑ 冶刂͑ 4.1 दझమ ࢎਊ ੋఠಕझ ࠄ दझమ पઁ ࣗਝয ҳഅ Ѿҗח Ӓܿ 6, 7 җ э. ࢎਊח नѤъҗ ޙ۽ оೠ. ࢎਊח ۽Ӓ۔ प೯ റ ࠄੋ ID ৬ PW(password)۽ दझమী ۽Ӓੋ ೡ ࣻ . ޙ ҅ ࠁ, ജ ѐੋ ࠁ ߂ Ѩࢎ ؘఠח ۄ٘ ؘఠ߬झ ۖಬੋ AWS RDS ীࢲ ∙ҙܻػ. ۽ Ӓੋ റ ࢎח Ӓܿ 6 ജ ١۾ ߂ ؘఠ ١۾ ചݶ ৻ ജ ױ, ࢎ ҅ ҙܻ ݫী Ӕೡ ࣻ . (Ӓܿ 6) ઝ. ജ ١۾ . Ѩࢎ ؘఠ ١۾ <ജ ١۾> ݫ ࢶఖ द, ജ ӝઓ ١۾ ৈࠗী ٮۄ ܲ ରܳ ٮܲ. ࢜۽ ജ ١۾ द ജ ܴ, ࢤ֙ਘੌ, ࢿ߹, ઑഅ߽ ਬޖ, ചߣഐ ١ ѐੋ ࠁܳ ੑ۱ റ ജ ١ ۾ ߣഐ(subject_id)ܳ ࠗৈೠ. ח അ ۽Ӓੋ ੋ ࢎ ۽ ز ߓػ. ӝઓ ജ ١۾ दীח <ജ ઑഥ> ۆী ജ ١۾ ߣഐܳ Ѩ࢝ೞৈ ೧ ജܳ ח. ജ ࢸ റ, ܻо ৮ܐػ EEG, MRI-FNC, MRI-SBM ؘఠܳ স۽٘ ೠ . ജ ࠁ৬ EEG ؘఠח ߽ਗ DB ী স۽٘غݴ, MRI ؘ ఠח ജ Ѩࢎ ࠁ۽ࢲ ߽ਗ DB ী, ݠन۞ ݽ؛ ण ؘ
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ఠ۽ࢲ ױ റ ݠन۞ DB ী пп স۽٘ ػ. ٸ, ࢎ о ઑഅ߽ ജ۽ ױ ೡ ҃ label 1 ਸ ࠗৈ ߉ਵݴ, ݠन۞ ؘఠ߬झী द ױ ഛܫ ইצ য় 0, 1 ۽݅ ػ . ݠन۞ ण ࢜ ؘఠо ੑ۱غਸ ٸ ױ റ ण ೞب۾ ࢸغয . (Ӓܿ 7) ജ ױ ചݶ <ജ ױ> ݫ ࢶఖ द, ೧ ജ ؘఠ ࠙ࢳ Ѿҗܳ ਊ೧ ࢎо ઙਵ۽ ౸ױೠ. ױ Ѿҗ ചݶ ઝஏীח MRI-FNC, SBM ױݶ ৬ ݠन۞ਸ ਊೠ ഛܫ ױ Ѿҗо ߔ࠙ਯ(%)۽ ۱غݴ, ചݶ ஏীח EEG ؘ ఠ ഋ Ӓېܳ ࠅ ࣻ . ݢ, ஏ ইې ਤ ߹ ӓ ਸ ࢶఖೞݶ, N100-P200 ҳр ೧ ജ ֱ Ӓې(ۆ࢝ ࢶ)৬ Ѥъੋ ಣӐ ֱ Ӓې(ࡈр࢝ ࢶ)ܳ ೣԋ ࠺Ү∙ഛੋ ೡ ࣻ . Ӓ ࢚ױীח 1. ߡౡਸ ־ܲ റ, ࢤࢿغח హਸ ٛח ߮ী ೠ ֱо ࠜ࢝ਵ۽ दغҊ 2. ࣘਵ۽ ੌ ೠ హਸ ٛח ߮ ֱо ۆ࢝ਵ۽ दغয ߮ী ٮ ܲ ജ ࢚కܳ ঌ ࣻ . ݄݃ਵ۽ ‘դ Ѿҗ ࠁӝ’ܳ ా೧ ജ җѢ ױ ӝ۾ਸ ԃࢶ Ӓې۽ ۱ೞৈ दр թ ী ٮܲ ҃җ ҙ оמೞ. 4.2 ઑഅ߽ ױ दաܻয় ࠄ दաܻয়ীࢲ ࢎ ҅ ࠁ ؘఠח नѤъҗ ޙܳ оೞҊ ۽ ࢤࢿೠ ؘఠݴ, ઑഅ߽ ജ EEG, MRI ؘఠח ण ؘఠ৬ э Kaggle ؘఠ ܳ ࢎਊೞਵա, ण ؘఠ৬ח ୍ ܻ࠙ೞৈ ೯ೞ. ജ ױ Ѿҗח Ӓܿ 7 җ э. ݢ, 92% ഛܫ۽ ઑഅ߽ ജ৬ Ѥъੋਸ ҳ࠙ೞח ݠन ۞ ݽ؛ਸ ࢎਊೞৈ ഛܫ ࣻ Ѿҗܳ بೠ. ױ Ѿҗח 90%۽ ઑഅ߽ ജۄח ਫ਼ ױਸ ղܾ ࣻ . ژೠ ஏ ֱ Ӓېীࢲ, ۆ ഋ ജо ࡈр ഋ Ѥъੋҗ ࠺Үೞৈ N100 दীࢲ ؊ וܻҊ রઁػ ೖܳ աఋղח Ѫ ഛো ٘۞աݴ, ח ઑഅ߽ ജ ֱ ౠࢿҗ ੌೠ. ࢎח ف о җ ӔѢ৬ ӝઓ ജ ࣿ ߂ ೯زҙ ࣗѼਸ ઙೞৈ ೧ ജо ઑഅ߽ۄח Ѿۿী ب׳ೡ ࣻ . ח ӝઓ ױ ߑߨࠁ ؊ ೱ࢚ػ ഛܫ۽ ઑഅ߽ ױਸ оמೞѱ ೠ. 4.3 ਊ Ѿҗ ࢿ ױ݅ਸ ਊ೮؍ ӝઓ ױ ߑߨҗ ׳ܻ, ױ Ѿҗ о ࣻ৬ Ӓې۽ അؽਵ۽ॄ ױۄח ёҙࢿਸ ദٙೡ ࣻ . ژೠ Ӓې ߂ दпചܳ ా೧ ޙա ࣻ۽݅ ؘఠܳ അ೮ਸ ٸࠁ ҙੋ ౸ױ оמೞ. ೞ݅ ֱ ؘఠ ܳ ҙ҅ഋ ؘఠ߬झ ప࠶ (table) ഋక۽ റ ۽Ӓ۔ীࢲ ࠛ۞ৢ द, द ؘఠ ۨ(data frame)ച ೞӝ ٸޙী ֱ Ӓې ۱ী ೠ दр ࣗ ӡѱ(ড 1 ࠙) ஏغח ೠ҅о . ژೠ, ࢚प ਸ ా೧ ࠁ ݆ ജ ؘఠܳ ഛࠁܳ ాೠ ݠन۞ ݽ؛ न܉ࢿ ೱ࢚ ਃೞ. ͦ ͦ͟ 冶嵦͑ ࠄ ֤ޙীࢲח MRI ؘఠ ݠन۞ ݽ؛ ण Ѿҗܳ ߄ఔ ਵ۽ ઑഅ߽ ৈࠗܳ ഛܫਵ۽ ױೞҊ, ജ ߮ী ٮܲ ֱ ߸ച ন࢚ਸ ഝਊೞৈ ೠ ઑӝ ܐо ਃೠ ઑഅ߽ ജܳ ױೞח दझమਸ ઁউೠ. ਭ ߽җ э ёҙ Ѩࢎܳ ాೠ न ജ ױۄח ܳ о. ೱറ ܐ ޙо ߽ਗҗ ഈসਸ ా೧ ޙ ܐ धҗ ࠙ೠ ؘఠܳ ష۽ োҳܳ ೯ೠݶ ࠁ न܉ب ח दझమਸ ઁҕೡ ࣻ ਸ Ѫۄ ӝػ. 焾処怾竒͑
[1] Elisa Veronese, Umberto Castellani, Denis Peruzzo, Marcella Bellani, and Paolo Brambilla, Machine Learning Approaches: From Theory to Application in Schizophrenia, Mathematical Methods and Applications in Medical Imaging, Article ID 867924, p12, 2013
[2] Jason K. Johannesen, Jinbo Bi, Ruhua Jiang, Joshua G. Kenney & Chi-Ming A. Chen, Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults, Neuropsychiatric Electrophysiology, Article number: 3, 2016.2.11
[3] Sarina J. Iwabuchi1, Peter F. Liddle1 and Lena Palaniyappan, Clinical utility of machine-learning approaches in schizophrenia: improving diagnostic confidence for translational neuroimaging, Front. Psychiatry, 29 August 2013
[4] MLSP 2014 Schizophrenia Classification Challenge, https://www.kaggle.com/c/mlsp-2014-mri
[5] Judith M. Ford, Vanessa A. Palzes, Brian J. Roach, and Daniel H. Mathalon, Did I Do That? Abnormal Predictive Processes in Schizophrenia When Button Pressing to Deliver a Tone, Schizophr Bull. 2014 Jul; 40(4): 804–812. 2013 Jul 10
[6] asolin, MLSP2014-kaggle-challenge,
https://github.com/asolin/MLSP2014-kaggle-challenge
[7] EEG data from basic sensory task in Schizophrenia, https://www.kaggle.com/broach/button-tone-sz [8] അਔ ৻, ङղद҃ ਤ୶ਸ ਤೠ CNN ӝ߈ ਤҙ ے٘݃ ࠙ܨӝ ࢸ҅, ೠҴࠁܻഥ, ઁҮ ইۄಌझ, 2019 [9] ࣗ ৻, ࠻ ؘఠ ӝ߈ धणҙ ࠙ࢳ ߂ ҙ۲ ࢚ಿ ୶ୌ ৡۄੋ ށ API, ೠҴࠁܻഥ, ઁҮ ইۄಌझ, 2019 [10] Business Watch, ֎ߡ, 'ܐ·߽ਗ' ۄ٘ী ԡ൦ ਬ http://news.bizwatch.co.kr/article/mobile/2018/05/31/0022
