Khan Furrukh, Xiaoxi Jessie, Uehlin Andrew, Dalm Brian, Thomas Evan
Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA.
Image Guided Therapy Devices, Philips, CO, USA.
Heliyon. 2024 Jul 20;10(15):e34911. doi: 10.1016/j.heliyon.2024.e34911. eCollection 2024 Aug 15.
Patients' hand-drawn Archimedes spirals are widely used in the neurological community to grade tremors. These spirals are either drawn on paper and Xeroxed/scanned into digital images or digitizing tablets are used for the drawings. This process introduces artifacts such as variable widths of the drawn lines with varying pixel grey scale values. Xeroxing introduces additional artifacts resulting from paper misalignments. These artifacts and the presence of the reference spiral in the image complicate an automatic extraction of a mathematical spiral signal from the image.
We introduce a mathematical mapping that transforms the image pixels of the patient's hand-drawn spiral into a discrete signal that can be used for mathematical analysis.
A cohort of 18 hand-drawn spirals with various artifacts is used to validate our method.We extract the parameters of the discrete signals and show that the signals can be represented by truncating to as few as 150 parameters with a truncation RMS error of 6.26 % across the cohort. Using only 150 features makes machine learning a viable option for future applications. Furthermore, our method can be used to evaluate the frequency and the amplitude of the tremor.
In existing methods, the patient draws the spiral on a digitizing tablet, and features are extracted from this data for machine learning. We recognize that a vast majority of hospitals are still using the pencil-and-paper approach, and there is an abundance of ready-to-be-mined tremor-related data already stored as paper or digitized drawings. Our procedure is equally applicable to Xeroxed documents as well as files generated from digital tablets.
We have validated a new procedure requiring minimal user intervention to automatically extract a patient's hand-drawn spiral as a discrete mathematical signal from a scanned image or a file from a digital tablet.
患者手绘的阿基米德螺旋线在神经学界被广泛用于震颤分级。这些螺旋线要么画在纸上，然后复印/扫描成数字图像，要么使用数字化绘图板进行绘制。这个过程会引入一些伪影，比如绘制线条宽度变化且像素灰度值不同。复印会因纸张未对齐而引入额外的伪影。这些伪影以及图像中参考螺旋线的存在，使得从图像中自动提取数学螺旋信号变得复杂。
我们引入一种数学映射，将患者手绘螺旋线的图像像素转换为可用于数学分析的离散信号。
使用一组包含18条带有各种伪影的手绘螺旋线来验证我们的方法。我们提取离散信号的参数，并表明通过截断至仅150个参数，这些信号就能得到表示，整个队列的截断均方根误差为6.26%。仅使用150个特征使得机器学习在未来应用中成为一个可行的选择。此外，我们的方法可用于评估震颤的频率和幅度。
在现有方法中，患者在数字化绘图板上绘制螺旋线，并从这些数据中提取特征用于机器学习。我们认识到绝大多数医院仍在使用纸笔方法，并且有大量已存储为纸质或数字化绘图的现成的震颤相关数据有待挖掘。我们的程序同样适用于复印文档以及从数字绘图板生成的文件。
我们已经验证了一种新程序，该程序只需最少的用户干预，就能从扫描图像或数字绘图板文件中自动提取患者手绘螺旋线作为离散数学信号。
