Intelligent Biosignal Processing in Wearable and Implantable Sensors

electrocardiogram; deep metric learning; k-nearest neighbors classifier; premature ventricular contraction; dimensionality reduction; classifications; Laplacian eigenmaps; locality preserving projections; compressed sensing; convolutional neural network; EEG; epileptic seizure detection; RISC-V; ultra-low-power; sepsis; atrial fibrillation; prediction; heart rate variability; feature extraction; random forest; annotations; myoelectric prosthesis; sEMG; grasp phases analysis; grasp classification; machine learning; electronic nose; liver dysfunction; cirrhosis; semiconductor metal oxide gas sensor; vagus nerve; intraneural; decoding; intrafascicular; recording; carbon nanotube; artificial intelligence; lens-free shadow imaging technique; cell-line analysis; cell signal enhancement; deep learning; compressed sensing; ECG signal; reconstruction dictionaries; projection matrices; signal classifications; osteopenia; sarcopenia; XAI; SHAP; IMU; gait analysis; artificial intelligence; sensors; convolutional neural networks; Parkinson's disease; biomedical monitoring; accelerometer; pressure sensor; disease management; electromyography; correlation; high blood pressure; hypertension; photoplethysmography; electrocardiography; calibration; classification models; machine learning; deep learning; COVID-19; ECG trace image; transfer learning; Convolutional Neural Networks (CNN); feature selection; sympathetic activity (SNA); skin sympathetic nerve activity (SKNA); electrodes; electrocardiogram (ECG); cardiac time interval; dynamic time warping; fiducial point detection; heart failure; seismocardiography; wearable electroencephalography; motor imagery; motor execution; beta rebound; brain-machine interface; feature extraction; EEG classification; n/a