Robust Methods to Detect Abnormal Initiation in the Gastric Slow Wave from Cutaneous Recordings

Agrusa, Anjulie S.; Allegra, Alexis B.; Kunkel, David C.; Coleman, Todd P.

doi:10.1109/embc44109.2020.9176634

Cited by 4 publications

(5 citation statements)

References 39 publications

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“…arrhythmia classification). Moreover, transfer learning was used for prediction of glucose levels [ 26 , 27 ], estimation of Parkinson’s disease severity [ 71 ], detection of cognitive impairment [ 33 ] and schizophrenia [ 52 ], and forecasting of infectious disease trends [ 28 ] and outbreaks [ 29 ], among other applications [ 72 – 82 ].…”

Section: Discussionmentioning

confidence: 99%

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Transfer learning for non-image data in clinical research: A scoping review

et al. 2022

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Background Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. Methods and findings We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%). Conclusions In this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research.

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Section: Discussionmentioning

confidence: 99%

“…prediction of glucose levels [26,27], estimation of Parkinson's disease severity [71], detection of cognitive impairment [33] and schizophrenia [52], and forecasting of infectious disease trends [28] and outbreaks [29], among other applications [72][73][74][75][76][77][78][79][80][81][82].…”

Section: Plos Digital Healthmentioning

confidence: 99%

Transfer learning for non-image data in clinical research: A scoping review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Moreover, transfer learning was used for prediction of glucose levels [26,27], estimation of Parkinson's disease severity [71], detection of cognitive impairment [33] and schizophrenia [52], and forecasting of infectious disease trends [28] and outbreaks [29], among other applications [72][73][74][75][76][77][78][79][80][81][82].…”

Section: Time Seriesmentioning

confidence: 99%

Transfer learning for non-image data in clinical research: a scoping review

Ebbehøj

Thunbo

Andersen

et al. 2021

Preprint

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Background Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. Methods and Findings We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%). Conclusions In this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research.

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“…We use simultaneous EEG and EGG to acquire the signals from the brain and the gut, and study whether we can observe coupling between the electrophysiological oscillations in both systems. While brain oscillations are widely studied in the delta to gamma band [1–40 Hz] range [ 4 , 9 , 10 , 11 , 12 , 13 , 14 ], the gastric oscillations undergo a relatively slow rhythm (0.05 Hz) sourced from the intrinsic pacemaker Cajal cells lining the digestive system tract [ 8 , 15 , 16 , 17 ]. Here, we simultaneously studied electrophysiological activity from both the brain and the gut during both the resting state and a working memory task, across both hunger and satiety states.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Gut-Brain Electrophysiology Shows Cognition and Satiety Specific Coupling

Balasubramani

Walke

Grennan

et al. 2022

Sensors

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Recent studies, using high resolution magnetoencephalography (MEG) and electrogastrography (EGG), have shown that during resting state, rhythmic gastric physiological signals are linked with cortical brain oscillations. Yet, gut-brain coupling has not been investigated with electroencephalography (EEG) during cognitive brain engagement or during hunger-related gut engagement. In this study in 14 young adults (7 females, mean ± SD age 25.71 ± 8.32 years), we study gut-brain coupling using simultaneous EEG and EGG during hunger and satiety states measured in separate visits, and compare responses both while resting as well as during a cognitively demanding working memory task. We find that EGG-EEG phase-amplitude coupling (PAC) differs based on both satiety state and cognitive effort, with greater PAC modulation observed in the resting state relative to working memory. We find a significant interaction between gut satiation levels and cognitive states in the left fronto-central brain region, with larger cognitive demand based differences in the hunger state. Furthermore, strength of PAC correlated with behavioral performance during the working memory task. Altogether, these results highlight the role of gut-brain interactions in cognition and demonstrate the feasibility of these recordings using scalable sensors.

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Robust Methods to Detect Abnormal Initiation in the Gastric Slow Wave from Cutaneous Recordings

Cited by 4 publications

References 39 publications

Transfer learning for non-image data in clinical research: A scoping review

Transfer learning for non-image data in clinical research: A scoping review

Transfer learning for non-image data in clinical research: a scoping review

Simultaneous Gut-Brain Electrophysiology Shows Cognition and Satiety Specific Coupling

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