Construction of Fuzzy System for Classification of Heart Disease Based on Phonocardiogram Signal

Abadi, Agus Maman; Sumarna,

doi:10.1109/aidas47888.2019.8970975

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…The data used in this study are secondary data obtained from the observation of heart disease patients and healthy people at PKU Muhammadiyah Yogyakarta Hospital conducted by and [36] on February 24, 2017, to April 18, 2017. The data were collected in the form of heart rate signal recordings in .wav file TELKOMNIKA Telecommun Comput El Control  Classification of heart disease based on PCG signal using CNN (Aditya Wisnugraha Sugiyarto) 1699 format from 75 volunteers with heart disease and 25 normal people.…”

Section: Methodsmentioning

confidence: 99%

“…Research related to the analysis of heart disease based on the PCG signal has been used [28]- [31]. The analysis using wavelet transforms and the ML method for classification also has been used extensively [32]- [36]. Currently, it is widely accepted that the continuous wavelet transform (CWT) method is the most appropriate for analyzing non-stationary PCG signals (having various frequencies and in time) [37], [38].…”

Section: Introductionmentioning

confidence: 99%

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Classification of heart disease based on PCG signal using CNN

2021

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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).

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of heart disease based on PCG signal using CNN

2021

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“…The results of electrocardiograph sensors are in the form of graphs or signals that can be used to diagnose arrhythmias in the heart. In addition to ECG signals, a phonocardiogram (PCG) can be used to diagnose heart disease [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Classification of Arrhythmia Diseases by the Convolutional Neural Network Method Based on Ecg Images

Pratama

Abadi

2023

BAREKENG: J. Math. & App.

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Arrhythmia is a heart disorder that refers to an abnormal heartbeat rhythm. Arrhythmia detection uses an electrocardiogram (ECG) to describe the heart's electrical activity. This research aimed to know the performance of the Convolutional Neural Network method in classifying arrhythmia diseases based on ECG signal images. Several stages were used to classify arrhythmias: the pre-processing data stage, CNN model formation stage, model compiling, training, model testing, and evaluation. The CNN model architecture that is formed involves 7 Convolution Layers, 7 Pooling Layers, 2 Dropout Layers, 2 Dense Layers, and 1 Flatten Layer, as well as ReLu and Softmax activation functions. The input variable in the classification process with CNN is an ECG image. The output variable is the classification of ECG signals into 17 classes, including normal sinus and pacemaker rhythms. The processed data are 1000 images; the division scenario is 750 training data and 250 testing data. The result of arrhythmia's classification based on ECG image testing data using the CNN model shows the levels of Accuracy, Precision, Recall, and F1-score levels are 81%, 80%, 71%, and 73%, respectively, respectively. With the F1-score value as a measurement reference, the CNN model performs well in classifying ECG images

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Heart disease detection system based on ECG and PCG signals with the aid of GKVDLNN classifier

Jyothi,

Pradeepini

2023

Multimed Tools Appl

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Construction of Fuzzy System for Classification of Heart Disease Based on Phonocardiogram Signal

Cited by 3 publications

References 23 publications

Classification of heart disease based on PCG signal using CNN

Classification of heart disease based on PCG signal using CNN

Classification of Arrhythmia Diseases by the Convolutional Neural Network Method Based on Ecg Images

Heart disease detection system based on ECG and PCG signals with the aid of GKVDLNN classifier

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