Cloud-Based Clinical Physiological Monitoring System for Disease Prediction

Hussain, Iqram; Park, Se Jin; Hossain, Md Azam

doi:10.1007/978-981-16-9480-6_25

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Recent cutting-edge technologies showed the potential to enhance activity recognition outcomes and healthcare delivery using the Internet of Things (IoT), wearable technology, digital twins, and big data [11,12]. Physiological signals were utilized as effective tools for the monitoring of health and the early diagnosis of disease in real time [6,[13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An Explainable EEG-Based Human Activity Recognition Model Using Machine-Learning Approach and LIME

Hussain,

Jany,

Boyer

et al. 2023

Sensors

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Electroencephalography (EEG) is a non-invasive method employed to discern human behaviors by monitoring the neurological responses during cognitive and motor tasks. Machine learning (ML) represents a promising tool for the recognition of human activities (HAR), and eXplainable artificial intelligence (XAI) can elucidate the role of EEG features in ML-based HAR models. The primary objective of this investigation is to investigate the feasibility of an EEG-based ML model for categorizing everyday activities, such as resting, motor, and cognitive tasks, and interpreting models clinically through XAI techniques to explicate the EEG features that contribute the most to different HAR states. The study involved an examination of 75 healthy individuals with no prior diagnosis of neurological disorders. EEG recordings were obtained during the resting state, as well as two motor control states (walking and working tasks), and a cognition state (reading task). Electrodes were placed in specific regions of the brain, including the frontal, central, temporal, and occipital lobes (Fz, C1, C2, T7, T8, Oz). Several ML models were trained using EEG data for activity recognition and LIME (Local Interpretable Model-Agnostic Explanations) was employed for interpreting clinically the most influential EEG spectral features in HAR models. The classification results of the HAR models, particularly the Random Forest and Gradient Boosting models, demonstrated outstanding performances in distinguishing the analyzed human activities. The ML models exhibited alignment with EEG spectral bands in the recognition of human activity, a finding supported by the XAI explanations. To sum up, incorporating eXplainable Artificial Intelligence (XAI) into Human Activity Recognition (HAR) studies may improve activity monitoring for patient recovery, motor imagery, the healthcare metaverse, and clinical virtual reality settings.

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Section: Introductionmentioning

confidence: 99%

An Explainable EEG-Based Human Activity Recognition Model Using Machine-Learning Approach and LIME

Hussain,

Jany,

Boyer

et al. 2023

Sensors

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Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal

Islam

Hussain

Rahman

et al. 2022

Sensors

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State-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence (AI) models, allowing for rapid and easy disease diagnosis. However, most AI models are considered “black boxes,” because there is no explanation for the decisions made by these models. Users may find it challenging to comprehend and interpret the results. Explainable AI (XAI) can explain the machine learning (ML) outputs and contribution of features in disease prediction models. Electroencephalography (EEG) is a potential predictive tool for understanding cortical impairment caused by an ischemic stroke and can be utilized for acute stroke prediction, neurologic prognosis, and post-stroke treatment. This study aims to utilize ML models to classify the ischemic stroke group and the healthy control group for acute stroke prediction in active states. Moreover, XAI tools (Eli5 and LIME) were utilized to explain the behavior of the model and determine the significant features that contribute to stroke prediction models. In this work, we studied 48 patients admitted to a hospital with acute ischemic stroke and 75 healthy adults who had no history of identified other neurological illnesses. EEG was obtained within three months following the onset of ischemic stroke symptoms using frontal, central, temporal, and occipital cortical electrodes (Fz, C1, T7, Oz). EEG data were collected in an active state (walking, working, and reading tasks). In the results of the ML approach, the Adaptive Gradient Boosting models showed around 80% accuracy for the classification of the control group and the stroke group. Eli5 and LIME were utilized to explain the behavior of the stroke prediction model and interpret the model locally around the prediction. The Eli5 and LIME interpretable models emphasized the spectral delta and theta features as local contributors to stroke prediction. From the findings of this explainable AI research, it is expected that the stroke-prediction XAI model will help with post-stroke treatment and recovery, as well as help healthcare professionals, make their diagnostic decisions more explainable.

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Cloud-Based Clinical Physiological Monitoring System for Disease Prediction

Cited by 2 publications

References 17 publications

An Explainable EEG-Based Human Activity Recognition Model Using Machine-Learning Approach and LIME

An Explainable EEG-Based Human Activity Recognition Model Using Machine-Learning Approach and LIME

Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal

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