THE USAGE OF STATISTICAL FEATURES IN THE APPROXIMATION COMPONENTS OF WAVELET DECOMPOSITION FOR ECG CLASSIFICATION: A CASE STUDY FOR STANDING, WALKING AND SINGLE JUMP CONDITIONS

Normal 0 21 false false false TR X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:`Normal Tablo`; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:``; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:`Calibri`,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} The purpose of this study is to classify electrocardiogram insert ignore into journalissuearticles values(ECG); signals with a high accuracy rate. The ECG signals used are obtained from the Physiobank archive. These signals are preprocessed to remove noise. Features with distinctiveness in classification are obtained both in the time domain and the frequency domain. The Discrete Wavelet Transform method is used for feature extraction in frequency domain. ECG signals are classified by the Naive Bayes method after the required features are extracted.