What is the Role and Impact of the ECG in the Patient with Hyperkalemia?

Multiresolution mode decomposition for adaptive time series analysis

“…where O( ) comes from the approximation of L C functions using the values on grid points t h n , and the O( ) term in the residual function in (33). The following argument is similar to that for Lemma 3.3 in [31].…”

“…It is worth to exploring the application of MIMFs and MMD for fault detection in health data. For example, the detection of abnormal ECG waveforms is important to cardiac disease diagnosis [32,33]; the abnormality can be identified by detecting the sharp changes in the residual signal that are not correlated to the normal oscillation pattern. The second panel shows that the MIMF model can characterize time-varying shape functions, while the third panel shows that the MIMF model can capture the time-varying amplitude functions.…”

“…where the superscript (j) indicates the new target shape function series in the (j + 1)th iteration of Line 4 in Algorithm 3, and the multiresolution expansion coefficients have been absorbed in the shape functions. Since we adopt the Jacobi style iteration in the analysis, the residual function in (33) remains the same throughout the jth iteration, i.e. Line 9, 10, 15, and 16 were postponed until the end of the for-loop in Line 5.…”

“…The ECG waveform consists of three characteristic events (the P, QRS and T-wave as shown in Figure 4) associated with each beat. The detection of abnormal ECG waveforms is important to cardiac disease diagnosis [32,33] and the abnormality is the deviation of an observed ECG waveform to a standard ECG waveform (e.g. tall R peaks caused by possible thickening of heart muscle wall, wide QRS and wide S waves due to partial or complete right bundle branch block).…”