Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

< Back to Article

Figure 1.

Scheme of the LMS adaptive filtering to separate respiratory influences from the tachogram.

More »

Figure 1 Expand

Figure 2.

Block diagram of the simulation study.

More »

Figure 2 Expand

Figure 3.

Example of the simulation study of a typical subject using the ARMAX model in step 1 and 2.

Separation of the tachogram is performed using OSP. The signals are shown in both time (only 120 s are shown here) and frequency domain. From top to bottom: , , , . In dashed black, the estimated components using OSP are displayed.

More »

Figure 3 Expand

Figure 4.

Boxplots of the normalized root-mean-squared errors (NRMSE) between and (left), and between and (right) obtained using the simulation study.

Outliers are not shown.

More »

Figure 4 Expand

Figure 5.

Boxplots of the squared errors (SE) in LF power (top) and HF power (bottom) between and (left), and between and (right) obtained using the simulation study.

Outliers are not shown.

More »

Figure 5 Expand

Figure 6.

Block diagram of the stability study.

More »

Figure 6 Expand

Figure 7.

Boxplots of the normalized root mean-squared errors (NRMSE) (left), and the squared errors (SE) (right) in LF power (top) and HF power (bottom) between and obtained using the stability study.

Outliers are not shown.

More »

Figure 7 Expand

Figure 8.

Example of stability study using orthogonal subspace projection.

The residual signal of the full 6-minute period (, thick grey) and the three 2-minute segments (, dashed black) are shown.

More »

Figure 8 Expand

Figure 9.

Mean ROC of all classifiers.

More »

Figure 9 Expand