University of Connecticut

Defibtech, LLC

Ki H Chon

Seyed M A Salehizadeh

Yeonsik Noh

Alicia Cascella

Matt Valentine

Shirin Hajeb

Shirin Hajeb Mohammadalipour

Methods, systems, and apparatuses are described for automated external defibrillation for determining shock/no-shock decisions based on ECG readings taken when a patient is undergoing cardiopulmonary resuscitation (CPR). A device may filter an ECG signal of a patient to reduce artifacts from the signal caused by CPR being performed on the patient. The device may use the filtered ECG signal to determine whether to deliver a therapeutic shock to a patient even if the patient is undergoing CPR.

Automated condition-based suppression of CPR artifacts in ECG data

The invention provides, in some aspects, an automated external defibrillator (AED) that includes defibrillation circuitry capable of delivering a therapeutic shock to a patient. The AED also includes an input that receives a patient ECG signal representing real-time electrical activity of a heart of the patient and that may include artifacts resulting from cardiopulmonary resuscitation of the patient. The AED generates a real-time alert from a patient data image that is based on the patient ECG signal to effect application of the therapeutic shock to the patient. The AED generates that alert based at least in part on a shock recommendation of an artificial intelligence (AI) engine that analyzes the patient data image using a machine learning (ML) model trained with training data images. The patient data image comprises pixels whose values are a function of magnitudes of components of representations of respective time-slices of the patient ECG signal in a frequency domain. At least one training data image comprises pixels whose values are a function of magnitudes of components of representations of respective time-slices of a respective training ECG signal in the frequency domain.

Automated external defibrillation with deep neural network

A method and corresponding apparatus employ a time-varying spectral analysis approach for reconstructing a heart-related signal that includes motion artifacts. The motion artifacts are produced by motion of a biomedical sensor relative to a sensing location. By comparing time-varying spectra of the heart-related signal and a motion signal, those frequency peaks resulting from the motion artifacts may be suppressed in a time-varying spectrum of the heart-related signal. The time-varying spectral analysis based approach enables the heart-related signal to be reconstructed with accuracy by suppressing the motion artifacts. Example applications for the method and corresponding apparatus include training aids (e.g., runners' heart-rate monitors) and hospital patient heart-rate monitors.

Method and apparatus for removing motion artifacts from biomedical signals

A method and corresponding apparatus employ a time-varying spectral analysis approach for reconstructing an electrocardiogram (ECG) signal that includes motion artifacts. The motion artifacts are produced by motion of an ECG sensor relative to a sensing location. The time-varying spectral analysis based approach enables the ECG signal to be reconstructed with accuracy by suppressing the motion artifacts. Example applications for the method and corresponding apparatus include ECG-based heart rate monitoring in wearable devices for fitness tracking and health monitoring even during intense physical activities.

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Mansfield Center, CT, United States of America

Ki H Chon