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

Year 2018, , 1142 - 1151, 01.08.2018

Ömer Karal , İlyas Çankaya

https://doi.org/10.16984/saufenbilder.407686

Cited By: 1

Abstract

Electrocardiogram
(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

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