Exploring Cutting-Edge Developments in Deep Learning for Biomedical Signal Processing

Biomedical condition monitoring devices are progressing quickly by incorporating cost-effective and non-invasive sensors to track vital signs, record medical circumstances, and deliver meaningful responses. These sophisticated innovations rely on breakthrough technology to provide intelligent platfo...

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Published in:International journal of advanced computer science & applications Vol. 15; no. 5
Main Authors: Zhu, Yukun, Zhang, Haiyan, Liu, Bing, Dou, Junyan
Format: Journal Article
Language:English
Published: West Yorkshire Science and Information (SAI) Organization Limited 2024
Subjects:
Biomedical data
Condition monitoring
Deep learning
Electrocardiography
Health services
Innovations
Medical electronics
Real time
Signal processing
Telemedicine
ISSN:2158-107X, 2156-5570
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Abstract Biomedical condition monitoring devices are progressing quickly by incorporating cost-effective and non-invasive sensors to track vital signs, record medical circumstances, and deliver meaningful responses. These sophisticated innovations rely on breakthrough technology to provide intelligent platforms for health monitoring, quick illness recognition, and precise treatment. Biomedical signal processing determines patterns of signals and serves as the backbone for reliable applications, medical diagnostics, and research. Deep Learning (DL) methods have brought significant innovation in biomedical signal processing, leading to the transformation of the health sector and medical diagnostics. This article covers an entire range of technical innovations evolved for DL-based biomedical signal processing where different modalities have been considered, including Electrocardiography (ECG), Electromyography (EMG), and Electroencephalography (EEG). A vast amount of biomedical data in various forms is available, and DL concepts are required to extract and model this data in order to identify hidden complex patterns that can be utilized to improve the diagnosis, prognosis, and personalized treatment of diseases in an individual. The nature of this developing topic certainly gives rise to a number of challenges. First, the application of sensitive and noisy time series data requires truly robust models. Second, many inferences made at the bedside must have interpretability by design. Third, the field will require that processing be performed in real-time if used for therapeutic interventions. We systematically evaluate these challenges and highlight areas where continued research is needed. The general expansion of DL technologies into the biomedical domain gives rise to novel concerns about accountability and transparency of algorithmic decision-making, a subject which we briefly touch upon as well.
AbstractList Biomedical condition monitoring devices are progressing quickly by incorporating cost-effective and non-invasive sensors to track vital signs, record medical circumstances, and deliver meaningful responses. These sophisticated innovations rely on breakthrough technology to provide intelligent platforms for health monitoring, quick illness recognition, and precise treatment. Biomedical signal processing determines patterns of signals and serves as the backbone for reliable applications, medical diagnostics, and research. Deep Learning (DL) methods have brought significant innovation in biomedical signal processing, leading to the transformation of the health sector and medical diagnostics. This article covers an entire range of technical innovations evolved for DL-based biomedical signal processing where different modalities have been considered, including Electrocardiography (ECG), Electromyography (EMG), and Electroencephalography (EEG). A vast amount of biomedical data in various forms is available, and DL concepts are required to extract and model this data in order to identify hidden complex patterns that can be utilized to improve the diagnosis, prognosis, and personalized treatment of diseases in an individual. The nature of this developing topic certainly gives rise to a number of challenges. First, the application of sensitive and noisy time series data requires truly robust models. Second, many inferences made at the bedside must have interpretability by design. Third, the field will require that processing be performed in real-time if used for therapeutic interventions. We systematically evaluate these challenges and highlight areas where continued research is needed. The general expansion of DL technologies into the biomedical domain gives rise to novel concerns about accountability and transparency of algorithmic decision-making, a subject which we briefly touch upon as well.
Author Liu, Bing
Zhang, Haiyan
Dou, Junyan
Zhu, Yukun
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SubjectTerms Biomedical data
Condition monitoring
Deep learning
Electrocardiography
Health services
Innovations
Medical electronics
Real time
Signal processing
Telemedicine
Title Exploring Cutting-Edge Developments in Deep Learning for Biomedical Signal Processing
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