Advancements in Wearable Digital Health Technology: A Review of Epilepsy Management

Ahuja, Abhinav; Agrawal, Sachin; Acharya, Sourya; Batra, Nitesh; Daiya, Varun

doi:10.7759/cureus.57037

Cited by 3 publications

References 35 publications

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Detection of seizure clustering using ultralong-term subcutaneous EEG monitoring

Novitskaya,

Hirsch,

Schulze-Bonhage

2024

Clin Epileptol

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Epileptic seizures usually occur unpredictably yet not necessarily at random times. Cyclical patterns of seizure recurrence have been broadly described. Long-term seizure documentation may reveal individual cyclical seizure patterns; however, seizure reporting by patients or their caregivers is often inaccurate. Modern technologies for at-home monitoring of epileptic seizures enable continuous EEG recording for objective seizure counts over long-term timescales. Here, we present the clinical case of a patient with temporal lobe epilepsy due to hippocampal sclerosis who underwent ultralong-term subcutaneous EEG recording over a 1-year period, which revealed the occurrence of seizure clusters at three- to four-weekly intervals. The case demonstrates the possibility of detection of subject-specific seizure timing using a minimally invasive subcutaneous EEG recorder for ultralong-term at-home seizure monitoring. Information about individual seizure timing may contribute to a more personalized chronotherapeutic treatment approach in which antiseizure medication can be maintained at low doses during the low-risk periods and increased briefly before the anticipated seizures.

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Detection of seizure clustering using ultralong-term subcutaneous EEG monitoring

Novitskaya,

Hirsch,

Schulze-Bonhage

2024

Clin Epileptol

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Multiresolution Evaluation of Contourlet Transform for the Diagnosis of Skin Cancer

Sikkander,

Lakshmi,

Theivanathan

et al. 2024

Preprint

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The successful treatment of skin cancer and better patient outcomes depend on an early and precise diagnosis.This work offers a multiresolution assessment of the contourlet transform for the diagnosis of skin cancer, utilizing its capacity to catch fine features in images at many scales and orientations. The contourlet transform is applied to dermoscopic images to enhance feature extraction, providing a more comprehensive representation of skin lesions compared to traditional methods. The proposed method involves preprocessing dermoscopic images to improve clarity and reduce noise, followed by the application of the contourlet transform to decompose the images into various frequency bands. These decomposed images are then analyzed to extract relevant textural and structural features, which are subsequently used to train a machine learning classifier. A collection of annotated skin lesion photos is used for performance evaluation, and the outcomes are compared with state-of-the-art methods currently in use. The efficacy of the suggested method is evaluated using metrics including sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve. The findings show that the contourlet transform-based approach performs better than traditional methods in capturing important characteristics of skin lesions, improving the ability to distinguish benign from malignant lesions and improving diagnostic accuracy. The contourlet transform is a formidable tool for the multiresolution analysis of skin cancer images, according to the study's conclusion, and it has a lot of promise for enhancing dermatology computer-aided diagnosis systems.

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Advancements in Wearable Health Monitoring - Analyzing the Developments of Wearable Sensors and Machine Learning for Epileptic Seizure Detection to improve Athletic Performance

Dhir

2024

International Journal of Innovative Science and Research Techno

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Wearable technology (WT) is a revolution in real-time data analytics and sports performance tracking. Both new and professional athletes depend on wearable technology to improve their competitive outcomes and training efficiency. However, further studies are needed to gain complete understanding to optimize their full potential in sports. A warning before the onset of seizure is important to improve quality of life (QoL) of athletes who have epilepsy. There is a need to evaluate the feasibility of wearable sensors to predict seizures with machine learning (ML). Epilepsy poses different challenges to manage and monitor because of unpredictable seizures. Wearable devices provide real-time data collection and constant monitoring to provide insights to trends and patterns related to seizure. Wearable technology is helpful to manage seizure as it allows early prediction, detection, and personalized intervention to empower healthcare providers and patients. This study explores latest advancements in wearable sensors designed for managing epilepsy. The findings of this study has highlighted the importance of wearable devices to improve accuracy in seizure detection, improve patient health with real-time monitoring, and promote data-based decision-making. However, this study recommends further research to validate reliability and accuracy of those devices in different clinical settings and populations. Combined efforts are needed among clinicians, researchers, patients, and technology developers to drive advancements and innovation in wearable technology for managing epilepsy, ultimately improving quality of life and outcomes for people with this neurological disorder.

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Advancements in Wearable Digital Health Technology: A Review of Epilepsy Management

Cited by 3 publications

References 35 publications

Detection of seizure clustering using ultralong-term subcutaneous EEG monitoring

Detection of seizure clustering using ultralong-term subcutaneous EEG monitoring

Multiresolution Evaluation of Contourlet Transform for the Diagnosis of Skin Cancer

Advancements in Wearable Health Monitoring - Analyzing the Developments of Wearable Sensors and Machine Learning for Epileptic Seizure Detection to improve Athletic Performance

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