Duun-Henriksen, Jonas

Cirka 50 millioner lider af epilepsi verden over. På trods af at 70% kan kontrollere deres anfald med antiepileptisk medicin, er det stadig en besværlig sygdom at leve med for en stor gruppe patienter. Dette PhDprojekt omhandler hvorledes disse personer kan hjælpes med kontinuerlig monitorering af deres hjerneaktivitet. Helt specifikt blev tre emner undersøgt: Muligheden for automatisk anfaldsforudsigelse, optimering af en automatisk anfaldsdetektionsalgoritme og sammenhængen mellem intra- og ekstrakranielt EEG. Angående automatisk epileptisk anfaldsforudsigelse har hverken denne forfatter eller nogen andre forskere været i stand til at opnå klinisk brugbare resultater. Dette skal dog ikke tages som nedslående for fremtiden. Nye meget store offentlige databaser eråbnet op i 2012. De vil forhåbentlig give forskerne mulighed for at identificere mønstre i patienternes EEG, som kan føre til pålidelige anfaldsforudsigelsesalgoritmer. Derimod blev der opnået mere lovende resultater i en undersøgelse af brugen af et ambulant EEG monitoreringsapparat til brug ved idiopatisk generaliseret epilepsi. Kombineret med en automatisk anfaldsdetektionsalgoritme vil sådan et apparat kunne give en objektiv opgørelse over frekvensen, varigheden og tidspunktet for anfaldsmønstrenes optræden. Baseret på standard-EEG data fra 20 patienter optaget i klinikken, kunne en log-sum af wavelet transformerede koefficienter bruges som input til en support vektor maskine. Dette resulterede i at 97% af anfaldsmønstre med varighed på mindst 2 sekunder blev korrekt detekteret uden nogen falsk positive detektioner. Dette blev opnået med en generisk algoritme på signalet fra kun en enkelt frontal EEG kanal. Ved at benytte samme algoritmestruktur på data fra to ambulante børn, som blev monitoreret i omtrent tre hele dage hver, blev sensitiviteten på 90% og den falske detektionsrate på 0,12/t. Når flere monitoreringer bliver udført, vil algoritmen kunne optimeres yderligere, så bedre resultater opnås. Den sidste undersøgelse gik på sammenhængen mellem spontan, vågen intra- og ekstrakranielt EEG. Syv patienter med elektroder placeret subduralt og subgalealt blev benyttet til at estimere synsfeltet for en enkelt ekstrakraniel kanal. Ved at beregne kohærensen mellem kanalerne, kunne den anerkendte hypotese om at kraniet fungerer som spatial midler eftervises. På trods af at kohærensen var signifikant over et kortikalt område på 150 cm2, var det kun kanaler inden for et område af cirka 30 cm2 som bidrog til højere kohærens. Forhøjelsen var lineær, hvor 50% af den maksimale kohærens blev opnået ved 2 cm2 (svarende til en kanal) og 75% blev opnået ved 16 cm2. Kohærensen for frekvensbånd under 16 Hz havde ens fald som funktion af den Euklidiske afstand mellem kanalerne. Frekvenser mellem 16 og 30 Hz havde et hurtigere fald og viste kun kohærente værdier for et kortikalt areal på 60 cm2. Der var ingen kohærens for frekvenser over 30 Hz uanset afstand. Mange patienter kæmper stadig med en daglig frygt for et pludseligt epileptisk anfald. Denne PhDafhandling identificerer steder hvor en kontinuer EEG monitor kan give patienter en forbedret livskvalitet, og et skridt mod sådan et apparat er opnået ved at udvikle, implementere og teste algoritmer til automatisk anfaldsdetektion.Approximately 50 million people worldwide suffer from epilepsy. Although 70% can control their seizures by anti-epileptic drugs, it is still a cumbersome disease to live with for a large group of patients. The current PhD dissertation investigates how these people can be helped by continous monitoring of their brain waves. More specifically, three issues were investigated: The feasibility of automatic seizure prediction, optimization of automatic seizure detection algorithms, and the link between intra- and extracranial EEG. Regarding feasibility of automatic seizure prediction, neither the author nor any other in the seizure prediction society have yet obtained clinical applicable prediction results. However, this should not be taken as discouraging for the future. New large public databases have emerged during 2012 which might provide the means to identify patterns leading to reliable seizure prediction algorithms. More promising results were obtained in the investigating of possible use of an outpatient EEG monitoring device for idiopathic generalized epilepsy patients. Combined with an automatic seizure detection algorithm such a device can give an objective account of the paroxysm frequency, duration, and time of occurrence. Based on standard EEG data from 20 patients recorded in the clinic, the log-sum of wavelet transform coefficients were used as feature input to a classifier consisting of a support vector machine. 97% of paroxysms lasting more than two seconds were correctly detected without any false positive detections. This was obtained using a generic algorithm on the signals from only a single frontal channel. Applying the same algorithm architecture on EEG data from two outpatient children monitored for approximately three entire days each, the sensitivity was 90% and the false detection rate was 0.12/h. When more recordings are collected, the outpatient algorithm can be further optimized and results should improve. The final investigation examined the relationship between spontaneous, awake intra- and extracranial EEG. Seven patients with electrodes placed subdurally as well as subgaleally were used to estimate the field of vision of a single extracranial channel. By computation of the coherence between the channels, the well recognized hypothesis stating that the skull acts as an electroencephalographic averager was proven correct. Although coherence was significant in an accumulation area of 150 cm2, only channels within a cortical area of approximately 30 cm2 showed to increase the coherence. The increase seemed to progress linearly with an accumulation area up to 31 cm2, where 50% of the maximal coherence was accumulated from only 2 cm2 (corresponding to one channel), and 75% from 16 cm2. The coherences of different frequency bands below 16 Hz all seem to have similar declines as a function of the Euclidean distance between channels. Frequencies between 16 and 30 Hz have a steeper decline and will only show coherent parts to cortical channels within 60 cm2. There is no coherence for frequencies above 30 Hz at any distance. A lot of patients with epilepsy still struggle with a dreadful fear of suddenly having a seizure. The current PhD study identified topics where an EEG monitor could provide improvement in the patient’s quality of life. By algorithm development, implementation and testing, a step toward such a device is presented.</p

Online Research Database In Technology

Detection and Prediction of Epileptic Seizures

http://orbit.dtu.dk/ws/files/77535562/Duun_Henriksen_2012_Detection_and_prediction_of_epileptic_seizures..PDF

Detection and Prediction of Epileptic Seizures

Abstract

Similar works

Full text

Available Versions

Online Research Database In Technology