Identification of ECG signal pattern changes to reduce the incidence of Ventricular Tachycardia false alarms

Abstract

The paper focuses on the reduction of the false alarms in the Intensive Care Units (ICU). Five alarm types were analyzed in this study: Asystole, Extreme Bradycardia, Ex- treme Tachycardia, Ventricular Tachycardia and Ventricu- lar Flutter/Fibrillation. Most of the analyzed alarm types rely on the quality of the heart rate estimation. The false alarm reduction algorithms analyzed in this paper use the quality estimate of the arterial blood pressure signal from which the heart rate is estimated and additionally the re- sults of heart beat detection in two ECG signals are an- alyzed before making the final decision about the true or false alarm type. The most attention in this paper is focused on the cor- rect detection of Ventricular Tachycardia alarms. The de- cision about the true or false alarm is made according to RR interval variation and changes of QRS complex shape features. A subset of sample entries data of the Physionet/CinC Challenge 2015 is used to test the proposed algorithm modifications. The false alarm detection according to the RR interval variation gave 49% TPR, 49% TNR (score 34.82) for the Phase I Entries data set and 46% TPR, 51% TNR (score 34.59) for the Phase II Entries data set. The VT alarm detection algorithm based on the features related to the the ECG waveform shape has increased the VT score for Phase I Entries data set to 41.98.