IDENTIFIKASI ISYARAT ELEKTROKARDIOGRAM SEGMEN ST DAN KONTRAKSI VENTRIKEL PREMATUR BERBASIS GELOMBANG SINGKAT

Heart disease is an important public health problem because of its high morbidity and mortality. On heart disease, identification of PVC and ST segment is very important to know to define therapy strategy and prognosis. PVC indicate an increased risk of sudden death and emergence of ST segment are the earliest sign of a heart attack. PVC and ST segment is often not detected if the examination is done manually, this is because the shape of PVC and ST segment varies the amplitude, phase and angle of tilt and often appeared only a few moments between normal electrocardiogram signal. PVC and ST Segment Detection accurately is the background of this research. This study aimed to identify abnormalities PVC and ST segment using Wavelet detection. This method is used to optimize the time-frequency components more appropriate than the STFT method. New wavelet formed by finding the highest correlation value at each different wavelet scale until get a new wavelet equation. Area under curve (AUC) methods were done for Statistical analysis to evaluate sensitivity new wavelet design.The originality of this study was applied to a new wavelet design DeGeNorm, DeGePVC and DeGeSTSeg. This study has been validated using a different sampling rate, different noise signals and diverse morphology from the six leads electrocardiogram. This study have also been compared with morlet and mexican hat. The results show the effectiveness of new wavelet algorithm. DeGeNorm with the value of auc=0.998. DeGePVC with the value of auc=0.988 and DeGeSTSeg with the value of auc=0.994. Higher than Mexican hat and Morlet with the value of auc=0.753. The advantage of using this new wavelet detection is reducing the sensitivity to noise compared to other techniques, with the determination of each component of the electrocardiogram accurately and quickly. That is because mother wavelet uses generaly and not specific to PVC and ST segment identificatio