ECG Lead Reconstruction Using Machine Learning

The patent extracts and removes certain electrodes from the conventional 10-electrode configuration, using only a subset (e.g., 3 or 5 electrodes) to record ECG signals. The missing lead information is then reconstructed through mathematical algorithms and machine learning models, thereby reducing the number of physical electrodes needed while maintaining diagnostic accuracy.

The patent creates virtual copies of missing ECG leads through computational reconstruction. Instead of physically placing all 10 electrodes, the system records signals from fewer electrodes and generates synthetic representations of the missing leads using algorithms that replicate the electrical activity patterns, effectively copying the information that would have been obtained from additional electrodes.

The patent introduces mathematical algorithms and machine learning models as intermediary components between the limited physical electrodes and the desired comprehensive ECG output. These intermediaries process the signals from the few physical electrodes and reconstruct the full 12-lead ECG information, bridging the gap between reduced hardware and maintained diagnostic quality.

The patent changes the parameter of electrode count from the conventional 10 electrodes to a reduced number (3 or 5 electrodes), making the system suitable for ambulatory monitoring. Compensation for the reduced measurement points is achieved through advanced signal processing techniques and predictive algorithms that infer missing information from available data.

The patent performs preliminary training of machine learning models using datasets from conventional 12-lead ECG recordings before deployment. During this offline training phase, the algorithms learn to reconstruct missing leads from limited inputs. Once trained, the models can be deployed in portable devices where they perform rapid inference, shifting the computational burden from the portable device to the initial training phase.

The patent employs dynamic adaptive algorithms that can adjust their processing based on the quality and characteristics of the input signals. The reconstruction methods dynamically select appropriate algorithms or adjust parameters based on real-time signal conditions, optimizing the balance between computational complexity and reconstruction accuracy for different monitoring scenarios.