A method to differentiate between ventricular fibrillation and asystole during chest compressions using...

In recent years, numerous adaptive filtering techniques have been developed to suppress the chest compression (CC) artifact for reliable analysis of the electrocardiogram (ECG) rhythm without CC interruption. Unfortunately, the result of rhythm diagnosis during CCs is still unsatisfactory in many studies. The misclassification between corrupted asystole (ASY) and corrupted ventricular fibrillation (VF) is generally regarded as one of the major reasons for the poor performance of reported methods. In order to improve the diagnosis of VF/ASY corrupted by CCs, a novel method combining a least mean-square (LMS) filter and an amplitude spectrum area (AMSA) analysis was developed based only on the analysis of the surface of the corrupted ECG episode. This method was tested on 253 VF and 160 ASY ECG samples from subjects who experienced cardiac arrest using a porcine model and was compared with six other algorithms. The validation results indicated that this method, which yielded a satisfactory result with a sensitivity of 93.3%, a specificity of 96.3% and an accuracy of 94.8%, is superior to the other reported techniques. After improvement using the human ECG records in real cardiopulmonary resuscitation (CPR) scenarios, the algorithm is promising for corrupted VF/ASY detection with no hardware alterations in clinical practice.