Nörolojik krizlerden epilepsi nöbetinin kestirimi
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Dosyalar
Tarih
2020-07-23
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Batman Üniversitesi Fen Bilimleri Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/openAccess
Attribution-ShareAlike 3.0 United States
Attribution-ShareAlike 3.0 United States
Özet
Toplumda sara adıyla bilinen epilepsi hastalığı Dünya Sağlık Örgütü (WHO) tahminlerine göre
dünya nüfusunda %0,4-1 insanı etkileyen ciddi ve yaygın nörolojik bir hastalıktır. Anlık ve tekrarlayıcı
nöbetlerle karakterize olan epilepsi hastalığı çocukluk ve yetişkinlik çağında daha sık ortaya çıkmasıyla
beraber hemen her yaş grubunda insanı etkilemektedir. Genelde bilinç kaybı, hareket bozukluğu gibi sadece
nöbet ve nöbeti takip eden birkaç saatlik zaman dilimini etkileyen ancak ilaçlarla kontrol altına alınabilen
geçici durumlar oluşturmaktadır.
Epileptik nöbetlere benzer krizler geçiren epileptik olmayan (pseudo veya yalancı) nöbetlerin de
olması teşhisi güçleştirmektedir. Epilepsi hastalarının nöbetlerinin epileptik olup olmadığı (bunun için sık
ve güvenilir tanı yöntemi kriz anında video-EEG ölçümüdür) ve kullanılacak ilaçlarının dozu hasta
geçmişine bağlı olarak belirlenmektedir. Epileptik nöbet geçirdiği şüphesi ile uzman hekime müracaat eden
hastaların %10-20'sinin epilepsi hastası olmadığı belirlenmiştir. Hastanın epileptik ilaçlara verdiği tepki
temel alınarak bunun tespitinin, tedavinin başladığından ortalama 7,2 yıl sonra belirlenebilmektedir.
Bu tez çalışmasında çeşitli sensörler (EMG, EKG, İvmeölçer gibi) yardımıyla epileptik nöbetlerin
kestirimi için gelişen teknolojiyle entegre bir yaklaşım önerilmiştir. Çalışmanın ana amacı epileptik
nöbetlerin kestirimi için yapılan işlem maliyetini azaltacak bir test rutini oluşturmaktır. Dicle Üniversitesi
Tıp Fakültesi Nöroloji Bölümünde tedavi gören hastalardan epileptik bir nöbet sürecinde alınan veri seti
kullanılarak çeşitli yöntemler ile optimize edilmiş ve daha sonra elde edilen özelliklerle beraber Aşırı
Öğrenme Makineleri (ELM) ile sınıflandırılmıştır.
Epilepsy disease, known in the community as ‘sara’, is a serious and widespread neurological disease affecting 0.4-1% of the world population according to the World Health Organization (WHO) estimates. Epilepsy disease, characterized by instantaneous and repetitive seizures, affects people in almost every age group, although it occurs more frequently in childhood and adulthood. In general, loss of consciousness, movement disorder, such as only seizures and seizures that affect a period of several hours following the seizure, but can be controlled by drugs creates temporary conditions. It is also difficult to diagnose non-epileptic seizures (pseudo-or pseudo-seizures) that are similar to epileptic seizures. Whether the seizures of epileptic patients are epileptic (frequent and reliable diagnostic method is video-EEG measurement at the time of crisis) and the dose of the drugs to be used are determined based on the patient's history. It was determined that 10-20% of the patients who applied to the specialist physician with the suspicion of having epileptic seizure did not have. Based on the patient's response to epileptic drugs, its detection can be determined on average 7.2 years after the start of treatment. In this thesis, an integrated approach with the developing technology is proposed for the prediction of epileptic seizures with the help of various sensors (such as EMG, ECG, Accelerometer). The main purpose of the study is to create a test routine to reduce the cost of the procedure for the prediction of epileptic seizures. Using a data set taken from patients treated in the Dicle University, Faculty of Medicine, Department of Neurology, during an epileptic seizure process, it was optimized by various methods and classified by over-learning machines (ELM) with later characteristics.
Epilepsy disease, known in the community as ‘sara’, is a serious and widespread neurological disease affecting 0.4-1% of the world population according to the World Health Organization (WHO) estimates. Epilepsy disease, characterized by instantaneous and repetitive seizures, affects people in almost every age group, although it occurs more frequently in childhood and adulthood. In general, loss of consciousness, movement disorder, such as only seizures and seizures that affect a period of several hours following the seizure, but can be controlled by drugs creates temporary conditions. It is also difficult to diagnose non-epileptic seizures (pseudo-or pseudo-seizures) that are similar to epileptic seizures. Whether the seizures of epileptic patients are epileptic (frequent and reliable diagnostic method is video-EEG measurement at the time of crisis) and the dose of the drugs to be used are determined based on the patient's history. It was determined that 10-20% of the patients who applied to the specialist physician with the suspicion of having epileptic seizure did not have. Based on the patient's response to epileptic drugs, its detection can be determined on average 7.2 years after the start of treatment. In this thesis, an integrated approach with the developing technology is proposed for the prediction of epileptic seizures with the help of various sensors (such as EMG, ECG, Accelerometer). The main purpose of the study is to create a test routine to reduce the cost of the procedure for the prediction of epileptic seizures. Using a data set taken from patients treated in the Dicle University, Faculty of Medicine, Department of Neurology, during an epileptic seizure process, it was optimized by various methods and classified by over-learning machines (ELM) with later characteristics.
Açıklama
Anahtar Kelimeler
Epileptik Nöbetler, Epileptik Olmayan Nöbetler, Nöbet Kestirimi, Makine Öğrenmesi, Giyilebilir Sensörler, Epileptic Seizures, Non-Epileptic Seizures, Seizure Prediction Extreme Machine Learning, Machine Learning, Wearable Sensors
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Dal, S. (2020). Nörolojik krizlerden epilepsi nöbetinin kestirimi. (Yayınlanmamış Yüksek Lisans Tezi). Batman Üniversitesi Fen Bilimleri Enstitüsü, Batman.
