IEEE Trans Neural Syst Rehabil Eng. 2019 Oct;27(10):2077-2086. doi: 10.1109/TNSRE.2019.2938301. Epub 2019 Aug 29.
Freezing of gait (FOG) is an episodic gait disturbance affecting locomotion in Parkinson's disease. As a biomarker to detect FOG, the Freeze index (FI), which is defined as the ratio of the areas under power spectra in 'freeze' band and in 'locomotion' band, can negatively be affected by poor time and frequency resolution of time-frequency spectrum estimate when short-time Fourier transform (STFT) or Wavelet transform (WT) is used. In this study, a novel high-resolution parametric time-frequency spectral estimation method is proposed to improve the accuracy of FI. A time-varying autoregressive moving average model (TV-ARMA) is first identified where the time-varying parameters are estimated using an asymmetric basis function expansion method. The TV-ARMA model is then transformed into frequency domain to estimate the time-frequency spectrum and calculate the FI. Results evaluated on the Daphnet Freezing of Gait Dataset show that the new method improves the time and frequency resolutions of the time-frequency spectrum and the associate FI has better performance in the detection of FOG than its counterparts based on STFT and WT methods do. Moreover, FOGs can be predicted in advance of its occurrence in most cases using the new method.
冻结步态（FOG）是一种影响帕金森病患者运动的间歇性步态障碍。作为检测 FOG 的生物标志物，冻结指数（FI）定义为“冻结”频带和“运动”频带的功率谱面积之比，当使用短时傅里叶变换（STFT）或小波变换（WT）时，其会受到时间和频率分辨率差的负面影响。在这项研究中，提出了一种新的高分辨率参数时频谱估计方法来提高 FI 的准确性。首先，通过使用非对称基函数展开方法来识别时变自回归移动平均模型（TV-ARMA），然后将 TV-ARMA 模型转换到频域以估计时频谱并计算 FI。在 Daphnet 冻结步态数据集上的评估结果表明，该新方法提高了时频谱的时间和频率分辨率，并且与基于 STFT 和 WT 方法的 FI 相比，在检测 FOG 方面具有更好的性能。此外，在大多数情况下，使用新方法可以提前预测 FOG 的发生。
