A multimodal approach to reduce false arrhythmia alarms in the intensive care unit

As part of the 2015 PhysioNet/CinC Challenge, this work aims at lowering the number of false alarms, which are a persistent concern in the intensive care unit. The multimodal database consists of 1250 life-threatening alarm recordings, each categorized as a bradycardia, tachycardia, asystole, ventricular tachycardia or ventricular flutter/fibrillation arrhythmia. Based on the quality of available signals, heart rate was either estimated from pulsatile waveforms (photoplethysmogram and/or arterial blood pressure) using an adaptive frequency tracking algorithm or computed from ECGs using an adaptive mathematical morphology approach. Furthermore, we introduced a supplementary measure based on the spectral purity of the ECGs to determine if a ventricular tachycardia or flutter/fibrillation arrhythmia has taken place. Finally, alarm veracity was determined based on a set of decision rules on heart rate and spectral purity values. Our method achieved overall scores of 76.11 and 85.04 on the real-time and retrospective subsets, respectively.

[1]  Jean-Marc Vesin,et al.  Multi-signal extension of adaptive frequency tracking algorithms , 2009, Signal Process..

[2]  Pablo Laguna,et al.  Bioelectrical Signal Processing in Cardiac and Neurological Applications , 2005 .

[3]  Jean-Marc Vesin,et al.  Adaptive Mathematical Morphology for QRS fiducial points detection in the ECG , 2014, Computing in Cardiology 2014.

[4]  Ho-En Liao,et al.  Two discrete oscillator based adaptive notch filters (OSC ANFs) for noisy sinusoids , 2005, IEEE Transactions on Signal Processing.

[5]  J. S. Barlow,et al.  Changes in EEG mean frequency and spectral purity during spontaneous alpha blocking. , 1990, Electroencephalography and clinical neurophysiology.

[6]  Mathieu Lemay,et al.  Photoplethysmography-based ambulatory heartbeat monitoring embedded into a dedicated bracelet , 2013, Computing in Cardiology 2013.

[7]  Bo Yang,et al.  Reducing false arrhythmia alarms in the ICU , 2015, 2015 Computing in Cardiology Conference (CinC).

[8]  G. Clifford,et al.  Suppress False Arrhythmia Alarms of ICU Monitors Using Heart Rate Estimation Based on Combined Arterial Blood Pressure and Ecg Analysis , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[9]  Qiao Li,et al.  The PhysioNet/Computing in Cardiology Challenge 2015: Reducing false arrhythmia alarms in the ICU , 2015, 2015 Computing in Cardiology Conference (CinC).

[10]  Mohammed Saeed,et al.  Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform , 2008, J. Biomed. Informatics.

[11]  Qiao Li,et al.  Ventricular Fibrillation and Tachycardia Classification Using a Machine Learning Approach , 2014, IEEE Transactions on Biomedical Engineering.

[12]  Yong Bai,et al.  False ventricular tachycardia alarm suppression in the ICU based on the discrete wavelet transform in the ECG signal. , 2014, Journal of electrocardiology.

[13]  A. Mäkivirta,et al.  The median filter as a preprocessor for a patient monitor limit alarm system in intensive care. , 1991, Computer methods and programs in biomedicine.

[14]  G. Clifford,et al.  Signal quality and data fusion for false alarm reduction in the intensive care unit. , 2012, Journal of electrocardiology.

[15]  Jean-Marc Vesin,et al.  Adaptive Tracking of EEG Frequency Components , 2009 .