A telehealth system framework for assessing knee-joint conditions using vibroarthrographic signals
A telehealth system framework for assessing knee-joint conditions using vibroarthrographic signals
复制标题
使用振动关节信号评估膝关节状况的远程医疗系统框架
DOI：
10.1016/j.bspc.2019.101580
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
S. Krishnan
中科院分区：
文献类型：
--
作者：
Y. Athavale;S. Krishnan
忙碌的生活方式，加上有时在食物和运动的选择不足，导致被诊断为关节生成障碍的人数增加。此外，大量从事专门职业（如军队）或体育运动的人患有与关节有关的损伤，从而导致手术置换，在某些严重情况下导致残疾。检测关节疾病或残疾的标准方法包括关节镜检查和关节造影。振动关节造影（VAG）是一种非侵入性技术，其中专家听取使用活动记录仪记录的关节运动产生的声音。在这项研究中，我们提出了一个系统，该系统编码，分析和分割基于活动记录的VAG数据，用于评估软骨退化的严重程度，并突出显示在肢体运动过程中引起噼啪声的活动实例。所提出的系统通过在源处提供有效的数据压缩和分析来封装IoMT（医疗物联网）要求。使用来自89名参与者的体动记录数据集，我们的实验表明，该系统能够将体动记录数据压缩为每个样本3位，从而将信号大小减少约88%，而不会丢失任何重要的肢体运动信息。这已经使用来自具有健康和不健康膝关节的参与者的3位量化VAG数据的简单模式分类来进一步验证。该方法产生了84.6%的识别准确率，相比之下，原始VAG数据产生了80%的准确结果。此外，系统中提出的自适应分割方案通过从每个VAG信号中正确识别90%的感兴趣段来利用3位编码。
Hectic lifestyle coupled sometimes with deficient choices in food and exercise, has led to an increase in the number of individuals being diagnosed with joint generation disorders. In addition, a substantial number of individuals in specialized occupations (such as army) or sports suffer from joint related injuries, thereby leading to surgical replacements and in some severe cases as disability. Standard methods of detecting joint disorders or disabilities include arthroscopy and arthrogram. Vibroarthrography (VAG) is a non-invasive technique wherein the specialist listens to sounds generated from joint movements recorded using actigraphs. In this study, we propose a system which encodes, analyses and segments actigraphy-based VAG data for assessing the severity of cartilage degeneration, and highlighting instances of activity which causes crackling sounds during limb movements. The proposed system encapsulates IoMT (Internet of Medical Things) requirements by providing efficient data compression and analysis at the source. Using an actigraphy dataset from 89 participants, our experiments yielded that the system is able to compress the actigraphy data into 3-bits per sample, thereby reducing the signal size by about 88%, without losing any vital limb movement information. This has been further validated using a simple pattern classification of 3-bit quantized VAG data from participants with healthy and unhealthy knee joints. The method, which yielded a recognition accuracy of 84.6%, as compared to raw VAG data which yielded 80% accurate results. In addition, the proposed adaptive segmentation scheme in the system, leverages the 3-bit encoding by correctly identifying 90% of the segments of interest from each VAG signal.
影响因子：
5.6
作者：
Maglione, Jeanne E.;Liu, Lianqi;Ancoli-Israel, Sonia
通讯作者：
Ancoli-Israel, Sonia
影响因子：
3.3
作者：
Zhang Y;Jordan JM
通讯作者：
Jordan JM