Wearable ECG Signal Analysis Using FPGA Feature Extraction
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Solution Overview
Problem
Current wearable devices lack an efficient method to determine user-interested information, such as health information related to diseases, using electrocardiography (ECG) signals, which are unique, universal, and difficult to imitate or lose, but require advanced processing for accurate disease detection and identity authentication.
Innovation Solution
A wearable device equipped with an ECG sensor and a field-programmable gate array (FPGA) system that determines a feature set for the ECG signal, including time-domain and frequency-domain data, and uses similarity analysis with reference feature sets to identify health information and identity information, leveraging machine learning techniques for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced processing methods are used to accurately detect disease from ECG signals, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the ECG signal processing into distinct time-domain feature extraction and frequency-domain feature extraction modules. Each module independently processes specific aspects of the signal, allowing complex analysis to be divided into manageable components that can be implemented with simpler, dedicated hardware circuits rather than a monolithic complex system.
Solution Approach 2:
The patent introduces feature extraction circuits as intermediary components between the ECG signal acquisition and the final disease detection decision. These intermediaries transform the raw complex ECG signal into simplified feature representations (time-domain and frequency-domain features) that are easier to process and compare against reference data, reducing the complexity of the overall detection system.
2Loss of time
If ECG signal processing is performed in real-time for timely disease detection, then response time is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary feature extraction in real-time as the ECG signal is being acquired, rather than waiting to collect a complete signal segment before analysis. The time-domain and frequency-domain feature extraction circuits continuously process incoming signal data, preparing feature representations in advance so that comparison with reference features can occur with minimal delay, enabling timely disease detection.
Solution Approach 2:
The patent replaces complex computational algorithms with dedicated hardware extraction circuits that perform feature extraction through specialized electronic operations. These hardware circuits use mathematical transformations (such as Fourier transforms for frequency-domain analysis) implemented in hardware rather than software, enabling real-time processing with reduced computational overhead and faster response time.
3Measurement precision
If multiple feature domains (time and frequency) are analyzed for ECG signals, then detection accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent divides the feature analysis into separate time-domain and frequency-domain processing paths, each with its own dedicated extraction circuit. This segmentation allows each domain to be processed independently using optimized methods specific to that domain, improving accuracy by capturing different aspects of the ECG signal while managing complexity through modular, independent processing blocks.
Data Source
AI summary
This disclosure provides wearable-device based user-interested information determination methods, apparatuses and wearable devices. The method includes: receiving, by an electrocardiography (ECG) sensor associated with the wearable device, an ECG signal of a user, determining a feature set for the ECG signal, in which the feature set includes time-domain feature data of the ECG signal and frequency-domain feature data of the ECG signal, and determining the user-interested information based on similarity between the feature set and reference feature sets indicative of the user-interested information, in which the user-interested information includes health information associated with a disease. The wearable device includes an ECG sensor configured to receive an ECG signal and an FPGA system. The FPGA system includes modules for determine user-interested information based on the ECG signal. The apparatus includes a processor and a memory coupled to the processor. The memory is configured to store instructions to implement the method.