Abstract: A method of generating a model for generating a synthetic electrocardiography (ECG) signal comprises: receiving subject-specific training data for machine learning, said training data comprising a photoplethysmography (PPG) signal acquired from the subject and an ECG signal acquired from the subject, wherein the ECG signal provides a ground truth of the subject for associating the ECG signal with the PPG signal; using associated pairs of a time-series of the PPG signal and a corresponding time-series of the ECG signal as input to a deep neural network, DNN; and determining, through the DNN, a subject-specific model relating the PPG signal of the subject to the ECG signal of the subject for converting the PPG signal to a synthetic ECG signal using the subject-specific model.

Abstract: A method of generating a model for generating a synthetic electrocardiography (ECG) signal comprises: receiving subject-specific training data for machine learning, said training data comprising a photoplethysmography (PPG) signal acquired from the subject and an ECG signal acquired from the subject, wherein the ECG signal provides a ground truth of the subject for associating the ECG signal with the PPG signal; using associated pairs of a time-series of the PPG signal and a corresponding time-series of the ECG signal as input to a deep neural network, DNN; and determining, through the DNN, a subject-specific model relating the PPG signal of the subject to the ECG signal of the subject for converting the PPG signal to a synthetic ECG signal using the subject-specific model.

Abstract: A method of generating a model for heart rate estimation from a photoplethysmography, PPG, signal of a subject comprises: receiving (102) subject-specific training data for machine learning, said training data comprising a PPG signal from the subject and a heart rate indicating signal from the subject, wherein the heart rate indicating signal provides a ground truth of heart rates of the subject for associating a heart rate with a time period of the PPG signal; using (104) associated pairs of a heart rate and a complete dataset of a time-series of a PPG signal over a time period as input to a deep neural network, DNN; and determining (106; 312), through the DNN, a subject-specific model relating the PPG signal of the subject to the heart rate of the subject.