Open AccessECG Waveform Classification Based on P-QRS-T Wave Recognition
Author(s) -
Muzhir Shaban Al-Ani
Publication year - 2018
Publication title -
uhd journal of science and technology
Language(s) - English
Resource type - Journals
eISSN - 2521-4217
pISSN - 2521-4209
DOI - 10.21928/uhdjst.v2n2y2018.pp7-14
Subject(s) - waveform , qrs complex , preprocessor , pattern recognition (psychology) , signal (programming language) , computer science , artificial intelligence , amplitude , graph , feature extraction , speech recognition , cardiology , medicine , physics , telecommunications , radar , theoretical computer science , quantum mechanics , programming language
Electrocardiogram (ECG) is a periodic signal reflects the activity of the heart. ECG waveform is an important issue to define the heart function so it is helpful to recognize the type of heart diseases. ECG graph generate a lot of information that is converted into electrical signal with standard values of amplitude and duration. The main problem raised in this measurement is the mixing between normal and abnormal, in addition some time there are overlapping between the P-QRS-T waveform. This research aims to offer an efficient approach to measure all parts of P-QRS-T waveform in order to give a correct decision of heart functionality. The implemented approach including any steps; preprocessing, baseline process, feature extraction and diagnosis. The obtained result indicated an adequate recognition rate to verify the heart functionality.