Reduction of false alarms in the intensive care unit using an optimized machine learning based approach

Abstract

This work attempts to reduce the number of false alarms generated by bedside monitors in the intensive care unit (ICU), as a majority of current alarms are false. In this study, we applied methods that can be categorized into three stages: signal processing, feature extraction, and optimized machine learning. At the stage of signal processing, we ensured that the heartbeats were properly annotated. During feature extraction, besides extracting features that are relevant to the arrhythmic alarms, we also extracted a set of signal quality indices (SQIs), which we used to distinguish noise/artifact from normal physiological signals. When applying a machine learning algorithm (Random Forest), we performed feature selection in order to reduce the complexity of the models and improve the efficiency of the algorithm. The dataset used is from Reducing False Arrhythmia Alarms in the ICU: the PhysioNet/Computing in Cardiology Challenge 2015. Using the performance metric “score” from the Challenge, we achieved a score of 83.08 in the real-time category on the hidden test set, which is the highest in all published work.

ICU False Alarm Identification Based on Convolution Neural Network

“…However, due to the nature of regular monitoring physiological signals including heterogeneity, low signal to noise ratio, high deficiency and high temporal resolution over long periods of time [14], applying machine-learning approaches to them is quite challenging, and much human intervention is required. Machine learning algorithms usually focus on extracting features that can distinguish different alarms, and use algorithms such as majority voting [15,16], SVM [17,18], decision trees [19], random forests [20,21,22] for classification. Most machine-learning algorithms use the same inputting parameters as the rule-based strategies, such as SQIs [19,20,22], heartrate [15,19,22], QRS detection [17], etc.…”

“…Machine learning algorithms usually focus on extracting features that can distinguish different alarms, and use algorithms such as majority voting [15,16], SVM [17,18], decision trees [19], random forests [20,21,22] for classification. Most machine-learning algorithms use the same inputting parameters as the rule-based strategies, such as SQIs [19,20,22], heartrate [15,19,22], QRS detection [17], etc. In addition to basic statistical and spectral features, certain derived features designed according to the alarm type are also used [20,22].…”

“…Most machine-learning algorithms use the same inputting parameters as the rule-based strategies, such as SQIs [19,20,22], heartrate [15,19,22], QRS detection [17], etc. In addition to basic statistical and spectral features, certain derived features designed according to the alarm type are also used [20,22]. Due to the large number of features, machine learning solutions often require feature dimensionality reduction, such as using PCA [15,23] and feature selections [22].…”

“…In addition to basic statistical and spectral features, certain derived features designed according to the alarm type are also used [20,22]. Due to the large number of features, machine learning solutions often require feature dimensionality reduction, such as using PCA [15,23] and feature selections [22]. For example, considering that false alarms were usually caused by low QRS complex amplitude and high noise, Au-Yeung and colleagues [22] first processed the signal to ensure that the heartbeat can be correctly labeled and then extracted features from the signal.…”

“…Due to the large number of features, machine learning solutions often require feature dimensionality reduction, such as using PCA [15,23] and feature selections [22]. For example, considering that false alarms were usually caused by low QRS complex amplitude and high noise, Au-Yeung and colleagues [22] first processed the signal to ensure that the heartbeat can be correctly labeled and then extracted features from the signal. In particular, they extracted a series of SQIs to distinguish noise or artifacts from real physiological signals.…”