Edge AI Heart Failure Detection Using LSTM Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical decision-making technologies face challenges in accurately predicting acute heart failure and other adverse heart events, particularly in detecting subtle changes in human physiological signals over time, which are often delayed until they become severe.
Innovation Solution
A system utilizing a multi-level recurrent neural network (RNN) with long short-term memory (LSTM) and attention networks, combined with edge computing, to analyze real-time physiological signals such as heart rate and blood pressure, enabling timely intervention by correlating signal changes over extended periods and providing interpretable AI results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If distributed edge computing devices are used to process physiological signals in real-time, then detection speed and timeliness of adverse events is improved, but device complexity and system architecture becomes more complex
Solution Approach 1:
The system divides the computing architecture into multiple edge computing devices distributed across different locations. Each edge device independently processes physiological signals from nearby sensory devices, segmenting the overall processing task across multiple nodes. This segmentation enables real-time detection at the edge while distributing system complexity across manageable components rather than concentrating it in a single centralized system.
2Measurement precision
If multi-level RNN with LSTM and attention networks are used to analyze long temporal history of physiological signals, then measurement precision of subtle changes is improved, but computational requirements and processing time increases
Solution Approach 1:
The system pre-trains the multi-level RNN with LSTM and attention networks offline using historical physiological data to learn temporal patterns and correlations. This preliminary training phase allows the model to capture subtle changes in physiological signals over extended periods. During real-time operation, the pre-trained model performs inference with reduced computational overhead, balancing detection precision with acceptable processing time by shifting heavy computation to the offline training phase.
3Reliability
If real-time correlation of multiple physiological signals is performed to detect subtle changes, then reliability of adverse event prediction is improved, but computational load and energy consumption increases
Solution Approach 1:
The system performs signal correlation and analysis locally at distributed edge computing devices rather than centralizing all processing. Each edge device processes physiological signals from its local sensory devices, making predictions based on local data patterns. This local quality approach improves reliability by enabling real-time detection at the source while reducing energy consumption associated with transmitting and processing all data centrally, as only critical results need transmission to the central server.
4Area of stationary object
If distributed edge computing devices are physically spaced apart to enable widespread monitoring, then coverage area and patient reach is improved, but data communication overhead and network requirements increases
Solution Approach 1:
The system extracts and processes critical physiological information locally at each distributed edge computing device before transmission. Instead of transmitting raw physiological signals from all sensors across the network, each edge device extracts relevant features and predictions locally, transmitting only these processed results to the central server. This extraction approach expands coverage area through distributed deployment while minimizing data communication overhead by sending only essential information across the network.
Data Source
AI summary
A system and method for processing human related data to make personalized and context aware decisions with distributed machine learning at an edge and a cloud is disclosed. A nearest edge computing device receives first, second and third sensed signals from first, second and third sensory devices, determines when the first, second and third sensed signals exceed corresponding thresholds, correlates pairs of the sensed signals to generate multiple correlation patterns, determines a lag time between the first sensed signal exceeding the first threshold and the second sensed signal exceeding the second threshold, provides each of the multiple correlation patterns and the lag time as inputs to multiple long short term memory (LSTM) neural networks, controls the multiple LSTM neural networks to provide outputs, and maps the patient to a stage of a medical condition based at least in part on the multiple correlation patterns and the lag time.