QRS Complex Detection in ECG Signals
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Solution Overview
Problem
Existing methods for detecting the QRS complex in electrocardiogram (ECG) signals face challenges in accurately identifying the R wave amidst noise and transient fluctuations, often requiring complex filtering and threshold adjustments.
Innovation Solution
A method involving the ECG_MODULE, ABS_DIFF_SHORT_MODULE, ABS_DIFF_LONG_MODULE, COMP comparator, PEAK_DETECTOR, PULSE GENERATOR, TH_MODULE, R_AMPLITUDE_MEMORY, and R_TIMESTAMP_MEMORY modules to detect the QRS complex by monitoring differences between instantaneous and averaged ECG signal values, adjusting thresholds based on previous cycle amplitudes, and filtering out noise through averaging and differentiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex filtering and threshold adjustments are used to detect QRS complex, then detection accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the QRS detection process into distinct phases: Q wave detection, R wave detection, and S wave detection. Each phase uses simplified criteria specific to that wave type, avoiding the need for complex overall filtering. The detection algorithm is divided into multiple decision blocks that sequentially process different aspects of the signal.
Solution Approach 2:
The patent changes detection parameters dynamically based on the current detection phase. Different threshold values, time windows, and amplitude criteria are applied depending on whether the system is looking for Q, R, or S waves. This allows simple detection logic to achieve high accuracy by adapting parameters to the specific detection task at hand.
2Reliability
If noise filtering is applied to ECG signal, then signal quality improves, but detection sensitivity to transient fluctuations decreases
Solution Approach 1:
The patent uses dynamic threshold adjustment where the detection thresholds are not fixed but adapt based on the current signal conditions and detection phase. The system dynamically switches between different detection criteria depending on whether it expects a Q wave, R wave, or S wave, maintaining sensitivity to transient fluctuations while filtering noise.
Solution Approach 2:
The patent performs preliminary signal processing to identify the general QRS complex region before attempting to detect specific waves. This preliminary action prepares the system by establishing initial parameters and time windows, allowing subsequent detection to be both noise-resistant and sensitive to important transient features.
3Reliability
If multiple filtering stages are used to remove noise, then noise immunity improves, but processing time increases
Solution Approach 1:
The patent extracts only the essential features needed for QRS detection rather than applying multiple full filtering stages. It takes out the critical detection elements (amplitude thresholds, time windows, wave-specific criteria) from the complete signal processing chain, achieving noise immunity through selective feature extraction rather than exhaustive filtering.
Solution Approach 2:
The patent applies partial filtering action focused specifically on the frequency ranges and time periods relevant to QRS complex detection. Rather than applying broad-spectrum multiple filtering stages, it applies targeted processing only where needed, achieving sufficient noise immunity without excessive processing time.
Data Source
AI summary
The subject of the invention is a method of detecting a QRS complex in an electrocardiogram (ECG) signal, which can be used in biomedical diagnostics. In the method according to the invention, an ABS_DIFF_SHORT signal generated on an output of an ABS_DIFF_SHORT_MODULE module, which is the difference of the current instantaneous value of the ECG signal provided by an ECG_MODULE measuring module, and the mean value of a SHORT_AVG of the ECG signal calculated for a time interval T_SHORT, are monitored using the COMP comparator. Then, the COMP comparator detects the moment when an ABS_DIFF_SHORT signal reaches a threshold TH value previously determined with a TH_MODULE module, and then, when the ABS_DIFF_SHORT signal reaches the threshold TH value, the countdown of a SEARCHING_WINDOW time window starts with a PULSE_GENERATOR pulse generator which has fixed-length for search for a R wave in the QRS complex of the ECG signal. In the next step, using a PEAK_DETECTOR detection module, the maximum value of the ABS_DIFF_LONG_MODULE module produced at its output is recorded during the SEARCHING_WINDOW time window of the ABS_DIFF_LONG signal, which is the difference of the current instantaneous value of the ECG signal and the average LONG_AVG value of the ECG signal calculated for a segment of a T_LONG time segment while the PEAK_DETECTOR detection module the moment of the detected maximum value of the ABS_DIFF_LONG signal is recorded during the SEARCHING_WINDOW time window. After that, the maximum value of the ABS_DIFF_LONG signal detected by the PEAK_DETECTOR detection module is stored by means of a R_AMPLITUDE_MEMORY memory module, which is considered to be the R-wave amplitude in the QRS complex.