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  • Open Access


    Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals

    Ashit Kumar Dutta1,*, Yasser Albagory2, Manal Al Faraj1, Yasir A. M. Eltahir3, Abdul Rahaman Wahab Sait4

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1517-1529, 2023, DOI:10.32604/csse.2023.026482

    Abstract The recently developed machine learning (ML) models have the ability to obtain high detection rate using biomedical signals. Therefore, this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography (EEG) Biomedical Signals, named OSAE-SSCEEG technique. The major intention of the OSAE-SSCEEG technique is to find the sleep stage disorders using the EEG biomedical signals. The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach. Moreover, the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization (SAE-SR) with softmax (SM) approach. Finally, the parameter optimization of the SAE-SR technique is carried out… More >

  • Open Access


    Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

    Souad Larabi-Marie-Sainte1, Eatedal Alabdulkreem2, Mohammad Alamgeer3, Mohamed K Nour4, Anwer Mustafa Hilal5,*, Mesfer Al Duhayyim6, Abdelwahed Motwakel5, Ishfaq Yaseen5

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4589-4601, 2022, DOI:10.32604/cmc.2022.027922

    Abstract Electroencephalography (EEG) eye state classification becomes an essential tool to identify the cognitive state of humans. It can be used in several fields such as motor imagery recognition, drug effect detection, emotion categorization, seizure detection, etc. With the latest advances in deep learning (DL) models, it is possible to design an accurate and prompt EEG EyeState classification problem. In this view, this study presents a novel compact bat algorithm with deep learning model for biomedical EEG EyeState classification (CBADL-BEESC) model. The major intention of the CBADL-BEESC technique aims to categorize the presence of EEG EyeState. The CBADL-BEESC model performs feature… More >

  • Open Access


    Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

    S. Karthik1, M. Santhosh1,*, M. S. Kavitha1, A. Christopher Paul2

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 183-199, 2022, DOI:10.32604/csse.2022.021698

    Abstract Automated biomedical signal processing becomes an essential process to determine the indicators of diseased states. At the same time, latest developments of artificial intelligence (AI) techniques have the ability to manage and analyzing massive amounts of biomedical datasets results in clinical decisions and real time applications. They can be employed for medical imaging; however, the 1D biomedical signal recognition process is still needing to be improved. Electrocardiogram (ECG) is one of the widely used 1-dimensional biomedical signals, which is used to diagnose cardiovascular diseases. Computer assisted diagnostic models find it difficult to automatically classify the 1D ECG signals owing to… More >

  • Open Access


    Intelligent Biomedical Electrocardiogram Signal Processing for Cardiovascular Disease Diagnosis

    R. Krishnaswamy1,*, B. Sivakumar2, B. Viswanathan3, Fahd N. Al-Wesabi4,5, Marwa Obayya6, Anwer Mustafa Hilal7

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 255-268, 2022, DOI:10.32604/cmc.2022.021995

    Abstract Automatic biomedical signal recognition is an important process for several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patterns of the ECG signals. In order to raise the diagnostic accuracy and reduce the diagnostic time, automated computer aided diagnosis model is necessary. With the advancements of artificial intelligence (AI) techniques, large quantity of biomedical datasets can be easily examined for decision making. In this aspect, this paper presents an intelligent biomedical ECG signal processing (IBECG-SP) technique for CVD diagnosis. The proposed IBECG-SP technique examines… More >

  • Open Access


    Transfer Learning Model to Indicate Heart Health Status Using Phonocardiogram

    Vinay Arora1, Karun Verma1, Rohan Singh Leekha2, Kyungroul Lee3, Chang Choi4,*, Takshi Gupta5, Kashish Bhatia6

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4151-4168, 2021, DOI:10.32604/cmc.2021.019178

    Abstract The early diagnosis of pre-existing coronary disorders helps to control complications such as pulmonary hypertension, irregular cardiac functioning, and heart failure. Machine-based learning of heart sound is an {efficient} technology which can help minimize the workload of manual auscultation by automatically identifying irregular cardiac sounds. Phonocardiogram (PCG) and electrocardiogram (ECG) waveforms provide the much-needed information for the diagnosis of these diseases. In this work, the researchers have converted the heart sound signal into its corresponding repeating pattern-based spectrogram. PhysioNet 2016 and PASCAL 2011 have been taken as the benchmark datasets to perform experimentation. The existing models, viz. MobileNet, Xception, Visual… More >

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