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All Journal Indonesian Journal on Computing (Indo-JC) The International Journal of Medical Science and Health Research
Miftah Pramudyo
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Computer Science & IT Dentistry Health Professions Medicine & Pharmacology Nursing Public Health Veterinary

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Journal : Indonesian Journal on Computing (Indo-JC)

 1 
Study of Denoising Method to Detect Valvular Heart Disease Using Phonocardiogram (PCG) Muhammad Yaumil Ihza Ihza; Satria Mandala; Miftah Pramudyo
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 1 (2022): April, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.1.610

Abstract

Heart sound is a very weak acoustic signal, very susceptible to external acoustic signals and electrical disturbances, especially friction caused by the subject's breathing or body movements. The heart sound signal will be recorded in a phonocardiogram (PCG) and produce heart sounds, noise, and extra sounds. The purpose of this work is to denoise the signal from the heart sounds recorded on the PCG and determine valvular heart disease (VHD). Several methods have been proposed for denoising heart sound signals, both in the time domain and in the frequency domain. Most of these methods still have problems for denoising results. In this paper, the techniques used to denoise the heart sound signal are Discrete Wavelet Transform (DWT), Short Term Fourier Transform (STFT), and Low-Pass filter.
Study of Machine Learning Algorithm on Phonocardiogram Signals for Detecting of Coronary Artery Disease Satria Mandala; Miftah Pramudyo; Ardian Rizal; Maurice Fikry
Indonesia Journal on Computing (Indo-JC) Vol. 5 No. 3 (2020): December, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.3.536

Abstract

Several methods of detecting coronary artery disease (CAD) have been developed, but they are expensive and generally use an invasive catheterization method. This research provides a solution to this problem by developing an inexpensive and non-invasive digital stethoscope for detecting CAD. To prove the effectiveness of this device, twenty-one subjects consisting of 11 CAD patients and 10 healthy people from Hasan Sadikin Hospital Bandung were selected as validation test participants. In addition, auscultation was carried out at four different locations around their chests, such as the aorta, pulmonary, tricuspid, and mitral. Then the phonocardiogram data taken from the stethoscope were analyzed using machine learning. To obtain optimal detection accuracy, several types of kernels such as radial basis function kernel (RBF), polynomial kernel and linear kernel of Support Vector Machine (SVM) have been analyzed. The experimental results show that the linear kernel outperforms compared to others; it provides a detection accuracy around 66%. Followed by RBF is 56% and Polynomial is 46%. In addition, the observation of phonocardiogram signals around the aorta is highly correlated with CAD, giving an average detection accuracy for the kernel of 66%; followed by 44% tricuspid and 43% pulmonary.
Performance Analysis of PPG Signal Denoising Method Using DWT and EMD for Detection of PVC and AF Arrhytmias: Analisis Performansi Metode Denoising Sinyal PPG Menggunakan DWT dan EMD untuk deteksi Aritmia PVC dan AF Muhammad Aniq Wafa; Satria Mandala; Miftah Pramudyo
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.2.648

Abstract

In the cardiac arrhythmia detection system using a Photoplethysmography (PPG) sensor, noise is often found in the PPG signal due to internal and external factors in the signal retrieval process. So it is necessary to do a denoising process to remove noise before the signal is used. This study aims to test the Discrete wavelet transform (DWT) and Empirical Mode Decomposition (EMD) methods in removing noise from the PPG signal and to test the denoising signal on the Premature Arrhythmia Verticular Contractions (PVC) and Atrial Fibrillation (AF) detection systems. The parameters used to compare the performance of the denoising method are Mean Square Error (MSE), Signal to Noise Ratio (SNR), Accuracy, F1, Precision, and Recall. The method with the highest SNR, Accuracy, F1, Precision, and Recall values ​​and the lowest MSE values ​​is the best denoising method.
Co-Authors Ardian Rizal Frisky Ronald Tua Marbun Maurice Fikry Muhammad Aniq Wafa Muhammad Yaumil Ihza Ihza Rheza Felari Simanjuntak Rhinza Seputra Simanjuntak Satria Mandala Tatyana Sianipar