Real-time, low-complexity, low-memory solution to ECG-based heart rate detection.

Area of Science:

• Biomedical Engineering
• Cardiovascular Signal Processing
• Medical Device Technology

Background:

• Electrocardiogram (ECG) signal analysis is crucial for diagnosing cardiac conditions.
• Noisy ECG data, including muscle artifacts, presents significant challenges for accurate QRS complex detection.
• Existing methods often require substantial computational resources, limiting their application in low-power devices.

Purpose of the Study:

• To develop and validate a computationally efficient algorithm for QRS complex detection in noisy ECG signals.
• To assess the performance of the proposed method in terms of detection accuracy and false detection rates.
• To explore the applicability of the method for heart rate monitoring using phonocardiogram (PCG) signals.

Main Methods:

• A novel signal processing technique was designed to filter noise and identify QRS complexes.
• The algorithm was implemented with a focus on minimizing computational complexity and memory footprint.
• Performance evaluation was conducted using the publicly available MIT-BIH arrhythmia database.

Main Results:

• The proposed method achieved a QRS complex detection accuracy of 99.3%.
• A low false detection rate of 0.47% was recorded, demonstrating robustness against noise and artifacts.
• The algorithm's low resource requirements were confirmed, making it suitable for embedded systems.

Conclusions:

• The developed method offers a highly accurate and efficient solution for QRS complex detection in challenging ECG data.
• This technique holds potential for real-time heart rate monitoring applications, including those using phonocardiogram signals.
• The low complexity and memory requirements facilitate integration into portable and wearable medical devices.