From Pacemaker to Wearable: Techniques for ECG Detection Systems.

Area of Science:

• Biomedical Engineering
• Cardiology
• Signal Processing

Background:

• Cardiovascular diseases (CVD) are a leading cause of death globally, necessitating advanced diagnostic tools.
• Electrocardiogram (ECG) analysis is a crucial, convenient method for assessing cardiac function and detecting heart irregularities.
• Current ECG analysis methods often focus on noise removal, rhythm analysis, and heartbeat detection for pacemaker improvement, but require further clinical validation.

Purpose of the Study:

• To discuss techniques for implementing on-chip ECG detectors in cardiac pacemaker systems.
• To review challenges in ECG signal morphology analysis from existing medical literature.
• To identify gaps in current ECG detection advancements and testing methodologies.

Main Methods:

• Literature review of ECG signal analysis techniques for cardiac pacemakers.
• Extensive review of challenges in ECG signal morphology analysis.
• Identification of essential performance indicators for state-of-the-art ECG detectors, including robustness to noise, wavelet parameter selection, numerical efficiency, and detection performance.

Main Results:

• Key performance indicators for on-chip ECG detectors include noise robustness, optimal wavelet parameter choice, numerical efficiency, and accurate detection.
• Many existing ECG detection algorithms lack verification using standard ECG databases and limited datasets.
• Some algorithms demonstrate high detection performance for QRS complexes but are validated on insufficient data.

Conclusions:

• Robustness, efficiency, and validated performance are critical for on-chip ECG detectors in pacemakers.
• There is a significant need for comprehensive testing and clinical validation of ECG detection algorithms using diverse datasets.
• Implementing standardized evaluation methods, such as the bullseye test for morphology analysis, is essential to address current gaps.