Sensors in Health Disease Detection Based on Speech Signals
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: closed (20 July 2024) | Viewed by 2659
Special Issue Editors
Interests: blind source separation; independent component analysis; biomedical signal processing; human computer interaction; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Interests: cybersecurity for Internet of Things (IoT) applications; IoT fog analytics for real-time ICT applications (augmented reality); smart cities and real-time digital infrastructures (smart traffics and digital twins)
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Special Issue Information
Dear Colleagues,
Recent advances in modern sensors and signal processing techniques in medicine have improved the accuracy and reliability of medical diagnoses. Today, biomedical signal analysis, especially speech signals, is becoming one of biology and medicine's most crucial disease detection and interpretation methods.
Speech signals can be a valuable and objective tool supporting the diagnosis of many neurodegenerative diseases. The basic feasibility of speech-based disease detection or symptom severity prediction can already be demonstrated for a broad spectrum of medical conditions ranging from acute to chronic respiratory diseases.
New real-time methods are needed to improve the medical prognosis of diseases and shorten the time required for diagnosis. Speech can now be used as a digital biomarker for disease detection, which has advantages including easier diagnosis access, symptom monitoring, cost savings, and higher diagnostic precision.
This Special Issue aims to present a complete range of proven and new methods that play a leading role in improving biomedical diagnostics using speech signals. In this Special Issue, we want to build a bridge between different scientific disciplines and offer highly innovative researchers in various fields a platform to exchange research in this exciting and emerging field: Sensors in Health Disease Detection Based on Speech Signals.
Dr. Ganesh Naik
Prof. Dr. Tony Jan
Guest Editors
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Keywords
- biomedical applications
- deep learning
- digital biomarker
- healthcare
- machine learning
- speech technology
- non-invasive sensors
- articulation points
- voice analysis
- pathological speech conditions
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