Multimodal Arrhythmia Classification Using Deep Neural Networks

Abstract

Arrhythmias are deviations from the normal heart rhythm with impact on the cardiovascular health. Their prompt detection plays an important role in mitigating potential negative outcomes, particularly in patients in the intensive care units (ICU). The detection of arrhythmias has mainly been focused on electrocardiogram (ECG) signals. However, ICU patient mobility frequently leads to disconnection of certain ECG leads, potentially compromising the accurate arrhythmia detection. Arterial line blood pressure (ABP) and central venous pressure (CVP) signals are routinely monitored in ICU patients. Changes in the ABP and CVP suggest alterations in the haemodynamic status and cardiac function of the patients. Thus, leveraging these signals for arrhythmia detection, either independently or in conjunction with ECG data, present a viable approach to ensure that even in scenarios where ECG signals are unavailable, alarm systems alerting healthcare providers of arrhythmias remain functional. In this paper, we employ a hybrid model using long-short term memory networks (LSTM) and convolutional neural network (CNN), along with different residual CNN (ResNet) models for multimodal arrhythmia classification. When using all three channels, ResNet50 achieved the best accuracy of 99.58% on five different arrhythmia classes. The significant efficiency of utilizing ABP and CVP signals independently for the classification of arrhythmias, was also highlighted. ResNet50 was trained with ABP and CVP signals independently and correctly identified arrhythmias with an accuracy of 98.79% and 96.67%, respectively. Moreover, the same ResNet50 model was trained on the MIT-BIH arrhythmia database, achieving an accuracy, sensitivity, and precision of 98.78%, 98.77% and 98.80%, which demonstrates the scalability of the proposed model.