Abstract: Introduced here approaches to developing, training, and implementing algorithms to cardiac dysfunction through automated analysis of physiological data. As an example, a model may be developed and then trained to quantify left and right ventricular dysfunction using electrocardiogram waveform data that is associated with a population of individuals who are diverse in terms of age, gender, ethnicity, socioeconomic status, and the like. This approach to training allows the model to predict the presence of left and right ventricular dysfunction in a diverse population. Also introduced here is a regression framework for predicting numeric values of left ventricular ejection fraction.

Abstract: Systems and methods for determining whether a subject has a cardiovascular abnormality are provided. An electrocardiogram of the subject is obtained, the electrocardiogram representing a plurality of time intervals summing to a total time interval and comprising, for each lead in a plurality of leads, a plurality of data points representing electronic measurements at the respective lead at a different time interval in the plurality of time intervals. For each lead, a plurality of sub-waveforms is obtained from the electrocardiogram, each sub-waveform (i) having a common duration that is less than the total time interval and that represents a fixed multiple of a reference heartbeat duration, and (ii) offset from a beginning of the electrocardiogram by a unique multiple of a sliding parameter. The plurality of sub-waveforms for each lead is inputted into a neural network comprising a plurality of parameters, obtaining an indication of cardiovascular abnormality.

Abstract: A vision transformer system and method generate a diagnosis from an electrocardiogram (ECG) of the patient. A patch generating module generates image patches of the ECG. A tokenization module generates numerical patch-based tokens corresponding to image patches. A transformer module generates a numerical classification token from the numerical patch-based tokens. A classification module generates and outputs a diagnosis message from the numerical classification token, wherein the diagnosis message is the patient diagnosis corresponding to the patient ECG and indicating a state of health of the heart of the patient. A masking module mask a preset portion of the plurality of patches, and the numerical classification token is generated from the plurality of numerical patch-based tokens, the unmasked patches, and the masked patches. The tokenization module receives ECG training data to be trained to generate the numerical classification token. The method implements the vision transformer system.

Abstract: Introduced here approaches to developing, training, and implementing algorithms to cardiac dysfunction through automated analysis of physiological data. As an example, a model may be developed and then trained to quantify left and right ventricular dysfunction using electrocardiogram waveform data that is associated with a population of individuals who are diverse in terms of age, gender, ethnicity, socioeconomic status, and the like. This approach to training allows the model to predict the presence of left and right ventricular dysfunction in a diverse population. Also introduced here is a regression framework for predicting numeric values of left ventricular ejection fraction.

Abstract: Introduced here approaches to developing, training, and implementing algorithms to cardiac dysfunction through automated analysis of physiological data. As an example, a model may be developed and then trained to quantify left and right ventricular dysfunction using electrocardiogram waveform data that is associated with a population of individuals who are diverse in terms of age, gender, ethnicity, socioeconomic status, and the like. This approach to training allows the model to predict the presence of left and right ventricular dysfunction in a diverse population. Also introduced here is a regression framework for predicting numeric values of left ventricular ejection fraction.