Continuous blood pressure prediction system using Conv-LSTM network on hybrid latent features of photoplethysmogram (PPG) and electrocardiogram (ECG) signals.

Continuous blood pressure (BP) monitoring is essential for managing cardiovascular disease. However, existing devices often require expert handling, highlighting the need for alternative methods to simplify the process. Researchers have developed various methods using physiological signals to address this issue.

Evaluation of cardiac pro-arrhythmic risks using the artificial neural network with ToR-ORd model output.

Torsades de pointes (TdP) is a type of ventricular arrhythmia that can lead to sudden cardiac death. Drug-induced TdP has been an important concern for researchers and international regulatory boards. The Comprehensive Proarrhythmia Assay (CiPA) initiative was proposed that integrates testing and computational models of cardiac ion channels and human cardiomyocyte cells to evaluate the proarrhythmic risk of drugs.

PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN-LSTM.

Researchers commonly use continuous noninvasive blood-pressure measurement (cNIBP) based on photoplethysmography (PPG) signals to monitor blood pressure conveniently. However, the performance of the system still needs to be improved. Accuracy and precision in blood-pressure measurements are critical factors in diagnosing and managing patients' health conditions.

qInward variability-based proarrhythmic risk assessment of drugs using deep learning model.

Many researchers have suggested evaluation methods and Torsades de Pointes (TdP) metrics to assess the proarrhythmic risk of a drug based on the simulation, as part of the Comprehensive Proarrhythmia Assay (CiPA) project. In the previous study, we validated the robustness of 12 features using the ordinal logistic regression (OLR) model by comparing the classification performances of metrics according to the experimental datasets used; however, the OLR model using 12 features did not provide desirable results. This study proposed a convolutional neural network (CNN) model using the variability of promising TdP metrics hypothesizing that the variability of features based on beats has more information than the single value of features.