Patent ID: 11969266
Assignee: NORTHWESTERN UNIVERSITY
Field: Medical technology (Instruments)
Classification: CPC A  G | IPC A  G

Claim 17:
18. A method of sensing and classifying physiological parameters of a body, comprising:
(a) implanting a deep learning medical device into the body, the deep learning medical device comprising:
a sensing and actuation unit comprising one or more implantable sensors operative to sense physiological parameters of the body and one or more actuators; and
a processing and communication unit, in communication with the sensing and actuation unit, comprising:
a deep learning module operative to receive input samples from the sensing and actuation unit, the deep learning module including a neural network trained to process the input samples through a plurality of layers to classify the physiological parameters sensed by the sensing and actuation unit and provide classification results, wherein the input samples are provided as a plurality of channels, and
a communication interface in communication with the deep learning module to receive the classification results, the communication interface comprising an ultrasonic transceiver to transmit and receive ultrasonic signals through biological tissue;

(b) transmitting the input samples from the sensing and actuating unit to the communication and processing unit as the plurality of channels;
(c) predicting, by the deep learning module, classifications for each of the p a channels individually per a sampling time interval;
(d) at least one of:
selecting a spatial probable class output across space by a spatial majority vote across the plurality of channels by determining a probability that a number of successful spatially predicted classifications is at least greater than half of a number of the plurality of channels within the sampling time interval according to a spatial class output cumulative distribution function (spatial CDF), P
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 wherein X is the number of successful spatially predicted classifications within the sampling time interval, wherein nc is a total number of channels in the plurality of channels used for the majority vote, wherein p is a probability of success of the neural network for a single one of the plurality of channels, and wherein ┌nc/2┐ is a ceiling of nc/2; and/or
selecting a temporal probable class output across time by a temporal majority vote across a plurality of the sampling time intervals within each individual one of the plurality of channels by determining a probability that a number of successful temporally predicted classifications is at least greater than half of the number of sampling time intervals within the individual one of the plurality of channels according to a temporal class output cumulative distribution function (temporal CDF), P
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 wherein X is the number of successful temporally predicted classifications within the individual one of the plurality of channels wherein nt is a total number of sampling time intervals used for the majority vote, wherein p is the probability of success of the neural network for each individual one of the sampling time intervals, and wherein, ⌈
  
   
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 is a ceiling of, n
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and

(e) one or both of:
at the communication interface, transmitting the classification results to an external device, and
at the processing and communication unit, determining instructions, based on the classification results, for the sensing and actuation unit and transmitting the instructions to the sensing and actuation unit.