Abstract: A non-invasive and convenient method and apparatus for approximation of left ventricular end diastolic pressure (LVEDP) can be used in both hospital/clinic environments and nursing home or home environments. The method and apparatus use non-invasive sensors and a new “cardiac triangle” computational method to obtain an approximation of LVEDP. The computational method uses hemodynamic and electrocardiogram (ECG) waveforms as input, which can be collected by a portable device or devices.

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: A non-invasive and convenient method and apparatus for approximation of left ventricular end diastolic pressure (LVEDP) can be used in both hospital/clinic environments and nursing home or home environments. The method and apparatus use non-invasive sensors and a new “cardiac triangle” computational method to obtain an approximation of LVEDP. The computational method uses hemodynamic and electrocardiogram (ECG) waveforms as input, which can be collected by a portable device or devices.

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: A non-invasive and convenient method and apparatus for approximation of left ventricular end diastolic pressure (LVEDP) can be used in both hospital/clinic environments and nursing home or home environments. The method and apparatus use non-invasive sensors and a new “cardiac triangle” computational method to obtain an approximation of LVEDP. The computational method uses hemodynamic and electrocardiogram (ECG) waveforms as input, which can be collected by a portable device or devices.

Abstract: Systems and methods are provided for extracting hemodynamic information, optionally employing portable electronic devices with optional User Interface (UI) features for system implementation. The systems and methods may be employed for acquiring hemodynamic signals and associated electrophysiological data and/or analyzing the former or both in combination to yield useful physiological indicia or results. Such hardware and software is advantageously used for non-invasively monitoring cardiac health.

Abstract: A system configured to be at least partially implanted along an aorta includes an inelastic, static member and a pinching member. The pinching member is configured to receive an activation signal at an activation rate and in response to the activation signal, repeatedly compress the aorta at the second location at the activation rate to pump fluid within the aorta in a desired pumping direction. The system is configured to selectively control wave reflections in order to achieve both improved wave dynamics to reduce cardiac load and increased (or at least non-diminished) blood flow to targeted organs within the cardiovascular system.

Abstract: An artificial chamber including a first conduit, a second conduit, a third conduit, and a wall defining a space; in which the first conduit and the second conduit are positioned opposite one another; in which the third conduit is opposite the wall; and in which the wall has a concave surface is disclosed. The chamber can be part of a system for providing pulmonary support. The system includes the chamber and a first pump connected to the third conduit, and connected to a fourth conduit; in which the chamber receives fluid via the first conduit and the second conduit, in which the first pump receives fluid from the chamber via the third conduit; and in which the fourth conduit transports fluid from the first pump to a first blood vessel. Methods of making a chamber and a system, and methods of using the chamber and system are also disclosed.

Abstract: A non-invasive and convenient method and apparatus for approximation of left ventricular end diastolic pressure (LVEDP) can be used in both hospital/clinic environments and nursing home or home environments. The method and apparatus use non-invasive sensors and a new “cardiac triangle” computational method to obtain an approximation of LVEDP. The computational method uses hemodynamic and electrocardiogram (ECG) waveforms as input, which can be collected by a portable device or devices.