Convolutional neural network-based method for the real-time detection of reflex syncope during head-up tilt test

Reflex syncope (RS) is the most common type of syncope caused by dysregulation of the autonomic nervous system. Diagnosing RS typically involves the head-up tilt test (HUTT), which tracks physiological signals such as blood pressure and electrocardiograms during postural changes. However, the HUTT is time-consuming and may trigger RS symptoms in patients. Therefore, a real-time monitoring system for RS risk assessment is necessary to enhance medical efficiency and patient convenience. Although several methods have been developed, most depend on manually extracted features from physiological signals, making them susceptible to feature extraction methods and signal noise.

This study introduces a deep learning-based method for real-time RS detection. This method removes the need for manually extracted features by employing an end-to-end architecture consisting of residual and squeeze-and-excitation blocks. The likelihood of RS occurrence was quantified using the proposed method by analyzing a raw blood pressure signal.

Data from 1348 patients (1291 normal and 57 with RS) were used to develop and evaluate the proposed method. The area under the receiver operating characteristic curve was 0.972 for RS detection using ten-fold cross-validation. A threshold between zero and one can adjust the performance characteristics of the proposed method. At a threshold of 0.75, the method achieved sensitivity and specificity values of 94.74 and 94.27 %, respectively. Notably, the technique detected RS 165.35 s before its occurrence, on average.

The proposed method outperformed conventional methods in RS detection. In addition to its excellent detection performance, this method only requires blood pressure monitoring, reducing reliance on the number of input signals and enhancing its applicability compared to procedures that require multiple signals. These advantages contribute to the development of safer, more convenient, and more efficient RS detection systems.