Brain abnormality detection using statistical analysis of individual structural connectivity networks and EEG signals
Date
2023-11-27
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Abstract
Tipptasemel meditsiiniteaduse ja tehisintellekti uuringud on valmis ümber kujundama ajuhaiguste diagnoosimist. Käesolev doktoritöö “Statistilisel analüüsil põhinev aju ebanormaalsuste tuvastamine kasutades individuaalsete struktuuriliste ühenduvuste võrke ja EEG signaale” keskendub Alzheimeri tõvele ja kliinilisele depressioonile, kasutades tipptasemel tehnoloogiaid uuenduste esile toomiseks.
Sünni, trauma, haiguse või muude asjaolude tagajärjel tekkinud ajuanomaaliad mõjutavad oluliselt inimese füüsilist ja vaimset tervist. Selles doktoritöös käsitletakse kaht peamist teemat: MRT kaudu diagnoositud Alzheimeri tõve ning EEG kaudu tuvastatud kliinilist depressiooni.
Masinõppe algoritmid tõlgendavad ajuskanneeringu pilte, et tuvastada haigustele omaseid mustreid, nagu näiteks ajustruktuuri muudatusi Alzheimeri tõve puhul. Erinevatel pildimustritel põhinev andmete analüüs võimaldab haiguse olemasolu diagnoosida kiiremini ning täpsemalt.
Kliinilise depressiooni puhul analüüsib masinõpe EEG salvestusi, et tuvastada ajutegevusega seotud muudatusi ning ennustada depressiooni esinemist. EEG kaudu on võimalik mõõta depressiooniga seotud ajutegevust ning masinõppe abil tuvastada haiguspilt. EEG mustrite analüüs võimaldab edukat patsientide klassifitseerimist ning seeläbi kiiremat diagnoosimist.
Käesolev lõputöö toob esile inimese leidlikkust ning tehisintellekti potentsiaali tervishoiu paremaks muutmiseks. See viitab uuele ajastule ajuhäirete diagnoosimisel, kus on võimalik senisest kiiremini ning täpsemini Alzheimeri tõve ja kliinilist depressiooni tuvastada. Tulevikus on potentsiaalselt näha mitmeid paremaid tervishoiu lahendusi, mida taolised tehnoloogiad edendavad.
Brain illness diagnosis procedures are on the verge of transformation through medical science and artificial intelligence research. This thesis, called “Brain abnormality detection using statistical analysis of individual structural connectivity networks and EEG signals,” is about Alzheimer’s disease and clinical depression. It uses cutting-edge technologies that show the future possibility of such a transformation. Brain anomalies, whether present at birth or resulting from trauma, disease, or other circumstances, significantly impact physical and mental health, and detecting such anomalies can aid in medical diagnostics. This thesis focuses on two major topics: diagnosing Alzheimer's disease using MRI and detecting clinical depression using EEG. Machine learning algorithms interpret brain scans to discover disease-specific patterns, such as alterations in brain structure, in people with Alzheimer’s disease. With data analysed using different image pattern modalities, the possibility for faster, more accurate diagnosis exists. In the case of clinical depression, machine learning analyses EEG recordings to detect trends and predict depression. Machine learning interprets EEG recordings to classify the medical condition by analysing depression-related brain activity. Various patterns in EEG recordings enable disease classification, allowing for enhanced diagnosis. This thesis emphasises human ingenuity and the potential of AI to change healthcare. It points to a new era in brain illness diagnosis, with earlier and more accurate Alzheimer's and clinical depression diagnoses. As these technologies advance, the future holds the potential for better healthcare
Brain illness diagnosis procedures are on the verge of transformation through medical science and artificial intelligence research. This thesis, called “Brain abnormality detection using statistical analysis of individual structural connectivity networks and EEG signals,” is about Alzheimer’s disease and clinical depression. It uses cutting-edge technologies that show the future possibility of such a transformation. Brain anomalies, whether present at birth or resulting from trauma, disease, or other circumstances, significantly impact physical and mental health, and detecting such anomalies can aid in medical diagnostics. This thesis focuses on two major topics: diagnosing Alzheimer's disease using MRI and detecting clinical depression using EEG. Machine learning algorithms interpret brain scans to discover disease-specific patterns, such as alterations in brain structure, in people with Alzheimer’s disease. With data analysed using different image pattern modalities, the possibility for faster, more accurate diagnosis exists. In the case of clinical depression, machine learning analyses EEG recordings to detect trends and predict depression. Machine learning interprets EEG recordings to classify the medical condition by analysing depression-related brain activity. Various patterns in EEG recordings enable disease classification, allowing for enhanced diagnosis. This thesis emphasises human ingenuity and the potential of AI to change healthcare. It points to a new era in brain illness diagnosis, with earlier and more accurate Alzheimer's and clinical depression diagnoses. As these technologies advance, the future holds the potential for better healthcare
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Keywords
neurologic diseases, classification, electroencephalography, automatic learning, deep learning, magnetic resonance imaging