Assessing Upper Limb Motor Function in the Immediate Post-Stroke Perioud Using Accelerometry

• URL: http://arxiv.org/abs/2311.04226v1
• Date: Wed, 1 Nov 2023 18:43:20 GMT
• Title: Assessing Upper Limb Motor Function in the Immediate Post-Stroke Perioud Using Accelerometry
• Authors: Mackenzie Wallich, Kenneth Lai, and Svetlana Yanushkevich
• Abstract summary: The objective of this paper is to determine whether accelerometry-derived measurements can also be used to monitor and rapidly detect sudden changes in upper limb motor function in stroke patients. Six binary classification models were created by training on variable data window times of paretic upper limb accelerometer feature data. The classification models yielded Area Under the Curve (AUC) scores that ranged from 0.72 to 0.82 for 15-minute data windows to 0.77 to 0.94 for 120-minute data windows.
• Score: 0.6390468088226495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accelerometry has been extensively studied as an objective means of measuring upper limb function in patients post-stroke. The objective of this paper is to determine whether the accelerometry-derived measurements frequently used in more long-term rehabilitation studies can also be used to monitor and rapidly detect sudden changes in upper limb motor function in more recently hospitalized stroke patients. Six binary classification models were created by training on variable data window times of paretic upper limb accelerometer feature data. The models were assessed on their effectiveness for differentiating new input data into two classes: severe or moderately severe motor function. The classification models yielded Area Under the Curve (AUC) scores that ranged from 0.72 to 0.82 for 15-minute data windows to 0.77 to 0.94 for 120-minute data windows. These results served as a preliminary assessment and a basis on which to further investigate the efficacy of using accelerometry and machine learning to alert healthcare professionals to rapid changes in motor function in the days immediately following a stroke.

Related papers

• REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates [54.96885726053036]
  This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data. Our model demonstrates high accuracy in both seizure detection and classification tasks.
  arXiv  Detail & Related papers  (2024-06-03T16:30:19Z)
• The Potential of Wearable Sensors for Assessing Patient Acuity in Intensive Care Unit (ICU) [12.359907390320453]
  Acuity assessments are vital in critical care settings to provide timely interventions and fair resource allocation. Traditional acuity scores do not incorporate granular information such as patients' mobility level, which can indicate recovery or deterioration in the ICU. In this study, we evaluated the impact of integrating mobility data collected from wrist-worn accelerometers with clinical data obtained from EHR for developing an AI-driven acuity assessment score.
  arXiv  Detail & Related papers  (2023-11-03T21:52:05Z)
• A Federated Learning Framework for Stenosis Detection [70.27581181445329]
  This study explores the use of Federated Learning (FL) for stenosis detection in coronary angiography images (CA) Two heterogeneous datasets from two institutions were considered: dataset 1 includes 1219 images from 200 patients, which we acquired at the Ospedale Riuniti of Ancona (Italy) dataset 2 includes 7492 sequential images from 90 patients from a previous study available in the literature.
  arXiv  Detail & Related papers  (2023-10-30T11:13:40Z)
• Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach [4.826264675070211]
  This paper proposes a novel calibration method for wearable accelerometers. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction.
  arXiv  Detail & Related papers  (2023-07-10T19:25:45Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• MAPPING: Model Average with Post-processing for Stroke Lesion Segmentation [57.336056469276585]
  We present our stroke lesion segmentation model based on nnU-Net framework, and apply it to the Anatomical Tracings of Lesions After Stroke dataset. Our method took the first place in the 2022 MICCAI ATLAS Challenge with an average Dice score of 0.6667, Lesion-wise F1 score of 0.5643, Simple Lesion Count score of 4.5367, and Volume Difference score of 8804.9102.
  arXiv  Detail & Related papers  (2022-11-11T14:17:04Z)
• Shape Analysis for Pediatric Upper Body Motor Function Assessment [1.7434874566844876]
  Neuromuscular disorders, such as Spinal Muscular Atrophy (SMA) and Duchenne Muscular Dystrophy (DMD), cause progressive muscular degeneration and loss of motor function for 1 in 6,000 children. Traditional upper limb motor function assessments do not quantitatively measure patient-performed motions. This paper uses curve registration and shape analysis to temporally align trajectories while simultaneously extracting a mean reference shape.
  arXiv  Detail & Related papers  (2022-09-10T17:02:31Z)
• Automated Mobility Context Detection with Inertial Signals [7.71058263701836]
  The primary goal of this paper is the investigation of context detection for remote monitoring of daily motor functions. We aim to understand whether inertial signals sampled with wearable accelerometers, provide reliable information to classify gait-related activities as either indoor or outdoor.
  arXiv  Detail & Related papers  (2022-05-16T09:34:43Z)
• 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)
• Automated Stroke Rehabilitation Assessment using Wearable Accelerometers in Free-Living Environments [13.850999550050428]
  Traditional stroke rehabilitation assessment methods can be subjective and expensive. We developed an automated system that can predict the assessment score in an objective manner. Comprehensive experiments were conducted to evaluate our system on both acute and chronic patients.
  arXiv  Detail & Related papers  (2020-09-17T02:10:48Z)
• Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation [51.72616167073565]
  We propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation. We predict and fuse a pyramid of motion fields from multiple scales of feature representations to generate a more refined motion field. We then use a novel cyclic teacher-student training strategy to make the inference end-to-end and further improve the tracking performance.
  arXiv  Detail & Related papers  (2020-06-28T21:03:19Z)

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.