Reconstructing ECG precordial leads from a reduced lead set using independent component analysis

Smart ECG Biosensor Design with an Improved ANN Performance Based on the Taguchi Optimizer

This paper aims to design a smart biosensor to predict electrocardiogram (ECG) signals in a specific auscultation site from other ECG signals measured from other measurement sites. The proposed design is based on a hybrid architecture using the Artificial Neural Networks (ANNs) model and Taguchi optimizer to avoid the ANN issues related to hyperparameters and to improve its accuracy. The proposed approach aims to optimize the number and type of inputs to be considered for the ANN model. Indeed, different combinations are considered in order to find the optimal input combination for the best prediction quality. By identifying the factors that influence a model’s prediction and their degree of importance via the modified Taguchi optimizer, the developed biosensor improves the prediction accuracy of ECG signals collected from different auscultation sites compared to the ANN-based biosensor. Based on an actual database, the simulation results show that this improvement is significant; it can reach more than 94% accuracy.

Patient-specific 12-lead ECG reconstruction from sparse electrodes using independent component analysis

We propose and evaluate a method of 12-lead electrocardiogram (ECG) reconstruction from a three-lead set. The method makes use of independent component analysis and results in adaptive patient-specific transforms. The required calibration process is short and makes use of a single beat. We apply the method to two sets of leads: leads I, II, V2 and the Frank XYZ leads. Performance is evaluated via percent correlation calculations between reconstructed and original leads from a publicly available database of 549 ECG recordings. Results depict percent correlation exceeding 96% for almost all leads. Adaptability of the method's transform is shown to compensate for changes in signal propagation conditions due to breathing, resulting in reduced variance of reconstruction accuracy across beats. This implies that the method is robust to changes that occur after the time of calibration. Accurate and adaptive reconstruction has the potential to augment the clinical significance of wireless ECG systems since the number of sensor nodes placed on the body is limited and the subject could be mobile.