Cardiac arrest is the leading cause of deaths for thousands of people every year. Immediate treatment of cardiac arrest can help reducing cardiovascular mortality rates. One of the most common approaches for treatment of cardiac arrest is cardiopulmonary resuscitation (CPR) that provides chest compressions. Quality of chest compression is considered as one of key indicators for CPR performance assessment. In this study, we present an approach for CPR quality evaluation using ECG and thoracic impedance signals via a least mean square based adaptive filter. Then, CPR quality evaluation is performed based on analyzing the spectra and frequencies of input ECG and estimated CPR signals. The proposed approach is applied for a dataset including 526 segments from patients presenting with asystole. The results are then compared with those derived from the compression depth used as reference CPR signals. Experimental results show performance of the proposed approach for assessment of CPR quality.
