Deep Learning for High-Dimensional Sense, Non-Linear Signal Processing and Intelligent Diagnosis, vol II

This is a volume II of our previous collection. Accurate interpretation of complex, high-dimensional signals derived from diagnostic tools such as EEG and MRI is crucial for advancing the understanding and treatment of mental health disorders. These signals, characterized by their high dimensionality, non-linearity, and low signal-to-noise ratios, pose significant challenges in terms of analysis and practical application. However, with the burgeoning field of medical big data and the advances in deep learning technologies, unprecedented opportunities emerge for enhancing real-time diagnostic and monitoring systems within psychiatric settings. This landscape calls for innovative approaches in integrating IoT with psychiatric practices to revolutionize how we detect, monitor, and treat mental health conditions.

This Research Topic aims to showcase novel methodologies and applications of deep learning in processing and analyzing psychiatric data, paving the way for improved diagnostic and therapeutic strategies. By bringing together cutting-edge research that intersects deep learning, IoT, and high-dimensional data processing, it seeks to address the unique challenges that psychiatry faces today. This includes developing more sophisticated diagnostic tools, improving accuracy in psychiatric assessments, and enabling real-time patient monitoring.

To advance the field of psychiatry through the integration of these technologies, we welcome submissions that focus on, but are not limited to, the following themes:

- Application of high-dimensional sensing technologies like EEG and MRI in psychiatric assessments.
- IoT advancements for real-time mental health monitoring and intervention.
- Development of deep learning models to interpret complex psychiatric data.
- Innovations in multimodal data integration for a holistic approach to mental health diagnostics.
- Enhancements in signal processing for better noise reduction in psychiatric monitoring.
- Bridging machine learning and psychiatry for improved patient outcomes.
- Tailoring brain-computer interfaces to support psychiatric treatment plans.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Study Protocol
• Systematic Review

Keywords: deep learning, high-dimensional signal processing, IoT signal processing, non-linear data, big data, psychiatric, mental illness, machine learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.