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Non-Invasive Heart Failure Evaluation Using Machine Learning Algorithms.

Odeh Adeyi Victor¹, Yifan Chen¹, Xiaorong Ding¹

¹School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.

Sensors (Basel, Switzerland)

|April 13, 2024

Summary

This summary is machine-generated.

Continuous monitoring using photoplethysmogram (PPG) and electrocardiogram (ECG) signals accurately detects heart failure. This integrated approach offers a promising non-invasive strategy for early diagnosis and improved cardiovascular health assessment.

Keywords:

echocardiogram heart failure machine learning photoplethysmogram

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Area of Science:

Cardiology
Biomedical Engineering
Data Science

Background:

Heart failure is a widespread cardiovascular disease requiring effective diagnostic methods.
Current diagnostic strategies may not always provide timely intervention.
Non-invasive monitoring offers a potential avenue for early detection.

Purpose of the Study:

To investigate the efficacy of integrating photoplethysmogram (PPG) and electrocardiogram (ECG) signals for early heart failure detection.
To develop a machine learning model using selected physiological features for enhanced diagnostic accuracy.
To evaluate the performance of a combined PPG and ECG approach against single-signal methods.

Main Methods:

Utilized the MIMIC-III database, including 682 heart failure patients and 954 controls.
Focused on continuous, non-invasive signal monitoring.
Selected key features: QRS interval, RR interval, augmentation index, heart rate, systolic/diastolic pressures, and peak-to-peak amplitude.
Trained machine learning algorithms on these selected features.

Main Results:

Achieved high diagnostic performance: 98% accuracy, 97.60% sensitivity, 96.90% specificity, and 97.20% precision.
The integrated PPG and ECG approach outperformed single-signal strategies.
Feature selection reduced computational complexity and overfitting risk.

Conclusions:

The combined PPG and ECG monitoring approach shows significant potential for early and precise heart failure diagnosis.
Continuous monitoring via wearable technology represents a major advancement in non-invasive cardiovascular assessment.
The proposed method is suitable for hardware implementation to facilitate continuous monitoring and early detection of critical conditions.