US 12,347,557 B2
Deep neural network (DNN) assisted sensor for energy-efficient electrocardiogram (ECG) monitoring
Nikil Dutt, Irvine, CA (US); and Tao-Yi Lee, Irvine, CA (US)
Assigned to THE REGENTS OF THE UNIVERSITY OF CALIFORNIA, Oakland, CA (US)
Filed by THE REGENTS OF THE UNIVERSITY OF CALIFORNIA, Oakland, CA (US)
Filed on Dec. 1, 2020, as Appl. No. 17/108,494.
Claims priority of provisional application 62/945,655, filed on Dec. 9, 2019.
Prior Publication US 2021/0174961 A1, Jun. 10, 2021
Int. Cl. G16H 50/00 (2018.01); A61B 5/00 (2006.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01)
CPC G16H 50/20 (2018.01) [A61B 5/0006 (2013.01); A61B 5/7267 (2013.01); G16H 40/67 (2018.01)] 6 Claims
OG exemplary drawing
 
1. An energy-efficient method of monitoring a continuous physiological signal while maintaining high accuracy, the method comprises:
a) receiving, by a convolutional neural network (CNN), one or more uncompressed samples of the continuous physiological signal from a sensor;
wherein the sensor comprises a memory unit comprising the CNN;
b) determining, by the CNN, a first probability that the each uncompressed sample of the one or more uncompressed samples is abnormal and a second probability that the uncompressed sample is normal;
c) determining whether or not to transmit the one or more abnormal uncompressed samples using a threshold strategy that utilizes the first probability and the second probability; and
d) transmitting the one or more abnormal uncompressed samples based on said determination;
wherein the CNN has been trained with a dataset comprising at least 205 records;
wherein the CNN has 91,920 parameters;
wherein the CNN comprises a 64-channel 1-dimensional convolution filter having a filter length of 64, a batch normalization layer, a rectified linear activation layer, a dropout layer set to a 25% dropout rate, and a softmax layer.