Waveform Data Segmentation for AI Event Detection
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Solution Overview
Problem
Current methods for aggregating outputs from AI models used in waveform data analysis in healthcare settings result in lower-than-desired performance, leading to inaccurate identification of segments with specified events in clinical waveform data.
Innovation Solution
A waveform processing system that divides data into overlapping chunks of fixed length, inputs these chunks into an AI model, and aggregates the outputs to create a segment identification array, using a sliding window approach to improve accuracy and reliability of event detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If waveform data is processed using current aggregation methods, then processing is simpler, but accuracy of segment identification deteriorates
Solution Approach 1:
The waveform data is divided into multiple overlapping chunks, each processed independently by the AI model. This segmentation allows the system to handle long waveform data efficiently while maintaining high identification accuracy through localized analysis and subsequent aggregation of results.
Solution Approach 2:
Multiple chunk segment identification arrays are merged through aggregation to create a comprehensive segment identification array. The aggregation process combines results from overlapping chunks, resolving discrepancies and enhancing overall identification accuracy beyond what single-chunk analysis could achieve.
2Reliability
If waveform data is divided into non-overlapping chunks, then processing is faster, but reliability of event detection deteriorates
Solution Approach 1:
Overlapping chunks are prepared in advance with predetermined offsets, ensuring that boundary regions are included in multiple chunks. This preliminary structuring enables reliable event detection at chunk boundaries without requiring re-processing, maintaining both reliability and processing efficiency.
Solution Approach 2:
The aggregation process incorporates feedback from multiple overlapping chunk analyses, allowing the system to verify and correct detections. Events detected in one chunk are cross-validated against adjacent overlapping chunks, improving reliability while the systematic aggregation approach maintains processing speed.
3Measurement precision
If AI model output aggregation is simplified, then computation is less intensive, but performance deteriorates
Solution Approach 1:
The aggregation process applies different handling to different regions of the chunk arrays. Local operations focus computational resources on resolving discrepancies in overlapping regions where events are most likely to be missed or misclassified, rather than uniformly processing entire arrays, thus improving performance without excessive computational overhead.
Solution Approach 2:
The system adjusts aggregation parameters such as the offset between overlapping chunks and the aggregation threshold based on performance requirements. By optimizing these parameters, the system achieves high AI model performance while controlling computation intensity through efficient aggregation strategies.
Data Source
AI summary
A waveform processing system including a patient monitor, a display device, and a processor configured to control the display device to display a location in a stream of waveform data of a segment including a specified event such as aberration or variation in the waveform data, wherein the location of the segment is identified based on aggregating outputs of an AI model performing sliding window inferences on the stream of waveform data. Performing the sliding window inferences on the stream of waveform data may include dividing the waveform data into a plurality of overlapping chunks, each chunk comprising consecutive data values extracted from the stream of waveform data over a specified duration, and inputting each chunk into the AI model to generate arrays of output values indicating a presence or absence the segment, which may then be aggregated.