Figure 5.

Practical considerations for implementing signal recovery via PARRM in real time

(A) Exact period estimations in samples over 1,012 recordings for P1 and P2 over 250 days since DBS implant.

(B) Median absolute percent error (MAPE) between the standard PARRM filtering approach (using past and future samples, and exact period estimation) and by using past samples only with an exact period estimation, past samples only with the maximum period across the 1,012 recordings, and past samples only with the minimum period across the 1,012 recordings.

(C) Comparison of averaged continuous wavelet transforms when filtered by using PARRM with past and future samples versus past samples alone.

(D) PARRM performance measured by relative root-mean-squared error (RRMSE) is dependent on the number of samples used to determine the period. Error bars show the spread.

(E) Heatmap of RRMSE as a function of period distance ($D_{\text{period}}$) and half window size ($N_{\text{bins}}$). Darker blue indicates superior PARRM performance. Orange point indicates the $D_{\text{period}}$ and $N_{\text{bins}}$ that were used for all analysis.

(F) Voltage-time LFP trace after PARRM filtering containing a jump in the period.

(G) LFP (blue) and concurrent EEG (red), aligned by using location of period jump identified in both recordings.