GPU Parallel ECG Signal Analysis for Real-Time Health Cloud Processing
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Solution Overview
Problem
Current electrocardiogram (ECG) data analysis technologies face challenges in efficiently processing large-scale, long-term ECG data, leading to prolonged analysis times, which negatively impact user experience and the ability to provide rapid feedback on health cloud platforms.
Innovation Solution
A GPU-based parallel ECG signal analysis method is introduced, utilizing multiple threads to perform filtering, artifact removal, QRS detection, and abnormal waveform classification in parallel, leveraging GPU device capabilities to significantly speed up the analysis process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If serial electrocardiogram data analysis algorithm is used, then real-time analysis and real-time feedback of short-term electrocardiogram data is achieved, but the analysis of long-term electrocardiogram data costs much time and seriously affects user experience
Solution Approach 1:
The patent segments the long-term ECG data analysis process into multiple independent processing tasks that can be executed in parallel on GPU devices. Each thread handles a specific segment or aspect of the analysis (e.g., different time windows, different signal processing operations), allowing simultaneous processing of multiple data portions without interfering with each other, thereby reducing total analysis time while maintaining comprehensive analysis quality
Solution Approach 2:
The patent transitions from serial processing (one-dimensional time sequence) to parallel processing by utilizing the multi-threaded architecture of GPU devices. This dimensional change allows multiple analysis operations to occur simultaneously across different threads, effectively converting a sequential time-based process into a spatially parallel process that achieves real-time analysis of long-term ECG data
2Productivity
If GPU-based parallel processing is implemented, then analysis speed is significantly increased, but device complexity increases
Solution Approach 1:
The patent leverages the universal parallel processing capability of GPU devices to handle multiple ECG analysis tasks simultaneously. The same GPU hardware and parallel processing framework are used for various processing stages (filtering, artifact removal, QRS detection, classification), eliminating the need for separate specialized hardware for each function and reducing overall system complexity while maintaining high analysis speed
Solution Approach 2:
The patent employs self-organizing parallel processing algorithms that automatically distribute computational tasks across available GPU threads without requiring complex external scheduling or control mechanisms. The processing system adapts to available resources and autonomously manages task allocation, reducing the complexity of system control while achieving efficient parallel processing
Data Source
AI summary
The present disclosure provides a GPU-based parallel electrocardiogram signal analysis method, comprising: performing a filtering process of electrocardiogram signals through a long interval artifact removal and a short interval artifact removal; performing a QRS detection of the filtering-processed electrocardiogram signals through an R-wave position extraction, a QRS complex start and end positions extraction and a QRS complex width extraction; performing an abnormal waveform classification of the QRS-detected electrocardiogram signals through template creation; wherein at least one of the long interval artifact removal, the short interval artifact removal, the R-wave position extraction, the QRS complex width extraction and the creation template is performed by a multiple threads at a GPU device side in parallel, any thread being read through its unique index number to process corresponding data. By executing one or more steps of the electrocardiogram signal analysis at GPU in parallel, the present disclosure increases the analysis speed of the electrocardiogram signals.