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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Health and Information System (IJHIS)
Said, Ziani
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Computer Science & IT Engineering Health Professions Library & Information Science Medicine & Pharmacology Public Health

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Obstructive sleep apnea detection based on electrocardiogram signal using one-dimensional convolutional neural network Widadi, Rahmat; Rizal, Achmad; Hadiyoso, Sugondo; Fauzi, Hilman; Said, Ziani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4129-4137

Abstract

Obstructive sleep apnea (OSA) is a respiratory obstruction that occurs during sleep and is often known as snoring. OSA is often ignored even though it can cause cardiovascular problems. Early diagnosis is needed for prevention towards worse complications. OSA clinical diagnosis can use polysomnography (PSG) while the patient is sleeping. The PSG examination includes calculating total apnea plus hypopnea every hour during sleep. However, PSG examination tends to be high cost, takes a long time, and is impractical. Since OSA is related to breathing and heart activity, the electrocardiogram (ECG) examination is an alternative tool in OSA analysis. Therefore, this study proposes OSA detection on single lead ECG using one dimensional (1D)-convolutional neural network (CNN). The proposed CNN architecture consists of 4 convolutional layers, 4 pooling layers, 1 dropout layer, 1 flatten layers, 2 dropout layers, 1 dense layer with rectified linear unit (ReLU) activation function, and 1 dense layer with SoftMax activation function. The proposed method was then tested on the ECG sleep apnea dataset from PhysioNet. The proposed model produces an accuracy of 92.69% with the average pooling scenario. The proposed method is expected to help clinicians in diagnosing OSA based on ECG signals.
Fuzzy Technology Design for Early Detection of Diseases in Tobacco Plants Anwar, Saiful; Said, Ziani; Dinata, Muhammad Imam
International Journal of Health and Information System Vol. 1 No. 1 (2023): May
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ijhis.v1i1.8

Abstract

Tobacco is an agricultural product that uses leaves to be processed into pesticides, medicines and cigarettes. Tobacco quality is determined by plant maintenance and reduced pest and disease attacks. To avoid these disturbances, control is needed quickly, precisely and accurately so that the tobacco plant disease cannot spread throughout agricultural land. In making fuzzy, diseases and symptoms in tobacco plants are used as a rule base in making a fuzzy expert system. The expert system created in this research is an expert system using the concept of fuzzy logic to diagnose tobacco plant diseases, using the Mamdani inference method and the defuzzification process using the centroid method (firmness value) to get the right conclusions in diagnosing tobacco plant diseases. From the results of Mamdani's design and manual fuzzy calculations, it can be concluded that the design is ready to be further implemented into the required programming language. From the sample calculation results, it was found that damping off disease has a moderate degree of risk with a value of 41.54. With the construction of this system, it will provide easy information for farmers to carry out and find out what symptoms are contracting diseases in tobacco plants.
Co-Authors Achmad Rizal Dinata, Muhammad Imam Hilman Fauzi, Hilman Rahmat Widadi Saiful Anwar Sugondo Hadiyoso