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

Claim 0:
1. A deep learning medical device implantable in a body, 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 the deep learning module is operative to predict classifications for each channel of the plurality of channels individually per a sampling time interval and at least one of:
select 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, ⌈
  
   
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 is a ceiling of, n
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  ;
 

 and/or
select 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, ⌈
  
   
    n
    c
   
   2
  
  ⌉
 

 is a ceiling of, n
    t
   
   2
  
  ,
 

 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.