A Two-Branch ResNet-BiLSTM Deep Learning Framework for Extracting Multimodal Features Applied to PPG-Based Cuffless Blood Pressure Estimation | JoVE Visualize

Area of Science:

Biomedical Engineering
Artificial Intelligence in Healthcare
Cardiovascular Health

Background:

Cardiovascular disease poses a significant health risk, with blood pressure levels being a key indicator.
Continuous blood pressure monitoring is crucial but hindered by the inconvenience of traditional cuff-based devices.
Existing deep learning approaches for cuffless blood pressure estimation often lack interpretability, limiting accuracy.

Purpose of the Study:

To develop a novel, accurate, and interpretable deep learning framework for cuffless blood pressure estimation using photoplethysmography (PPG).
To combine the strengths of Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (BiLSTM) for enhanced PPG signal analysis.
To validate the proposed method against established clinical standards.

Main Methods:

A two-branch deep learning architecture integrating ResNet and BiLSTM was designed.
The ResNet branch processed 60 features selected via Support Vector Machine-Recursive Feature Elimination (SVM-RFE), including novel trend features.
The BiLSTM branch analyzed complete PPG waveforms.
The model was trained and tested on the MIMIC-IV dataset, comprising 220 waveform segments from 218 patients.

Main Results:

The proposed ResNet-BiLSTM model achieved a mean absolute error of 3.47 mmHg for systolic blood pressure and 2.81 mmHg for diastolic blood pressure.
Standard deviations were 5.06 mmHg (systolic) and 4.11 mmHg (diastolic).
Performance met the Association for the Advancement of Medical Instrumentation (AAMI) standards and achieved an 'A' rating by British Hypertension Society (BHS) standards.

Conclusions:

The novel two-branch deep learning framework offers a promising solution for accurate and convenient cuffless blood pressure estimation.
The combination of ResNet and BiLSTM effectively leverages both extracted features and raw PPG waveforms.
The achieved performance validates the clinical applicability and potential of this advanced PPG-based monitoring technique.