Robust ECG data compression method based on ε-insensitive Huber loss function

Authors : Ömer KARAL, İlyas Çankaya

Pages : 1142-1151

Doi:10.16984/saufenbilder.407686

View : 19 | Download : 7

Publication Date : 2018-08-01

Article Type : Research Paper

Abstract :Electrocardiogram insert ignore into journalissuearticles values(ECG); signals are continuously monitored for early diagnosis of heart diseases. However, a long-term monitoring generates large amounts of data at a level that makes storage and transmission difficult. Moreover, these records may be subject to different types of noise distributions resulting from operating conditions. Therefore, an effective and reliable data compression technique is needed for ECG data transmission, storage and analysis without losing the clinical information content. This study proposes the ε-insensitive Huber loss based support vector regression for the compressing of ECG signals. Since the Huber loss function is a mixture of quadratic and linear loss functions, it can properly take into account the different noise types in the data set. Compression performance of the proposed method has been assessed using ECG records from the MIT-BIH arrhythmia database. Experimental results demonstrate that the proposed loss function is an attractive candidate for compressing ECG data.
Keywords : Data Compression, Electrocardiogram, Huber loss function, Support Vector Regression