Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Wearable Technology

Background:

• Electrocardiogram (ECG) signal analysis is crucial for diagnosing cardiac conditions.
• Wearable ECG devices require algorithms that are robust to noise, efficient, and accurate.
• Existing R-peak detection methods often struggle with the challenges posed by wearable sensor data.

Purpose of the Study:

• To develop a novel R-peak detection algorithm specifically for wearable ECG devices.
• To achieve robustness to noise, low latency, low resource complexity, and automatic parameter tuning.
• To validate the algorithm's performance on diverse public ECG databases.

Main Methods:

• A two-pronged algorithm combining triangle template matching and a single moving average filter.
• Triangle template matching enhances R-peak slope information.
• A dynamic threshold is established using a moving average filter for precise peak detection.

Main Results:

• The algorithm demonstrated high accuracy in R-peak detection across eight public ECG databases.
• Achieved low resource complexity, suitable for resource-constrained wearable devices.
• Successfully addressed noise and parameter tuning challenges inherent in wearable ECG data.

Conclusions:

• The proposed R-peak detection algorithm is highly effective for wearable ECG applications.
• Its combination of accuracy, efficiency, and robustness makes it ideal for real-time health monitoring.
• This algorithm represents a significant advancement for the field of wearable cardiac diagnostics.