Cardiopulmonary resuscitation artefact suppression using a Kalman filter and the frequency of chest compressions as the reference signal

Direct evaluation of the effect of filtering the chest compression artifacts on the uninterrupted cardiopulmonary resuscitation time

“…The sensitivity figures are consistent with those published for short-duration OHCA records extracted from the same original study [8,9], but the specificity was lower (81% compared with 85%). This difference can be explained by the greater prevalence of AS in this study and the more constrained selection of short-duration records [8,9]. The performance of the filter-SAA combination, measured in terms of its sensitivity and The number of records is indicated in parenthesis.…”

“…After filtering Before filtering specificity after filtering, has not improved substantially over the years [6][7][8][9]. In fact, different filtering approaches have shown similar accuracy when tested with the same records and the same SAA [16,17].…”

“…Instead, state-of-the-art studies of filtering evaluate the performance of the filters using short-duration (10-20 seconds) OHCA records corrupted by CPR and perform a single rhythm analysis before and after the CPR artifact is filtered. The researchers then evaluate how the sensitivity and specificity of the shock advice algorithm (SAA) changes after filtering [6][7][8][9]. In this context, the performance of current filter-SAA combinations is still not good enough because the specificity after filtering is too low at below 90% in all studies [10].…”

A Reliable Method for Rhythm Analysis during Cardiopulmonary Resuscitation

“…The time-varying characteristics of the CPR artifact and its spectral overlap with both shockable and nonshockable cardiac arrest rhythms mandate the use of adaptive filters [8], which use reference signals to model the CPR artifact. Over the years, many solutions have been proposed, including Wiener filters [9], Matching Pursuit Algorithms [10], Recursive Least Squares [11], least mean squares (LMS) [12], or Kalman filters [13, 14]. Adaptive solutions using exclusively the ECG have also been explored [15, 16], but the results were poorer.…”

Using an inertial navigation algorithm and accelerometer to monitor chest compression depth during cardiopulmonary resuscitation

“…To the best of our knowledge, there was no previous attempt to use the Kalman filter for this application except for EKG artifact removal during CC [25] . Our study shows that it is possible to use it when coupled to a sliding average filter to reconstruct the motion of the sternum during CPR.…”