An Energy Efficient ECG Signal Processor Detecting Cardiovascular Diseases on Smartphone

Area of Science:

• Biomedical Engineering
• Cardiovascular Technology
• Integrated Circuit Design

Background:

• Cardiovascular diseases require continuous monitoring and early diagnosis.
• Smartphone-based solutions offer accessibility but often lack efficiency.
• Existing diagnostic algorithms can be computationally intensive for mobile devices.

Purpose of the Study:

• To develop an energy-efficient Application Specific Integrated Circuit (ASIC) for real-time ECG signal processing.
• To integrate the ASIC with a smartphone application for cardiovascular disease diagnosis.
• To evaluate the performance and efficiency of the proposed system for wearable applications.

Main Methods:

• A novel forward search algorithm for ECG signal processing was implemented on a custom PQRST ASIC using 130-nm CMOS technology.
• The ASIC was designed with a small area (1.21 mm²) and low energy dissipation (96 pJ at 0.9 V).
• An Android application processed ASIC outputs to generate diagnostic reports and facilitate physician communication.

Main Results:

• The system achieved low failed detection rates: 0.69% for bundle branch block, 0.69% for hypertrophy, 0.34% for arrhythmia, and 1.72% for myocardial infarction.
• Complete detection of AV block was achieved using the Physionet St. Petersburg arrhythmia database.
• The PQRST ASIC demonstrated excellent energy efficiency, crucial for wearable devices.

Conclusions:

• The integrated ASIC and Android application provide an effective and energy-efficient solution for cardiovascular disease diagnosis.
• This system is highly suitable for developing next-generation wearable health monitoring devices.
• The low power consumption and high accuracy pave the way for widespread adoption in remote patient care.