Noninvasive cardiovascular event detection
AssigneeUniversity of Southern California
InventorsNiema M. PAHLEVAN, Rashid ALAVI DEHKORDI
Life of this patent
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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.
Claims15
A system for detecting myocardial infarction and ischemia using non-invasive tests, the system comprising:
- a wearable device worn by a patient comprising at least one sensor configured to capture a set of arterial blood pressure measurements of the patient, the wearable device being further configured to generate an arterial pressure waveform representing the set of arterial blood pressure measurements;
- a computing system comprising one or more processors executing computer program instructions to effectuate operations comprising:
- obtaining, from the wearable device, data comprising the arterial pressure waveform;
- computing, based on the data representing the arterial pressure waveform, a first intrinsic frequency of a systolic phase of a cardiac cycle of the patient, a second intrinsic frequency of a diastolic phase of the cardiac cycle of the patient, a systolic intrinsic phase angle describing the systolic phase of the cardiac cycle of the patient, and a diastolic intrinsic phase angle describing the diastolic phase of the cardiac cycle of the patient;
- providing the first intrinsic frequency, the second intrinsic frequency, the systolic intrinsic phase angle, and the diastolic intrinsic phase angle to a trained artificial neural network (ANN) configured to generate a classification score representing a likelihood that the patient has experienced a myocardial infarction;
- obtaining, from the trained ANN, the classification score;
- determining, based on the classification score, whether the patient has suffered a myocardial infarction;
- and
- in responsive to determining that the patient has suffered a myocardial infarction, providing an alert to one or more client devices of the patient, a medical provider of the patient, or the patient and the medical provider of the patient.
A non-transitory computer-readable medium storing computer program instructions that, when executed by one or more processors of a computing system, effectuate operations comprising:
- obtaining data representing measurements of a cardiac cycle of a first patient;
- determining, based the data, a first intrinsic frequency and a second intrinsic frequency of the cardiac cycle of the first patient, a first intrinsic phase angle, and a second intrinsic phase angle of the cardiac cycle of the first patient;
- providing the first intrinsic frequency, the second intrinsic frequency, the first intrinsic phase angle, and the second intrinsic phase angle to a trained neural network configured to determine whether the first patient has suffered at least one of a plurality of cardiovascular events;
- obtaining, from the trained neural network, a set of values each representing a likelihood that the first patient has suffered at least one of the plurality of cardiovascular events;
- determining whether one or more values from the set of values satisfy a threshold condition;
- and
- storing a result of the determination of whether one or more values from the set of values satisfy the threshold condition in memory.
A method implemented by a computing system executing computer program instructions, the method comprising:
- obtaining, with the computing system, data representing measurements of a cardiac cycle of a first patient;
- determining, with the computing system, based the data, a first intrinsic frequency of the cardiac cycle of the first patient, a second intrinsic frequency of the cardiac cycle, a first intrinsic phase angle of the cardiac cycle, and a second intrinsic phase angle of the cardiac cycle;
- providing, with the computing system, the first intrinsic frequency, the second intrinsic frequency, the first intrinsic phase angle, and the second intrinsic phase angle to a trained neural network configured to determine whether the first patient has suffered at least one of a plurality of cardiovascular events;
- obtaining, with the computing system, from the trained neural network, a set of values each representing a likelihood that the first patient has suffered at least one of the plurality of cardiovascular events;
- determining, with the computing system, whether one or more values from the set of values satisfy a threshold condition;
- and
- storing, with the computing system, a result of the determination of whether one or more values from the set of values satisfy the threshold condition in memory.
Dependencies are inferred from the claim text.