Abstract: Systems, methods, devices, and machine readable media storing instructions (programming) for an instantaneous or nearly instantaneous (e.g., within 0.1 seconds, within 0.001 seconds, etc.), non-invasive, and easy-to-use transfer from a radial and/or brachial waveform to a carotid waveform or its reduced-order parameters are described. Some embodiments relate to systems, methods, devices, and programming for determining cardiovascular (clinical) indices and biomarkers from two or more of the radial and/or brachial and/or carotid waveforms (or their corresponding reduced-order representations).

Abstract: Some embodiments relate to non-invasive techniques for determining whether a patient has experienced heart failure. In some embodiments, a machine learning model may be trained to determine whether the patient has experienced heart failure using blood pressure waveforms and ECGs taken concurrently. Using the intrinsic frequency methodology and the cardiac triangle mapping methodology, features about the patient's cardiac cycle can be extracted and provided to the trained model as input.

Abstract: The present application relates to using noninvasive techniques to determine whether a patient has experienced a cardiac event. The present application also relates to using noninvasive techniques to determine a size of a myocardial infarction experienced by a patient. In some embodiments, arterial pressure waveforms may be obtained, and from the arterial pressure waveform, a set of cardiac parameters may be extracted. The extracted cardiac parameters may be provided, as input, to the trained machine learning model, which may output a result indicating whether the patient experienced a cardiac event, a size of a myocardial infarction experience by a patient, or other information about the patient's cardiac health.

Abstract: The present application relates to using noninvasive techniques to determine whether a patient has experienced a cardiac event. The present application also relates to using noninvasive techniques to determine a size of a myocardial infarction experienced by a patient. In some embodiments, arterial pressure waveforms may be obtained, and from the arterial pressure waveform, a set of cardiac parameters may be extracted. The extracted cardiac parameters may be provided, as input, to the trained machine learning model, which may output a result indicating whether the patient experienced a cardiac event, a size of a myocardial infarction experience by a patient, or other information about the patient's cardiac health.

Abstract: Artificial intelligence (AI) based methodology for instantaneous signal analysis of cardiovascular waveforms using a single or multiple hemodynamic waveform(s) is described. For example, a system comprising at least one programmable processor and a non-transitory machine-readable medium storing instructions which, when executed by the at least one programmable processor, cause the at least one programmable processor to perform operations comprising receiving patient data having one or more cardiovascular waveforms related to a cardiac cycle or a vasculature of a patient; calculating, from the one or more waveforms, at least one output from a signal analysis method, inputting, into a trained artificial intelligence model, cardiovascular waveforms; determining, utilizing the trained artificial intelligence model, the clinically relevant parameters from a signal analysis method; and in response to determining the output parameters, providing the information about the underlying pathology to a user.