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A novel ECG compression algorithm using Pulse-Width Modulation integrated quantization for low-power real-time

Isuri Devindi¹, Sashini Liyanage¹, Titus Jayarathna²

¹Department of Computer Engineering, University of Peradeniya, Peradeniya, Sri Lanka.

Scientific Reports

|July 26, 2024

Summary

This summary is machine-generated.

This study introduces a low-complexity algorithm for compressing electrocardiogram (ECG) signals in Internet of Things (IoT) healthcare. The efficient method achieves significant data reduction for real-time cardiac monitoring with wearable devices.

Keywords:

ECG compression Hybrid PWM Low-complexity Nearly-perfect reconstruction Real-time algorithm

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Area of Science:

Biomedical Engineering
Signal Processing
Internet of Things (IoT) Healthcare

Background:

IoT healthcare systems require efficient data processing and transmission due to limited device resources.
Wireless transmission in IoT devices is power-intensive, necessitating ECG signal compression.
Existing methods may lack the required low complexity and efficiency for real-time wearable applications.

Purpose of the Study:

To develop a real-time, low-complexity algorithm for compressing electrocardiogram (ECG) signals.
To enable efficient data transfer from wearable cardiac monitoring devices.
To maintain signal integrity and diagnostic quality post-compression.

Main Methods:

A novel algorithm utilizing only nine arithmetic operations per ECG sample.
Generation of a hybrid Pulse Width Modulation (PWM) signal with 4-bit resolution.
Evaluation using the MIT-BIH database, assessing compression ratio, data savings, and signal fidelity.

Main Results:

Achieved an average Bit Compression Ratio (BCR) of 4 and 90.4% space savings.
Maintained a low Percentage Root-Mean-Square Difference (PRD) of 0.33% and a Quality Score (QS) of 12.
Demonstrated no adverse effects on QRS complex detection or heart rate variability.

Conclusions:

The proposed algorithm offers a highly efficient, low-complexity solution for ECG signal compression in IoT healthcare.
This method significantly reduces data storage and transmission requirements for real-time cardiac monitoring.
The algorithm's versatility suggests potential applications in compressing other signal types.