An Artificial Intelligence Based Effective Diagnosis of Parkinson Disease Using EEG Signal

Al‐Khasawneh, Mahmoud Ahmad; Alzahrani, Abdulrahman; Alarood, Ala Abdulsalam

doi:10.1007/978-981-99-2154-6_14

Cited by 2 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…EMG signals are associated with the neuromuscular system, while EEG signals may be used to diagnose neurological conditions [27]. Hence, each signal type is related to a different medical specialty [28]. Moreover, not only is the potential of each signal type in isolation vast, but also, by combining the information from multiple sources, a comprehensive view of the patient's state can be achieved.…”

Section: Backgroundsmentioning

confidence: 99%

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

Zhu,

Zhang,

Liu

et al. 2024

ijacsa

View full text Add to dashboard Cite

Biomedical condition monitoring devices are progressing quickly by incorporating cost-effective and noninvasive 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.

show abstract

Section: Backgroundsmentioning

confidence: 99%