The biomedical industry has grown in recent years because of advanced technology. Advanced sensing technologies and internet-of-things (IoT) devices offer real-time monitoring. Artificial intelligence (AI) algorithms have introduced new approaches to predictive and remote healthcare. Biomedical signals acquired through non-invasive sensors often have a low signal to noise ratio. They are processed using advanced signal processing methods. The classification and analysis of the signals are used in prognosis and diagnosis. They are also used in brain-computer interfaces and neural prostheses. Smart sensors and advanced signal processing techniques play a significant role in the healthcare sector by employing automated and computer-aided diagnosis (CAD) systems.

Biomedical signal processing is used to analyse, interpret, and classify various signals. It is captured through different types of non-invasive sensors and undergoes a few major challenges (e.g., dimensionality problem, artifacts, poor sampling effect, robustness, appropriate selection of channels and features, optimisation problems). Appropriate biomedical signal processing techniques are needed to overcome these challenges. These techniques help the prediction, diagnosis, and monitoring of patients. They also help track healthy patients when driving, exercising, and sleeping. Modern IoT framework, wireless biomedical implants, smart camera systems, smartphones, and smartwatches are used to monitor healthy patients.

The aim of this Special Issue is to bring together original research articles and review articles talking about recent advances and real-time applications for biomedical sensors. Submissions about new trends in measuring and processing biomedical information are welcome. Research on the use of intelligent algorithms to analyse biomedical signals, support diagnosis, and decision making are also encouraged.

Potential topics include but are not limited to the following:

• Non-invasive sensors used to receive and analyse biomedical signals
• Time-frequency and time-domain analysis of biomedical signals
• Non-linear and statistical signal processing and analysis for healthcare
• Signal processing for neuromonitoring and neuromodulation
• Brain-computer interaction and cognitive assessment
• Intelligent healthcare systems
• Application of machine and deep learning for biomedical signals in healthcare
• Computational simulation and modelling for biomedical applications in healthcare
• Current and future research trends in biomedical signals and image processing