A level-crossing based QRS-detection algorithm for wearable ECG sensors

Area of Science:

• Biomedical Engineering
• Signal Processing
• Integrated Circuits

Background:

• Accurate measurement of electrocardiogram (ECG) RR intervals is crucial for diagnosing cardiac conditions.
• Existing systems for ECG signal processing often face challenges with power consumption and data volume, limiting their use in wearable devices.

Purpose of the Study:

• To introduce an asynchronous analog-to-information conversion system for precise ECG RR interval measurement.
• To develop a novel R-peak detection algorithm optimized for compressed data from level-crossing sampling.

Main Methods:

• A modified level-crossing analog-to-digital converter was employed.
• A new algorithm was designed to detect R-peaks from level-crossing sampled ECG data.
• The system was simulated using the MIT-BIH Arrhythmia Database and synthesized in 0.13 μm CMOS technology.

Main Results:

• The proposed system demonstrated an average R-peak detection accuracy of 98.3%, sensitivity of 98.89%, and positive prediction of 99.4%.
• The synthesized system achieved ultra-low power consumption of 622 nW with a core area of 0.136 mm² at a 1.2 V supply voltage.

Conclusions:

• The developed asynchronous system offers a highly accurate and power-efficient solution for ECG RR interval monitoring.
• Its low power and small form factor make it suitable for integration into wearable wireless ECG sensors for body-sensor networks.