Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning
portes grátis
Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning
Qaisar, Saeed Mian; Nisar, Humaira; Subasi, Abdulhamit
Springer International Publishing AG
03/2024
373
Mole
Inglês
9783031232411
15 a 20 dias
Descrição não disponível.
1. ?Introduction to non-invasive biomedical signals for healthcare.- 2. Signal Acquisition Preprocessing and Feature Extraction Techniques for Biomedical Signals.- 3. The Role of EEG as Neuro-Markers for Patients with Depression: A systematic Review.- 4. Brain-Computer Interface (BCI) Based on the EEG Signal Decomposition Butterfly Optimization and Machine Learning.- 5. Advances in the analysis of electrocardiogram in context of mass screening: technological trends and application of artificial intelligence anomaly detection.- 6. Application of Wavelet Decomposition and Machine Learning for the sEMG Signal based Gesture Recognition.- 7. Review of EEG Signals Classification using Machine Learning and Deep-learning Techniques.- 8. "Biomedical signal processing and artificial intelligence in EOG signals".- 9. Peak Spectrogram and Convolutional Neural Network-based Segmentation and Classification for Phonocardiogram Signals.- 10. Eczema skin lesions segmentation using deep neural network (U-net).- 11. Biomedical signal processing for automated detection of sleep arousals Based on Multi-Physiological Signals with Ensemble learning methods.- 12. Deep Learning Assisted Biofeedback.- 13. Estimations of Emotional Synchronization Indices for Brain regions using Electroencephalogram Signal Analysis.- 14. Recognition Enhancement of Dementia Patients' Working Memory using Entropy-based Features and Local Tangent Space Alignment Algorithm.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Artificial intelligence;Biomedical signal processing;Classification;Chronic obstructive pulmonary (COP);Compression;Dimension reduction;Deep Learning;Electrocardiogram (ECG);Electroencephalogram (EEG);Electromyography (EMG);Electrooculography (EOG);Empirical mode decomposition;Fourier analysis;Features extraction;Machine learning;Non-invasive sensing;Optimization algorithms;Pre-processing, phonocardiogram (PCG);Time-frequency analysis;Wavelet transform
1. ?Introduction to non-invasive biomedical signals for healthcare.- 2. Signal Acquisition Preprocessing and Feature Extraction Techniques for Biomedical Signals.- 3. The Role of EEG as Neuro-Markers for Patients with Depression: A systematic Review.- 4. Brain-Computer Interface (BCI) Based on the EEG Signal Decomposition Butterfly Optimization and Machine Learning.- 5. Advances in the analysis of electrocardiogram in context of mass screening: technological trends and application of artificial intelligence anomaly detection.- 6. Application of Wavelet Decomposition and Machine Learning for the sEMG Signal based Gesture Recognition.- 7. Review of EEG Signals Classification using Machine Learning and Deep-learning Techniques.- 8. "Biomedical signal processing and artificial intelligence in EOG signals".- 9. Peak Spectrogram and Convolutional Neural Network-based Segmentation and Classification for Phonocardiogram Signals.- 10. Eczema skin lesions segmentation using deep neural network (U-net).- 11. Biomedical signal processing for automated detection of sleep arousals Based on Multi-Physiological Signals with Ensemble learning methods.- 12. Deep Learning Assisted Biofeedback.- 13. Estimations of Emotional Synchronization Indices for Brain regions using Electroencephalogram Signal Analysis.- 14. Recognition Enhancement of Dementia Patients' Working Memory using Entropy-based Features and Local Tangent Space Alignment Algorithm.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Artificial intelligence;Biomedical signal processing;Classification;Chronic obstructive pulmonary (COP);Compression;Dimension reduction;Deep Learning;Electrocardiogram (ECG);Electroencephalogram (EEG);Electromyography (EMG);Electrooculography (EOG);Empirical mode decomposition;Fourier analysis;Features extraction;Machine learning;Non-invasive sensing;Optimization algorithms;Pre-processing, phonocardiogram (PCG);Time-frequency analysis;Wavelet transform
