Abstract: According to an embodiment of the present disclosure, disclosed is a method for evaluating qualities of electrocardiogram data and diagnosing a disease only with a signal having an excellent quality. Specifically, according to the present disclosure, a computing device evaluates qualities of electrocardiogram data obtained from a plurality of leads, excludes electrocardiogram data obtained from at least one lead among the plurality of leads based on the evaluation of the qualities of the electrocardiogram data, and performs electrocardiogram analysis based on remaining electrocardiogram data by using a pre-learned electrocardiogram analysis model.

Abstract: Disclosed is a method, performed by a computing device, for analyzing electrocardiogram (ECG) data according to an embodiment of the present disclosure. The method may include the steps of: using a first model that uses, as input, ECG data acquired from an ECG measurement device, and determining whether an event related to at least one of atrial fibrillation or atrial flutter is found in the acquired ECG data; generating a first diagnosis result for the acquired ECG data when the event is found in the acquired ECG data; and when the event is not found in the acquired ECG data, generating a second diagnosis result estimating the risk level for at least one of atrial fibrillation or atrial flutter by using a second model that uses the acquired ECG data as input.

Abstract: An exemplary embodiment of the present disclosure discloses a method of estimating a heart age by using a pre-trained artificial neural network model. In particular, according to the present disclosure, a computing device obtains vital sign data of a user. The computing device estimates a heart age of the user based on the vital sign data of the user by using a pre-trained artificial neural network model. The pre-trained artificial neural network model corresponds to an artificial neural network model pre-trained based on information related to a heart disease.