6533b851fe1ef96bd12a9a48
RESEARCH PRODUCT
Lead Reconstruction Using Artificial Neural Networks for Ambulatory ECG Acquisition
Alejandro Grande-fidalgoEmilio Soria-olivasJavier CalpeCarlos Millán-navarroMónica Redónsubject
Computer sciencestandard 12-lead systemTP1-1185electrocardiogramBiochemistryArticlelead reconstructionAnalytical ChemistryElectrocardiographyLinear regressionHumansSegmentationSensitivity (control systems)cardiovascular diseasesElectrical and Electronic EngineeringLead (electronics)InstrumentationArtificial neural networkbusiness.industryChemical technologyReconstruction algorithmPattern recognitionSignal Processing Computer-AssistedAtomic and Molecular Physics and Opticscardiovascular diseasesambulatory monitoringAmbulatory ECGElectrocardiography AmbulatoryArtificial intelligenceNeural Networks ComputerEcg signalbusinessartificial neural networkAlgorithmsdescription
One of the most powerful techniques to diagnose cardiovascular diseases is to analyze the electrocardiogram (ECG). To increase diagnostic sensitivity, the ECG might need to be acquired using an ambulatory system, as symptoms may occur during a patient’s daily life. In this paper, we propose using an ambulatory ECG (aECG) recording device with a low number of leads and then estimating the views that would have been obtained with a standard ECG location, reconstructing the complete Standard 12-Lead System, the most widely used system for diagnosis by cardiologists. Four approaches have been explored, including Linear Regression with ECG segmentation and Artificial Neural Networks (ANN). The best reconstruction algorithm is based on ANN, which reconstructs the actual ECG signal with high precision, as the results bring a high accuracy (RMS Error <
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-08-01 | Sensors (Basel, Switzerland) |