Georgia Tech Research Corporation;Emory University
发明人:
Shamim Nemati,Gari Clifford,Supreeth Prajwal Shashikumar,Amit Jasvant Shah,Qiao Li
申请号：
US16472818
公开号：
US20190328243A1
申请日:
2017.12.21
申请国别(地区):
US
年份:
2019
代理人:
摘要：
The systems and methods can accurately and efficiently determine abnormal cardiac activity from motion data and/or cardiac data using techniques that can be used for long-term monitoring of a patient. In some embodiments, the method for using machine learning to determine abnormal cardiac activity may include receiving one or more may include applying a trained deep learning architecture to each tensor of the one or more periods of time to classify each window and/or each period into one or more classes using at least the one or more signal quality indices for the cardiac data and the motion data and cardiovascular features. The deep learning architecture may include a convolutional neural network, a bidirectional recurrent neural network, and an attention network. The one or more classes may include abnormal cardiac activity and normal cardiac activity.
