Avive Solutions, Inc.

Benjamin C Freed

David P Walter, Iii

Neural network based heart rhythm classifiers are described. The neural network is configured to receive an electrocardiogram segment and to output an indication of whether the electrocardiogram segment represents a heart rhythm that is suitable for treatment by a defibrillation shock. Preferably, the received electrocardiogram segment is not transformed or processed prior to its reception by the neural network and no features of the electrocardiogram are identified to the neural network. In some embodiments, the received electrocardiogram segment is the sole input to the neural network. In various embodiments, the neural network is configured to classify electrocardiogram segments obtained while CPR was being performed on the patient. Classifiers that output a characteristic of CPR are also described. Such outputs may include an indication of whether CPR was being performed while the ECG was being detected, compression depth, etc. The described classifiers are well suited for use in defibrillators.

Shockable heart rhythm classifier for defibrillators

A variety of convolutional neural network based shockable heart rhythm classifiers are described. The neural network is configured to receive an electrocardiogram segment as an input and to generate an output indicative of whether the received electrocardiogram segment represents a heart rhythm that is suitable for treatment by a defibrillation shock. Preferably, the received electrocardiogram segment is not transformed or processed prior to its reception by the convolutional neural network and no features of the electrocardiogram are identified to the convolutional neural network. In some embodiments, the received electrocardiogram segment is the sole input to the convolutional neural network. The described classifier is well suited for use in defibrillators.

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Frederick, MD, United States of America

Benjamin C Freed