Provided herein are computer-implemented methods of detecting cardiovascular disease in a subject. The methods include receiving an electrocardiogram (ECG) image for the subject; applying a machine-learning based algorithm to the ECG image for the subject, the algorithm being trained to distinguish a printed ECG reading of a heart with cardiovascular disease from a printed ECG reading of a healthy heart; comparing outputs of the algorithm to patterns of algorithm outputs for ECG images from healthy subjects and subjects with one or more cardiovascular diseases; and determining if the subject has cardiovascular disease based upon the outputs of the algorithm.

Provided herein are computer-implemented methods of detecting cardiovascular disease in a subject. The methods include receiving an electrocardiogram (ECG) image for the subject; applying a machine-learning based algorithm to the ECG image for the subject, the algorithm being trained to distinguish a printed ECG reading of a heart with cardiovascular disease from a printed ECG reading of a healthy heart; comparing outputs of the algorithm to patterns of algorithm outputs for ECG images from healthy subjects and subjects with one or more cardiovascular diseases; and determining if the subject has cardiovascular disease based upon the outputs of the algorithm.

A diagnostic electrocardiogram (ECG) apparatus (20) is disclosed herein. The apparatus comprises a body and printed electrodes. The body (110) includes extension members (61-66), with each having an expansion section (61a) and at least one electrode section (61b). Each expansion section (61a) has at least one concertina member (75) and at least one connector member (76). The diagnostic ECG apparatus (20) conforms to American Heart Association guidelines on diagnostic resting ECGs.