Feature Extraction of Hidden Oscillation in ECG Data Via Multiple-FOD Method



Feature extraction of hidden oscillation in ECG data via multiple-FOD method
Purutçuoğlu Gazi, Vilda; Erkuş, Ekin Can (null; 2019-10-30)
Fourier transform (FT) is a non-parametric method which can be used to convert the time domain data into the frequency domain and can be used to find the periodicity of oscillations in time series datasets. In order to detect periodic-like outliers in time series data, a novel and promising method, named as the outlier detection via Fourier transform (FOD), has been developed. From our previous studies, it has been shown that FOD outperforms most of the commonly used approaches for the detection of outliers...
Feature extraction from acoustic and hyperspectral data by 2d local discriminant bases search
Kalkan, Habil; Kalkan, Habil; Department of Information Systems (2008)
In this thesis, a feature extraction algorithm based on 2D Local Discriminant Bases (LDB) search is developed for acoustic and hyperspectral data. The developed algorithm extracts the relevant features by both eliminating the irrelevant ones and/or by merging the ones that do not provide extra information on their own. It is implemented on real world data to separate aflatoxin contaminated or high risk hazelnuts from the sound ones by using impact acoustic and hyperspectral data. Impact acoustics data is us...
Feature Selection in Modeling of Chip-Seq Data
Purutçuoğlu Gazi, Vilda (null; 2018-09-20)
Feature Selection for Graph Kernels
TAN, MEHMET; Polat, Faruk; Alhajj, Reda (2010-12-21)
Graph classification is important for different scientific applications; it can be exploited in various problems related to bioinformatics and cheminformatics. Given their graphs, there is increasing need for classifying small molecules to predict their properties such as activity, toxicity or mutagenicity. Using subtrees as feature set for graph classification in kernel methods has been shown to perform well in classifying small molecules. It is also well-known that feature selection can improve the perfor...
Feature Extraction and Object Classification for Target Identification at Wireless Multimedia Sensor Networks
Civelek, Muhsin; Yilmazer, Cengiz; Yazıcı, Adnan; Korkut, Fazli Oncul (2014-04-25)
In this paper, it is investigated the processes for automatic identification of the targets without personnel intervention in wireless multimedia sensor networks. Methods to extract the features of the object from the multimedia data and to classify the target type based on the extracted features are proposed within the scope of this study. The success of the proposed methods are tested by implementing a Matlab application and the results are presented in this paper
Citation Formats
E. C. Erkuş and V. Purutçuoğlu Gazi, Feature Extraction of Hidden Oscillation in ECG Data Via Multiple-FOD Method. 2020, p. 56.