An electrocardiogram analysis apparatus comprising: a machine learning part that has a machine learning model realized by machine learning that uses training electrocardiogram data of a patient with paroxysmal arrhythmia during a non-paroxysmal period during which no episode of the paroxysmal arrhythmia occurs and training electrocardiogram data of a person who has been determined not to have the paroxysmal arrhythmia, the machine learning model outputting, when an electrocardiogram data of a person who has not been determined whether to have the paroxysmal arrhythmia is input, information regarding whether the input electrocardiogram data is an electrocardiogram data of a person having the paroxysmal arrhythmia; an input processing part that inputs electrocardiogram data for one period of a plurality of periods forming electrocardiogram data of a person to be analyzed who has not been determined whether to have the paroxysmal arrhythmia, which is a subject of analysis, into the machine learning model; and an output control part that outputs, to an information terminal, abnormality information which is about whether the person to be analyzed has the paroxysmal arrhythmia on a basis of information output from the machine leaning model, the abnormality information representing at least one of (i) a value, a level, or a score of the probability that the person to be analyzed has the paroxysmal arrhythmia, (ii) a value, a level, or a score of the probability that the person to be analyzed does not have the paroxysmal arrhythmia, (iii) whether there is the paroxysmal arrhythmia in the person to be analyzed, or (iv) whether there are signs of the paroxysmal arrhythmia in the person to be analyzed, in association with the one period. a machine learning part that has a machine learning model realized by machine learning that uses training electrocardiogram data of a patient with paroxysmal arrhythmia during a non-paroxysmal period during which no episode of the paroxysmal arrhythmia occurs and training electrocardiogram data of a person who has been determined not to have the paroxysmal arrhythmia, the machine learning model outputting, when an electrocardiogram data of a person who has not been determined whether to have the paroxysmal arrhythmia is input, information regarding whether the input electrocardiogram data is an electrocardiogram data of a person having the paroxysmal arrhythmia; an input processing part that inputs electrocardiogram data for one period of a plurality of periods forming electrocardiogram data of a person to be analyzed who has not been determined whether to have the paroxysmal arrhythmia, which is a subject of analysis, into the machine learning model; and an output control part that outputs, to an information terminal, abnormality information which is about whether the person to be analyzed has the paroxysmal arrhythmia on a basis of information output from the machine leaning model, the abnormality information representing at least one of (i) a value, a level, or a score of the probability that the person to be analyzed has the paroxysmal arrhythmia, (ii) a value, a level, or a score of the probability that the person to be analyzed does not have the paroxysmal arrhythmia, (iii) whether there is the paroxysmal arrhythmia in the person to be analyzed, or (iv) whether there are signs of the paroxysmal arrhythmia in the person to be analyzed, in association with the one period.