Key result
Biomedical signal processing and machine learning technology can enable real-time monitoring for better chronic condition control and timely identification of hazardous occurrences.
Highlights the potential of machine learning in biomedical signal processing for real-time monitoring and chronic condition control.
Biomedical signal processing integrates the evaluation of health measures for the purpose of delivering significant factor diagnostic information [1]. Real-time monitoring features enabled by biomedical signal processing can lead to better chronic condition control and timely identification of hazardous occurrences. The use of a remote database for cloud computing in biomedical signal processing has significant implications for healthcare applications. To guarantee effective analysis, optimal data presentation, and product quality, innovative research and ideas need to be implemented.
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Sambas et al. (2024) reported an editorial. Machine learning technology for biomedical signal processing was evaluated. Biomedical signal processing and machine learning technology can enable real-time monitoring for better chronic condition control and timely identification of hazardous occurrences.
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