Key result
ECG classification based on baseline detection and feature extraction achieves high accuracy matching sinus rhythms.
The proposed approach for ECG waveform classification based on P-QRS-T wave recognition provides an adequate recognition rate for verifying heart functionality.
May aid ECG waveform classification; leaves open prospective validation before clinical adoption.
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.
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Muzhir Shaban Al-Ani (2018) studied Heart diseases (ECG waveform classification). ECG waveform classification approach based on P-QRS-T wave recognition was evaluated on ECG waveform recognition and matching accuracy. The proposed ECG waveform classification approach based on baseline detection and feature extraction achieved high accuracy in matching normal sinus rhythm, sinus tachycardia, sinus bradycardia, and sinus arrhythmia.
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