Real-time noise cancellation with deep learning

Porr, Bernd; Daryanavard, Sama; Bohollo, Lucía Muñoz; Cowan, Henry; Dahiya, Ravinder

doi:10.1371/journal.pone.0277974

Cited by 10 publications

(7 citation statements)

References 58 publications

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“…Another common challenge in biosensing is the existence of background noise that can mask the signal from the target analyte. Adaptive filtering techniques, such as those using neural networks, can distinguish between noise and the signal of interest with high accuracy [ 169 , 170 ]. These networks are trained to recognize the patterns associated with noise, allowing them to be filtered out without risking the elimination of target signal.…”

Section: Artificial Intelligence In Biosensingmentioning

confidence: 99%

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

Flynn,

Chang

2024

Diagnostics

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The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

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Section: Artificial Intelligence In Biosensingmentioning

confidence: 99%

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

Flynn,

Chang

2024

Diagnostics

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“…Thus, it constitutes a complex challenge that can be solved by the application of AI. 25 After denoising and applying frequency filters, the resultant EMG signal can be considered a raw EMG signal that is data-laden and ready for AI processing. There is a panoply of available techniques to extract meaningful features from raw signals, allowing for a condensed signal, input decongestion, and optimized processing time for AI models.…”

Section: Emg Signal Preprocessingmentioning

confidence: 99%

“…This type of artifact is more variable and difficult to ascertain using simple mathematical formulas. Thus, it constitutes a complex challenge that can be solved by the application of AI 25 …”

Section: Ai In Edx Medicinementioning

confidence: 99%

The role of artificial intelligence in electrodiagnostic and neuromuscular medicine:Currentstate and future directions

Taha,

Morren

2023

Muscle and Nerve

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The rapid advancements in artificial intelligence (AI), including machine learning (ML), and deep learning (DL) have ushered in a new era of technological breakthroughs in healthcare. These technologies are revolutionizing the way we utilize medical data, enabling improved disease classification, more precise diagnoses, better treatment selection, therapeutic monitoring, and highly accurate prognostication. Different ML and DL models have been used to distinguish between electromyography signals in normal individuals and those with amyotrophic lateral sclerosis and myopathy, with accuracy ranging from 67% to 99.5%. DL models have also been successfully applied in neuromuscular ultrasound, with the use of segmentation techniques achieving diagnostic accuracy of at least 90% for nerve entrapment disorders, and 87% for inflammatory myopathies. Other successful AI applications include prediction of treatment response, and prognostication including prediction of intensive care unit admissions for patients with myasthenia gravis. Despite these remarkable strides, significant knowledge, attitude, and practice gaps persist, including within the field of electrodiagnostic and neuromuscular medicine. In this narrative review, we highlight the fundamental principles of AI and draw parallels with the intricacies of human brain networks. Specifically, we explore the immense potential that AI holds for applications in electrodiagnostic studies, neuromuscular ultrasound, and other aspects of neuromuscular medicine. While there are exciting possibilities for the future, it is essential to acknowledge and understand the limitations of AI and take proactive steps to mitigate these challenges. This collective endeavor holds immense potential for the advancement of healthcare through the strategic and responsible integration of AI technologies.

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“…We are now describing how this can be done practically and is also an instructional example for other datasets. The corresponding Python code is available on GitHub [13].…”

Section: Bci-wall Calculationmentioning

confidence: 99%

BCI-Walls: A robust methodology to predict if conscious EEG changes can be detected in the presence of artefacts

Porr,

Bohollo

2023

PLoS ONE

Self Cite

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Brain computer interfaces (BCI) depend on reliable realtime detection of conscious EEG changes for example to control a video game. However, scalp recordings are contaminated with non-stationary noise, such as facial muscle activity and eye movements. This interferes with the detection process making it potentially unreliable or even impossible. We have developed a new methodology which provides a hard and measurable criterion if conscious EEG changes can be detected in the presence of non-stationary noise by requiring the signal-to-noise ratio of a scalp recording to be greater than the SNR-wall which in turn is based on the highest and lowest noise variances of the recording. As an instructional example, we have recorded signals from the central electrode Cz during eight different activities causing non-stationary noise such as playing a video game or reading out loud. The results show that facial muscle activity and eye-movements have a strong impact on the detectability of EEG and that minimising both eye-movement artefacts and muscle noise is essential to be able to detect conscious EEG changes.

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Real-time noise cancellation with deep learning

Cited by 10 publications

References 58 publications

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

The role of artificial intelligence in electrodiagnostic and neuromuscular medicine:Currentstate and future directions

BCI-Walls: A robust methodology to predict if conscious EEG changes can be detected in the presence of artefacts

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