Diagnosing Neurally Mediated Syncope Using Classification Techniques

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

BACKGROUND AND OBJECTIVES

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review

Syncope is a transient loss of consciousness with rapid onset. The aims of the study were to systematically evaluate available machine learning (ML) algorithm for supporting syncope diagnosis to determine their performance compared to existing point scoring protocols. We systematically searched IEEE Xplore, Web of Science, and Elsevier for English articles (Jan 2011 - Sep 2021) on individuals aged five and above, employing ML algorithms in syncope detection with Head-up titl table test (HUTT)-monitored hemodynamic parameters and reported metrics. Extracted data encompassed subject count, age range, syncope protocols, ML type, hemodynamic parameters, and performance metrics. Of the 6301 studies initially identified, 10 studies, involving 1205 participants aged 5 to 82 years, met the inclusion criteria, and formed the basis for it. Selected studies must use ML algorithms in syncope detection with hemodynamic parameters recorded throughout HUTT. The overall ML algorithm performance achieved a sensitivity of 88.8% (95% CI: 79.4-96.1%), specificity of 81.5% (95% CI: 69.8-92.8%) and accuracy of 85.8% (95% CI: 78.6-92.8%). Machine learning improves syncope diagnosis compared to traditional scoring, requiring fewer parameters. Future enhancements with larger databases are anticipated. Integrating ML can curb needless admissions, refine diagnostics, and enhance the quality of life for syncope patients.

Classification of vasovagal syncope from physiological signals on tilt table testing

BACKGROUND

The diagnostic test for vasovagal syncope (VVS), the most common cause of syncope is head-up tilt test (HUTT) assessment. During the test, subjects experienced clinical symptoms such as nausea, sweating, pallor, the feeling of palpitations, being on the verge of passing out, and fainting. The study's goal is to develop an algorithm to classify VVS patients based on physiological signals blood pressure (BP) and electrocardiography (ECG) obtained from the HUTT.

METHODS

After 10 min of supine rest, the subject was tilted at a 70-degree angle on a tilt table for approximately a total of 35 min. 400 µg of glyceryl trinitrate (GTN) was administered sublingually after the first 20 min and monitoring continued for another 15 min. Mean imputation and K-nearest neighbors (KNN) imputation approaches to handle missing values. Next, feature selection techniques were implemented, including genetic algorithm, recursive feature elimination, and feature importance, to determine the crucial features. The Mann-Whitney U test was then performed to determine the statistical difference between two groups. Patients with VVS are categorized via machine learning models including Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Multinomial Naïve Bayes (MNB), KNN, Logistic Regression (LR), and Random Forest (RF). The developed model is interpreted using an explainable artificial intelligence (XAI) model known as partial dependence plot.

RESULTS

A total of 137 subjects aged between 9 and 93 years were recruited for this study, 54 experienced clinical symptoms were considered positive tests, while the remaining 83 tested negative. Optimal results were obtained by combining the KNN imputation technique and three tilting features with SVM with 90.5% accuracy, 87.0% sensitivity, 92.7% specificity, 88.6% precision, 87.8% F1 score, and 95.4% ROC (receiver operating characteristics) AUC (area under curve).

CONCLUSIONS

The proposed algorithm effectively classifies VVS patients with over 90% accuracy. However, the study was confined to a small sample size. More clinical datasets are required to ensure that our approach is generalizable.