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

Buettner, Ricardo; Grimmeisen, Annika; Gotschlich, Anne

doi:10.24251/hicss.2020.396

Cited by 34 publications

(12 citation statements)

References 57 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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“…74]. In addition, we will report results on successfully applying our novel procedure to other schizophrenia data [75], and other diseases such as epilepsy [76,77] and sleep disorder [78,79].…”

Section: Future Workmentioning

confidence: 99%

Development of a Machine Learning Based Algorithm To Accurately Detect Schizophrenia based on One-minute EEG Recordings

Buettner¹,

Beil²,

Scholtz³

et al. 2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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While diagnosing schizophrenia by physicians based on patients' history and their overall mental health is inaccurate, we report on promising results using a novel, fast and reliable machine learning approach based on electroencephalography (EEG) recordings. We show that a fine granular division of EEG spectra in combination with the Random Forest classifier allows a distinction to be made between paranoid schizophrenic (ICD-10 F20.0) and nonschizophrenic persons with a very good balanced accuracy of 96.77 percent. We evaluate our approach on EEG data from an open neurological and psychiatric repository containing 499 one-minute recordings of n=28 participants (14 paranoid schizophrenic and 14 healthy controls). Since the fact that neither diagnostic tests nor biomarkers are available yet to diagnose paranoid schizophrenia, our approach paves the way to a quick and reliable diagnosis with a high accuracy. Furthermore, interesting insights about the most predictive subbands were gained by analyzing the electroencephalographic spectrum up to 100 Hz.

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“…• to triangulate objective and perceived user-oriented concepts [65,66,67] using physiological sensor data (i.e., electroencephalographic data [68,69,70,71] and spectra [72,73,74], electrocardiographic data [75,76], electrodermal activity [77], eye fixation [78,79,56], eye pupil diameter [80,81,53,82], facial expressions [83]), and, Page 569…”

Section: Future Workmentioning

confidence: 99%

Development of a Highly Precise Place Recognition Module for Effective Human-robot Interactions in Changing Lighting and Viewpoint Conditions

Baumgartl¹,

Buettner²

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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We present a highly precise and robust module for indoor place recognition, extending the work by Lemaignan et al. and Robert Jr. by giving the robot the ability to recognize its environment context. We developed a full end-to-end convolutional neural network architecture, using a pre-trained deep convolutional neural network and the explicit inductive bias transfer learning strategy. Experimental results based on the York University and Rzeszów University dataset show excellent performance values (over 94.75 and 97.95 percent accuracy) and a high level of robustness over changes in camera viewpoint and lighting conditions, outperforming current benchmarks. Furthermore, our architecture is 82.46 percent smaller than the current benchmark, making our module suitable for embedding into mobile robots and easily adoptable to other datasets without the need for heavy adjustments.

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

Cited by 34 publications

References 57 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

Development of a Machine Learning Based Algorithm To Accurately Detect Schizophrenia based on One-minute EEG Recordings

Development of a Highly Precise Place Recognition Module for Effective Human-robot Interactions in Changing Lighting and Viewpoint Conditions

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