Role of a Deep-Learning Based Convolutional Neural Network Model for Real-Time Ventricular Tachycardia Alarm Classification.

BACKGROUND

Ventricular tachycardia (VT) is a life-threatening arrhythmia that requires both relatively rapid and accurate detection in intensive care units (ICUs). Continuous monitoring systems play a crucial role in detecting them. However, previous studies have reported that nearly 9 out of 10 arrhythmia alarms in ICUs tend to be false positives, which usually transpire to a well-documented phenomenon called "alarm fatigue" that leads to desensitization, delayed responses, and increased cognitive burden on healthcare providers.

METHODS

We developed a deep learning based, one-dimensional convolutional neural network (1D-CNN) to classify VT alarms using multiple raw waveform inputs, including two electrocardiogram (ECG) leads, photoplethysmogram (PPG) and arterial blood pressure (ABP) signals. The model was trained using the publicly available VTaC Arrhythmia Benchmark Dataset. We used the 10-second waveform segments that preceded each VT alarm, pre-processed and then used them to train the machine learning model to correctly classify the VT alarm.

RESULTS

On the test set, the model achieved an area under the receiver operating characteristic curve of 0.901, overall accuracy of 83.22%, F1-score of 73.3%, sensitivity of 77.53%, specificity of 85.63%, and positive predictive value of 69.57%. The model successfully detected over three-quarters of them while significantly reducing false positive rates for the detection of VT.

CONCLUSIONS

This study demonstrates that a deep learning based 1D-CNN model using short segments of raw waveform data can achieve robust performance in distinguishing true and false VT alarms.