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QRS detection using adaptive filters: A comparative study.

Shweta Jain¹, M K Ahirwal², Anil Kumar¹

¹PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, MP 482005, India.

ISA Transactions

|October 18, 2016

Summary

This summary is machine-generated.

This study introduces an improved QRS detection algorithm for electrocardiogram (ECG) signals. The leaky LMS algorithm demonstrated superior performance, even in noisy conditions, achieving high sensitivity and positive predictivity.

Keywords:

Adaptive filtering Adaptive thresholding ECG Leaky-LMS QRS complex detection

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Area of Science:

Biomedical Engineering
Signal Processing

Background:

Electrocardiogram (ECG) signals are crucial for assessing cardiac health.
QRS detection is essential for monitoring ECG signals and extracting clinical information.

Purpose of the Study:

To design an improved QRS detection algorithm using adaptive filtering.
To evaluate the effectiveness of various Least Mean Squares (LMS) variants for QRS detection.

Main Methods:

Implemented and compared multiple LMS algorithm variants, including Sign-sign LMS, Sign error LMS, basic LMS, normalized LMS, variable leaky LMS, variable step-size LMS, leaky LMS, Recursive Least Squares (RLS), and fractional LMS.
Evaluated algorithm performance using fidelity parameters such as sensitivity and positive predictivity.
Tested algorithms on the MIT/BIH arrhythmia database.

Main Results:

The leaky LMS algorithm achieved the best performance with 99.68% sensitivity and 99.84% positive predictivity.
Processing time for the leaky LMS algorithm was recorded at 0.45 seconds.
Most LMS variants' performance degraded in low signal-to-noise ratio (SNR) environments, but leaky LMS maintained effectiveness.

Conclusions:

The leaky LMS algorithm is a highly effective method for QRS detection in ECG signals.
Leaky LMS offers robust performance, outperforming other variants, especially under noisy conditions.
The developed algorithm provides accurate and efficient QRS detection for cardiac monitoring.