Asking Both the User’s Heart and Its Owner: Empirical Evidence for Substance Dualism

Buettner, Ricardo; Bachus, Lars; Konzmann, Larissa; Prohaska, Sebastian

doi:10.1007/978-3-030-01087-4_30

Cited by 19 publications

(5 citation statements)

References 11 publications

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“…Furthermore, we will pursue to investigate how the integration of our automated CU approach into realworld Human-Computer-Interaction in multi-agentsettings [58,59] might influence the cognitive workload [60][61][62] and other user-oriented concepts [63][64][65] of the health personnel (e.g., surgeons and nurses). We aim to analyze this interaction using physiological sensor data (e.g., electroencephalographic data [66][67][68][69][70][71][72] and spectra [73][74][75][76][77][78], electrocardiographic data [79,80], electrodermal activity [81], eye fixation [82,83], eye pupil diameter [84,85], facial expressions [86]). We also plan to investigate levels of trust [87,88] and technology acceptance [89][90][91][92] of both clinical experts and patients towards the practical implementation of our model and confirm if the automated approach improves the coordination of doctors and treatments more efficiently.…”

Section: Future Workmentioning

confidence: 99%

High-Performance Detection of Corneal Ulceration Using Image Classification with Convolutional Neural Networks

Gross¹,

Breitenbach²,

Baumgartl³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Corneal Ulcer, also known as keratitis, represents the most frequently appearing symptom among corneal diseases, the second leading cause of ocular morbidity worldwide. Consequences such as irreversible eyesight damage or blindness require an innovative approach that enables a distinction to be made between patterns of different ulcer stages to lower the global burden of visual disability. This paper describes a Convolutional Neural Network-based image classification approach that allows the identification of different types of Corneal Ulcers based on fluorescein staining images. With a balanced accuracy of 92.73 percent, our results set a benchmark in distinguishing between general ulcer patterns. Our proposed method is robust against light reflections and allows automated extraction of meaningful features, manifesting a strong practical and theoretical relevance. By identifying Corneal Ulcers at an early stage, we aid reduction of aggravation by preventively applying and consequently tracking the efficacy of adapted medical treatment, which contributes to IT-based healthcare.

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Section: Future Workmentioning

confidence: 99%

High-Performance Detection of Corneal Ulceration Using Image Classification with Convolutional Neural Networks

Gross¹,

Breitenbach²,

Baumgartl³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Besides, in future work, we will triangulate EEG sensor data with other physiological sensor data [25,[53][54][55][56][57][58][59]. While we successfully evaluated our novel ML approach on several diseases [23,24,49,53,[60][61][62][63], addictions [64], stress [65] and personality traits [66], we will experimentally evaluate whether our approach is also robust under various conditions of a user's cognitive workload [67][68][69], concentration [70] and mindfulness [71,72] by physicians and patients in multi-agent settings [73][74][75][76].…”

Section: Future Workmentioning

confidence: 99%

Machine Learning Based Diagnostics of Developmental Coordination Disorder using Electroencephalographic Data

Buettner

Buechele²,

Grimmeisen³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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We report on promising results concerning the fast and accurate diagnosis of developmental coordination disorder (DCD) which heavily impacts the life of affected children with emotional and behavioral issues. Using a machine learning classifier on spectral data of electroencephalography (EEG) recordings and unfolding the traditional frequency bandwidth in a fine-graded equidistant 99-point spectrum we were able to reach an accuracy of over 99.35 percent having only one misclassification. Our machine learning work contributes to healthcare and information systems research. While current diagnostic methods in use are either complicated, time-consuming, or inaccurate, our automated machine-based approach is accurate and reliable. Our results also provide more insights into the relationship between DCD and brain activity which could stimulate future work in medicine.

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“…In addition, we will triangulate EEG data with other physiological sensors (i.e. electrocardiogram [48,49], electrodermal activity [50,51], eye fixation [52][53][54], eye pupil diameter [55][56][57][58]). Furthermore, we will experimentally evaluate whether our novel approach is also robust under various conditions of a user's cognitive workload [59][60][61] and related concepts [62][63][64], concentration [65], and mindfulness [66,67].…”

Section: Future Workmentioning

confidence: 99%

High-performance Diagnosis of Sleep Disorders: A Novel, Accurate and Fast Machine Learning Approach Using Electroencephalographic Data

Buettner¹,

Grimmeisen²,

Gotschlich³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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While diagnosing sleep disorders by physicians using electroencephalographic data is protracted and inaccurate, we report promising results from a novel, fast and reliable machine learning approach. Our approach only needs an electroencephalographic recording snippet of 10 minutes instead of eight hours to correctly classify the disorder with an accuracy of over 90 percent. The Rapid Eye Movement sleep behavior disorder can lead to secondary diseases like Parkinson or Dementia. Therefore, it is important to classify the disorder fast and with a high level of accuracy -which is now possible with our approach.

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Asking Both the User’s Heart and Its Owner: Empirical Evidence for Substance Dualism

Cited by 19 publications

References 11 publications

High-Performance Detection of Corneal Ulceration Using Image Classification with Convolutional Neural Networks

High-Performance Detection of Corneal Ulceration Using Image Classification with Convolutional Neural Networks

Machine Learning Based Diagnostics of Developmental Coordination Disorder using Electroencephalographic Data

High-performance Diagnosis of Sleep Disorders: A Novel, Accurate and Fast Machine Learning Approach Using Electroencephalographic Data

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