Special Issue: “Research on Biomedical Signal Processing”
Source abstract
Abstract
Over recent years, the number of signals of a different type that can be acquired from the human body has increased extraordinarily [...]
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short
Research context
OpenAlex topics: ECG Monitoring and Analysis · Non-Invasive Vital Sign Monitoring · EEG and Brain-Computer Interfaces
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Clinical Physiology, National Research Council of Italy, via Moruzzi 1, 56124 Pisa, Italy
References (12)
Show source-provided references
1. Plechawska-Wójcik, M., Augustynowicz, P., Kaczorowska, M., Zabielska-Mendyk, E., and Zapała, D. (2023). The Influence Assessment of Artifact Subspace Reconstruction on the EEG Signal Characteristics. Appl. Sci., 13.10.3390/app13031605 ↗
2. Bachi, L., Billeci, L., and Varanini, M. (2021). QRS Detection Based on Medical Knowledge and Cascades of Moving Average Filters. Appl. Sci., 11.10.3390/app11156995 ↗
3. Ferdinando, H., Seppälä, E., and Myllylä, T. (2021). Discrete Wavelet Transforms-Based Analysis of Accelerometer Signals for Continuous Human Cardiac Monitoring. Appl. Sci., 11.10.3390/app112412072 ↗
4. De La Pava Panche, I., Gómez-Orozco, V., Álvarez-Meza, A., Cárdenas-Peña, D., and Orozco-Gutiérrez, Á. (2021). Estimating Directed Phase-Amplitude Interactions from EEG Data through Kernel-Based Phase Transfer Entropy. Appl. Sci., 11.10.3390/app11219803 ↗
5. Bonilauri, A., Sangiuliano Intra, F., Baselli, G., and Baglio, F. (2022). Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications. Appl. Sci., 12.10.3390/app12010316 ↗
6. Paternoster, F., and Seiberl, W. (2022). Comparison of Different Approaches Estimating Skeletal Muscle Oxygen Consumption Using Continuous-Wave Near-Infrared Spectroscopy at a Submaximal Contraction Level—A Comparative Study. Appl. Sci., 12.10.3390/app12052272 ↗
7. He, Z., Qin, Z., and Koike, Y. (2022). Continuous Estimation of Finger and Wrist Joint Angles Using a Muscle Synergy Based Musculoskeletal Model. Appl. Sci., 12.10.3390/app12083772 ↗
8. Zuccalà, V., Favilla, R., and Coppini, G. (2021). Recognition of Stress Activation by Unobtrusive Multi Sensing Setup. Appl. Sci., 11.10.3390/app11146381 ↗
9. Vaz, M., Summavielle, T., Sebastião, R., and Ribeiro, R.P. (2023). Multimodal Classification of Anxiety Based on Physiological Signals. Appl. Sci., 13.10.3390/app13116368 ↗
10. Aresta, S., Bortone, I., Bottiglione, F., Di Noia, T., Di Sciascio, E., Lofù, D., Musci, M., Narducci, F., Pazienza, A., and Sardone, R. (2022). Combining Biomechanical Features and Machine Learning Approaches to Identify Fencers’ Levels for Training Support. Appl. Sci., 12.10.3390/app122312350 ↗
11. Park, D., and Kim, I. (2022). Application of Machine Learning in the Field of Intraoperative Neurophysiological Monitoring: A Narrative Review. Appl. Sci., 12.10.3390/app12157943 ↗
12. Kumar, S., and Ramachandran, P. (2022). Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework. Appl. Sci., 12.10.3390/app12168368 ↗