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Classification of Time Series Sequential Data

Title
Classification of Time Series Sequential Data
Author
Liu Ming
Advisor(s)
김영훈
Issue Date
2018-08
Publisher
한양대학교
Degree
Master
Abstract
Time series sequential data is a collection of observations of well-defined data items obtained through repeated measurements over time. And classification of time series data is the process of assigning an input time series to one of the several known classes or categories. The existing traditional method is limited by unclear features, high dimension data and low accuracy. In this thesis, we propose a method to classify heart disease based on electrocardiogram(ECG) signals. Since early detection of heart disease can enable us to ease the treatment as well as save people’s lives, accurate detection of heart disease using ECG is very important. Most of the traditional work including those mentioned above utilizing feature extraction designed for ECG signal mainly resorting to heuristic heartbeat segmentation. Recently, the latest advances in deep learning enable us to achieve high accuracy of classification in many applications such as speech and image recognition with relying on feature extraction customized for each application. In this paper, we propose a classification method of heart diseases based on ECG by adopting a machine learning method, called Long Short-Term Memory (LSTM), which is a state-of-the-art technique analyzing time series sequences in deep learning. As suitable data preprocessing, we also utilize symbolic aggregate approximation (SAX) to improve the accuracy. Our experimental results show that our approach not only achieves significantly better accuracy but also classifies heart diseases correctly in smaller response time than baseline techniques.
URI
http://dcollection.hanyang.ac.kr/common/orgView/000000107072http://repository.hanyang.ac.kr/handle/20.500.11754/75264
Appears in Collections:
GRADUATE SCHOOL OF ENGINEERING[S](공학대학원) > ELECTRICAL ENGINEERING AND COMPUTER SCIENCE(전기ㆍ전자ㆍ컴퓨터공학과) > Theses (Master)
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