EKSTRAKSI CIRI DAN KLASIFIKASI ISYARAT ECG BERBASIS TRANSFORMASI WAVELET DAN JARINGAN NEURAL BACKPROPAGATION

• Main Authors: , Budi Sumanto, , Prof. Dr. Ir. Thomas Sri Widodo
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2012
• Subjects: ETD
• Online Access: https://repository.ugm.ac.id/98771/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=55491
• Institution: Universitas Gadjah Mada

Electrocardiogram (ECG) is a recorded bio-elektrik activity of the heart that goes around the body and can be detected at some point leads. Shape or pattern of the ECG signal represents the state of heart health in general. Because the ECG signal is not stationary then it is important to know the information contained in these signals need to be a suitable method. To help analyze these signals not only at the time but also the frequency region. Therefore, wavelet transform method is ideal for application in analyzing the ECG signal. Wavelet transformation is applied to analyze the ECG signal to determine characteristics of the ECG signal of average power values, but before the selection of the most appropriate type of wavelets in analyzing the ECG signal is also very important. The characteristics that have been obtained with up to 5 levels of wavelet decomposition will be trained by using backpropagation method to form a network that will be able to recognize any kind of heart condition based on the characteristics of each. Four types of heart conditions are Normal Sinus Rhythm (NSR), Malignant Ventricular Ectopy (MVE), supraventricular arrhythmia (SVA) and polysomnographic (PS). The results of this study indicate the type of wavelet Symlet (Sym8) which has a mean squared error (MSE) of the dominant lower than other types such as Daubechies wavelet (db10), Biorthogonal (bior4.8), Coiflet (coif5), Symlet (Sym8) and discret Meyer (Dmey). For the backpropagation training process obtained at epoch to 39 to achieve convergence with its MSE value is 9,77.10-7 and slope is 0.000342. While the sensitivity of the system to recognize types of heart conditions for all data in the training process is 100% and sensitivity of the system in the testing process is 90%.