Machine Learning Based IoT Adaptive Architecture for Epilepsy Seizure Detection: Anatomy and Analysis

• URL: http://arxiv.org/abs/2305.19347v2
• Date: Tue, 5 Sep 2023 23:26:02 GMT
• Title: Machine Learning Based IoT Adaptive Architecture for Epilepsy Seizure Detection: Anatomy and Analysis
• Authors: Zag ElSayed, Murat Ozer, Nelly Elsayed, Ahmed Abdelgawad
• Abstract summary: A seizure tracking system is crucial for monitoring and evaluating epilepsy treatments. Caretaker seizure diaries are used in epilepsy care today, but clinical seizure monitoring may miss seizures. We propose a versal, affordable noninvasive based on a simple real-time k-Nearest-Neighbors (kNN) machine learning that can be customized and adapted to individual users in less than four seconds of training time.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A seizure tracking system is crucial for monitoring and evaluating epilepsy treatments. Caretaker seizure diaries are used in epilepsy care today, but clinical seizure monitoring may miss seizures. Monitoring devices that can be worn may be better tolerated and more suitable for long-term ambulatory use. Many techniques and methods are proposed for seizure detection; However, simplicity and affordability are key concepts for daily use while preserving the accuracy of the detection. In this study, we propose a versal, affordable noninvasive based on a simple real-time k-Nearest-Neighbors (kNN) machine learning that can be customized and adapted to individual users in less than four seconds of training time; the system was verified and validated using 500 subjects, with seizure detection data sampled at 178 Hz, the operated with a mean accuracy of (94.5%).

Related papers

• Energy-Efficient Seizure Detection Suitable for low-power Applications [0.5326090003728084]
  Epilepsy is the most common, chronic, neurological disease worldwide. Neuro implants can be used for effective treatment by suppressing an upcoming seizure upon detection. We present an energy-efficient seizure detection approach involving a TC-ResNet and time-series analysis.
  arXiv  Detail & Related papers  (2024-06-19T11:36:29Z)
• VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG [8.100646331930953]
  An accurate and efficient epileptic seizure onset detection can significantly benefit patients. Traditional diagnostic methods, primarily relying on electroencephalograms (EEGs), often result in cumbersome and non-portable solutions. We propose a novel Video-based Seizure detection model via a skeleton-basedtemporal Vision Graph neural network.
  arXiv  Detail & Related papers  (2023-11-24T15:07:29Z)
• Video object detection for privacy-preserving patient monitoring in intensive care [0.0]
  We propose a new method for exploiting information in the temporal succession of video frames. Our method outperforms a standard YOLOv5 baseline model by +1.7% mAP@.5 while also training over ten times faster on our proprietary dataset.
  arXiv  Detail & Related papers  (2023-06-26T11:52:22Z)
• Leveraging Unlabelled Data in Multiple-Instance Learning Problems for Improved Detection of Parkinsonian Tremor in Free-Living Conditions [80.88681952022479]
  We introduce a new method for combining semi-supervised with multiple-instance learning. We show that by leveraging the unlabelled data of 454 subjects we can achieve large performance gains in per-subject tremor detection.
  arXiv  Detail & Related papers  (2023-04-29T12:25:10Z)
• Knowledge-Distilled Graph Neural Networks for Personalized Epileptic Seizure Detection [43.905374104261014]
  We propose a novel knowledge distillation approach to transfer the knowledge from a sophisticated seizure detector (called the teacher) trained on data from the full set of electrodes to learn new detectors (called the student) They are both providing lightweight implementations and significantly reducing the number of electrodes needed for recording the EEG. Our experiments show that both knowledge-distillation and personalization play significant roles in improving performance of seizure detection, particularly for patients with scarce EEG data.
  arXiv  Detail & Related papers  (2023-04-03T15:37:40Z)
• Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones [75.23250968928578]
  We present a method for predicting the medication status of Parkinson's disease patients using the public mPower dataset. The proposed method shows promising results in predicting three medication statuses objectively.
  arXiv  Detail & Related papers  (2022-07-26T02:08:08Z)
• Reducing a complex two-sided smartwatch examination for Parkinson's Disease to an efficient one-sided examination preserving machine learning accuracy [63.20765930558542]
  We have recorded participants performing technology-based assessments in a prospective study to research Parkinson's Disease (PD) This study provided the largest PD sample size of two-hand synchronous smartwatch measurements.
  arXiv  Detail & Related papers  (2022-05-11T09:12:59Z)
• Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting [7.545072251531297]
  This work extensively compares state-of-the-art models and signal feature extractors in a real-time seizure detection framework suitable for real-world application. Our code is available at https://github.com/AITRICS/EEG_real_time_seizure_detection.
  arXiv  Detail & Related papers  (2022-01-21T16:53:32Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)
• Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification [80.00316465793702]
  We present a deep learning framework that enables robust modeling in challenging scenarios. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
  arXiv  Detail & Related papers  (2020-05-03T02:36:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.