2018英特爾ISEF獲獎作品摘要 計算生物與生物信息學 Intel Finalist Abstract Computational Biology and Bioinformatics

獲獎作品基本信息

年份	2018
學科	計算生物與生物信息學 Computational Biology and Bioinformatics
國家/州	United States of America

獲獎作品名稱

Optimization of Seizure Detection Using the Machine Learning Algorithm SVM

獲獎作品摘要

Epilepsy is a very common and devastating neurological disorder that affects 65 million people globally.? Electroencephalography (EEG) recording is an essential tool in evaluating seizure activity, critical for epilepsy drug development and patient care. However, due to the random and low frequency of seizures, seizure evaluation requires continuous, long-term EEG monitoring for weeks and months, producing huge volumes of data. This creates a formidable challenge for real-time tracking of seizures using wearable devices which have low computational power. Current algorithms for automating EEG seizure classification use computationally expensive methods to analyze minute features within small fragments of seizure events. However, despite this complexity, current algorithms still underperform, and laboratory technicians and clinical physicians alike still do not fully rely on these algorithms, opting to manually sift through thousands of hours of EEG data. Human visual analysis still drastically outperforms computer analysis. The proposed method in this study attempts to mimic the simplistic analysis of human vision for EEG seizure classification by focusing on broad, global trends in condensed EEG seizure data. EEG seizure clips were normalized and processed through a rolling mean function, producing smoothed EEG clips that represent the global shape of each clip. These signals were then directly inputted for machine training. This method achieved an accuracy rate of approximately 98.51%. Our approach provides an unique advantage in patient epilepsy management using wearables, where accuracy, computational cost, and speed are all critical to improving patient quality of life.

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高中生科研 英特爾 Intel ISEF
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ISEF 簡介

英特爾國際科學與工程大獎賽，簡稱 "ISEF"，由美國 Society for Science and the Public（科學和公共服務協會）主辦，英特爾公司冠名贊助，是全球規模最大、等級最高的中學生的科研科創賽事。ISEF 的學術活動學科包括了所有數學、自然科學、工程的全部領域和部分社會科學。ISEF 素有全球青少年科學學術活動的“世界杯”之美譽，旨在鼓勵學生團隊協作，開拓創新，長期專一深入地研究自己感興趣的課題。

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英特爾 ISEF 學術活動詳細介紹

英特爾 ISEF 全程指導方案

· 數學 · 物理 · 化學 · 生物 · 計算機 · 工程 ·

學科簡介：計算生物與生物信息學 Computational Biology and Bioinformatics

Studies that primarily focus on the discipline and techniques of computer science and mathematics as they relate to biological systems. This includes the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavior, and social systems.

Subcategories:

Computational Biomodeling?(MOD):?Studies that involve computer simulations of biological systems most commonly with a goal of understanding how cells or organism develop, work collectively and survive.

Computational Epidemiology (EPD):?The study of disease frequency and distribution, and risk factors and socioeconomic determinants of health within populations. Such studies may include gathering information to confirm existence of disease outbreaks, developing case definitions and analyzing epidemic data, establishing disease surveillance, and implementing methods of disease prevention and control.

Computational Evolutionary Biology?(EVO):?A study that applies the discipline and techniques of computer science and mathematics to explore the processes of change in populations of organisms, especially taxonomy, paleontology, ethology, population genetics and ecology.

Computational Neuroscience?(NEU):?A study that applies the discipline and techniques of computer science and mathematics to understand brain function in terms of the information processing properties of the structures that make up the nervous system.

Computational Pharmacology?(PHA):?A study that applies the discipline and techniques of computer science and mathematics to predict and analyze the responses to drugs.

Genomics?(GEN):?The study of the function and structure of genomes using recombinant DNA, sequencing, and bioinformatics.

Other?(OTH):?Studies that cannot be assigned to one of the above subcategories. If the project involves multiple subcategories, the principal subcategory should be chosen instead of Other.