Cardiovascular disease is the leading cause of death in the world, so early detection of heart conditions is very important. Detection related to cardiovascular disease can be conducted through the detection of heart signals interference, one of which is called phonocardiography. This study aims to classify heart disease based on phonocardiogram (PCG) signals using the convolutional neural networks (CNN). The study was initiated with signal preprocessing by cutting and normalizing the signal, followed by a continuous wavelet transformation process using a mother wavelet analytic morlet. The decomposition results are visualized using a scalogram, then the results are used as CNN input. In this study, the PCG signals used were classified into normal, angina pectoris (AP), congestive heart failure (CHF), and hypertensive heart disease (HHD). The total data used, classified into 80 training data and 20 testing data. The obtained model shows the level of accuracy, sensitivity, and diagnostic specificity of 100%, 100%, and 100% for training data, respectively, while the prediction results for testing data indicate the level of accuracy, sensitivity, and specificity of 85%, 80%, and 100%, respectively. This result proved to be better than the mother wavelet or other classifier methods, then the model was deployed into the graphical user interface (GUI).

To cite this article: Aisyah, A. N. et al. (2018). Effect of frequency and number of piezoelectric probes in sonication-assisted exfoliation of graphite layers into graphene oxide.

Electro-acoustic human heartbeat detector have been made with the main parts : (a) stetoscope (piece chest), (b) mic condenser, (c) transistor amplifier, and (d) cues analysis program with MATLAB. The frequency components that contained in heartbeat. cues have also been extracted with Short Time Fourier Transform (STFT) from 9 volunteers. The results of the analysis showed that heart rate appeared in every cue frequency spectrum with their harmony. The steps of the research were including detector instrument design, test and instrument repair, cues heartbeat recording with Sound Forge 10 program and stored in wav file ; cues breaking at the start and the end, and extraction/cues analysis using MATLAB. The MATLAB program included filter (bandpass filter with bandwidth between 0.01 – 110 Hz), cues breaking with hamming window and every part was calculated using Fourier Transform (STFT mechanism) and the result were shown in frequency spectrum graph. Keywords: frequency components extraction, heartbeat cues, Short Time Fourier Transform

The purpose of this research is to design and build Phonocardiography (PCG) system that can record the human heartbeat representatively and reliably, which is a device that can record a lot of information contained in a heartbeat. This research begins by designing and realizing devices that can record acoustic heartbeat signals. The PCG signal is easy to affect by noise such as stethoscope movement, the friction of stethoscope and chest, etc.. Therefore, signal analysis process such as denoising is needed. Denoising can be performed by using a discrete wavelet transform (DWT). In this research, the kind of wavelet that were used are coiflet1, symlet1, symlet2, daubechies3, and daubechies4. This wavelet type is compared with the original signal, and the mean square error (MSE) calculated. From the analysis results, the type of wavelet that gives the smallest MSE is symlet2 indicating that wavelet most resembles the original signal compared with the other two wavelets. The result of character extraction will be used in subsequent research to be input in a pattern recognition system.

The performance of a custom-made electronic stethoscope needs to be improved through a study on the operating point of the amplifier. The aim of this study is to analyse the composition and content of the frequency components contained in the phonocardiogram signal by spectral extraction. The output is a voltage signal that represents the heart rate. The operating point of the amplifier is varied. The signal is recorded on a laptop and analysed. The results show that the operating point produces a balanced swing signal. Hence, the amplifier’s operating point does not affect the component spectrum in the recorded signal.