Cardiac Arrhythmia Detection Using DCT Based Compressive Sensing and Random Forest Algorithm (CROSBI ID 637863)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Marasović, Tea ; Papić, Vladan
engleski
Cardiac Arrhythmia Detection Using DCT Based Compressive Sensing and Random Forest Algorithm
The discrimination of ECG signals is of crucial importance in clinical diagnoses of cardiac diseases. Manual analysis of ECG signals is very complex and time consuming task due to their composite nature. This paper proposes a novel scheme for reliable automatic classification of ECG signals into normal and three different abnormal (arrhythmia affected) categories. The feature extraction is based on an amalgamation of discrete cosine transform and random projection for dimensionality reduction. Furthermore, the classification is performed using random forest algorithm. The performance of the proposed scheme is evaluated on the restricted subset of ECG recordings from MIT-BIH arrhythmia database. In the experiments, a near perfect recognition accuracies of 99:33% and 99%, depending on the definition of projection matrix, are achieved with only 50 random projected coefficients ; i.e. after considerable dimensionality reduction of the input ECG signal.
ECG; cardiac arrhythmia; discrete cosine transform; compressive sensing; random projection; random forest
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
2016.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of the 1st International Multidisciplinary Conference on Computers and Energy Science (SpliTech 2016)
978-953-290-060-6
Podaci o skupu
International Multidisciplinary Conference on Computers and Energy Science (SpliTech 2016)
predavanje
13.07.2016-15.07.2016
Split, Hrvatska