Support Vector Machine ( Svm )

767 Words Nov 20th, 2015 4 Pages
Support Vector Machine (SVM) is primarily a classier method that performs classification tasks by constructing hyperplanes in a multidimensional space that separates cases of different class labels. SVM supports both regression and classification tasks and can handle multiple continuous and categorical variables. For categorical variables a dummy variable is created with case values as either 0 or 1. Thus, a categorical dependent variable consisting of three levels, say (A, B, C), is represented by a set of three dummy variables:

A:{1 0 0}, B: {0 1 0}, c:{0 0 1}

To construct an optimal hyperplane, SVM employs an iterative training algorithm, which is used to minimize an error function. According to the form of the error function, SVM models can be classified into four distinct groups:
• Classification SVM Type 1 (also known as C-SVM classification)
• Classification SVM Type 2 (also known as nu-SVM classification)
• Regression SVM Type 1 (also known as epsilon-SVM regression)
• Regression SVM Type 2 (also known as nu-SVM regression)
C. Dataset
The dataset for ECG signals are obtained from MIT-BIH pyhsionet database. There were two databeses present in the website, one was Normal Sinus Rhythm database(NSR), and other was sudden cardiac death(SCD) database. In this database it had one hour of each pateints ECG record, where 30 minutes were prior to cardiac arrest. Every 5 minutes of ECG srignal were used to record HRV, and thus keeping a window size of 10 minutes, HRV values…
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