Fig 1.
Schematic representation of the epidural stimulation unit (16-electrode array, IPG unit and wireless programmer) and its connections to the EMG recording system.
(The Motion Lab EMG System and EMG electrodes illustrations are from Motion Lab System Inc. Manual).
Fig 2.
Block diagram of the proposed framework for visualization and activation detection of evoked potentials induced by scES.
Fig 3.
The steps for converting raw EMG signals into 2-D and 3-D images.
(A) Raw EMG signal, (B) Signal segmentation using stimulation time intervals, (C) Overlaying all the segments and building the 3-D graph where X-axis is the evoked potentials duration (ms), the Y-axis is the stimulation voltage (V), and the Z-axis is the amplitude of the signals (μV) and (D) Converting the 3-D graphs into 2-D images using Colormap.
Fig 4.
(A) Applying GGMRF method to 2D image (B) An example of evoked potential before (black) and after (red) applying GGMRF method.
Fig 5.
Calculation steps for activation detection algorithm.
(A) Sample evoked potential (one segment of the EMG signal), (B) Histogram of the sample evoked potential (black) and its estimated Gaussian distribution (red), (C) Comparing the Gaussian pdf of evoked potential signal (red) to pdf of background noise (black), (D) Plotting the calculated LLR for all segments of the EMG signal and detect the activation threshold (arrow).
Fig 6.
Selected feature parameters for EMG activation signal.
(A) Visual inspection: Number of peaks of the evoked potential, Activation onset and Latency, (B) Computer-based feature extraction of peak-to-peak amplitude (Vpp), Activation latency, Time interval between minimum and maximum values (Tpp) and Integrated EMG (summation of absolute values of all gray areas).
Fig 7.
Examples of converting 14 EMG signals into colormap images for voltage ramp-up and frequency ramp-up experiments.
(A) Raw EMG signals of 14 ploximal and distal left and right leg muscles during voltage ramp-up from 0.1 to 10 V. (B) Raw EMG signals of same muscles during frequency ramp-up from 2 to 60 Hz. (C) Colormap image shows the corresponding peak-to-peak amplitudes (μV) with respect to each muscle and stimulation voltage after stimulation threshold detection. The gray area is presenting the pre-threshold part of the experiment where no activation was induced. (D) Colormap image shows the corresponding integrated EMG values with respect to 14 muscles and stimulation frequencies.
Fig 8.
Boxplot representation of performace measurements for comparing automated activation detection method with the ground truth.
(A) Accuracy, (B) Sensitivity, (C) Specificity, (D) Dice Similarity.
Table 1.
Comparison of the total accuracy for the new automated activation detection method with the TKEO and SODM methods based on five-number-summary.
Fig 9.
Examples of recorded EMG signals with different SNR levels and the performance comparison between three activation detection methods.
(A) High SNR signal from right MH; (B) Medium SNR signal from right GL and (C) Low SNR signal from L GL. Detected activation windows for AGLR + MMGRF, AGLR and TKEO from left to right are shown as continues and dashed black lines. De-noised signal is shown in light red and manually detected activation window as dashed red line.
Fig 10.
Comparison of three activation detection methods on simulated EMG signals as a function of SNR(dB).
Table 2.
The runtime mean and RMSE of each steps of the proposed framework.