Atrial Fibrillation Detection During Sepsis: Study on MIMIC III ICU Data

Abstract

Sepsis is defined by life-threatening organ dysfunction during infection and is one of the leading causes of critical illness. During sepsis, there is high risk that new-onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. As a result, computer aided automated and reliable detection of new-onset AF during sepsis is crucial, especially for the critically ill patients in the intensive care unit (ICU). In this paper, a novel automated and robust two-step algorithm to detect AF from ICU patients using electrocardiogram (ECG) signals is presented. First, several statistical parameters including root mean square of successive differences, Shannon entropy, and sample entropy were calculated from the heart rate for the screening of possible AF segments. Next, Poincaré plot-based features along with P-wave characteristics were used to reduce false positive detection of AF, caused by the premature atrial and ventricular beats. A subset of the Medical Information Mart for Intensive Care (MIMIC) III database containing 198 subjects was used in this study. During the training and validation phases, both the simple thresholding as well as machine learning classifiers achieved very high segment-wise AF classification performance. Finally, we tested the performance of our proposed algorithm using two independent test data sets and compared the performance with two state-of-the-art methods. The algorithm achieved an overall 100% sensitivity, 98% specificity, 98.99% accuracy, 98% positive predictive value, and 100% negative predictive value on the subject-wise AF detection, thus showing the efficacy of our proposed algorithm in critically ill sepsis patients. The annotations of the data have been made publicly available for other investigators.

Fig. 1.

(a) Sample 30-second NSR ECG recording and (b) the corresponding HR; (c) sample 30-second AF ECG segment and (d) the corresponding HR. The QRS peaks are denoted by black circles.

Fig. 2.

Scatter plots generated from the AF and NSR segments of the training data: (a) RMSSD vs. sample entropy and (b) Shannon vs. sample entropy.

Fig. 3.

Sample ECG segment with PAC rhythms; (b) corresponding HR generated from Fig. 3 (a).

Fig. 4.

Sample Poincaré plots generated from 2-minute (a) NSR, (b) PAC and (c) AF segments.

Fig. 5.

(a) P-waves detected from a sample PAC (non-AF) ECG segment. (b) Scatter plot of P_samp and PR_ratio obtained from AF and NSR segments.

Fig. 6.

(a) Sample 10-second PAC (non-AF) ECG segment with low PR_ratio. (b) Sample 10-second PAC (non-AF) ECG segment with high PR_ratio.

Fig. 7.

Flow chart of the proposed AF vs non-AF discrimination algorithm.

Fig. 8.

Number of false positive AF detections with different durations of consecutive segments, for three methods.