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TransfoRhythm: A Transformer Architecture Conductive to Blood Pressure Estimation via Solo PPG Signal Capturing (2404.15352v1)

Published 15 Apr 2024 in eess.SP and cs.LG
TransfoRhythm: A Transformer Architecture Conductive to Blood Pressure Estimation via Solo PPG Signal Capturing

Abstract: Recent statistics indicate that approximately 1.3 billion individuals worldwide suffer from hypertension, a leading cause of premature death globally. Blood pressure (BP) serves as a critical health indicator for accurate and timely diagnosis and/or treatment of hypertension. Driven by recent advancements in AI and Deep Neural Networks (DNNs), there has been a surge of interest in developing data-driven and cuff-less BP estimation solutions. In this context, current literature predominantly focuses on coupling Electrocardiography (ECG) and Photoplethysmography (PPG) sensors, though this approach is constrained by reliance on multiple sensor types. An alternative, utilizing standalone PPG signals, presents challenges due to the absence of auxiliary sensors (ECG), requiring the use of morphological features while addressing motion artifacts and high-frequency noise. To address these issues, the paper introduces the TransfoRhythm framework, a Transformer-based DNN architecture built upon the recently released physiological database, MIMIC-IV. Leveraging Multi-Head Attention (MHA) mechanism, TransfoRhythm identifies dependencies and similarities across data segments, forming a robust framework for cuff-less BP estimation solely using PPG signals. To our knowledge, this paper represents the first study to apply the MIMIC IV dataset for cuff-less BP estimation, and TransfoRhythm is the first MHA-based model trained via MIMIC IV for BP prediction. Performance evaluation through comprehensive experiments demonstrates TransfoRhythm's superiority over its state-of-the-art counterparts. Specifically, TransfoRhythm achieves highly accurate results with Root Mean Square Error (RMSE) of [1.84, 1.42] and Mean Absolute Error (MAE) of [1.50, 1.17] for systolic and diastolic blood pressures, respectively.

PDF HTML Abstract

Exploring Cuffless Blood Pressure Estimation Using the TransfoRhythm Framework

Overview of Research

This paper introduces a novel attention-based Deep Neural Network (DNN) architecture termed the "TransfoRhythm" to estimate blood pressure (BP) from photoplethysmography (PPG) signals. The research employs the recently released MIMIC-IV dataset, marking a significant development in the application of attention mechanisms for cuffless BP monitoring. By leveraging Multi-Head Attention (MHA), the proposed model is designed to capture the rich temporal dynamics and variations in PPG signals, thus enhancing the prediction accuracy of systolic and diastolic blood pressure (SBP and DBP).

Methodology

The TransfoRhythm framework utilizes an advanced neural architecture strategy combining MHA with position embeddings and a time compressor for effective feature translation into BP estimates. The model processes input data through several bespoke preprocessing and feature extraction stages, ensuring that inputs are robust and reflective of significant physiological signals. The architecture of the model supports parallel processing of inputs, crucial for handling the large volumes of data typical in healthcare applications.

Data Handling

The framework employs the MIMIC-IV dataset comprising 198 individual records with PPG, ECG, and invasive arterial blood pressure (ABP) signals. The preprocessing pipeline includes steps for noise reduction, signal smoothing via Butterworth and Moving Average Filters, and artifact removal, ensuring high-quality inputs for the DNN model.

Feature Engineering

Twelve features are extracted from the PPG waveforms focusing on temporal characteristics and morphological attributes which are vital for capturing the waveform dynamics associated with BP changes. These features include cycle duration, peak-to-notch durations, and integrative metrics over defined parts of the PPG waveform.

Results and Performance Evaluation

The TransfoRhythm framework demonstrated high accuracy in BP estimation with mean R-squared values above 0.991 for both SBP and DBP, and low error metrics (MAE and RMSE) across both measurement categories. Performance metrics closely adhere to both AAMI and BHS standards, underscoring the model's utility in clinical settings.

Benchmark Comparisons

Comparative analyses against other deep learning models using the same dataset highlight the superior predictive capability of TransfoRhythm, with significant improvements noted in MAE and RMSE values compared to established benchmarks like ResNet1D, U-Net, and hybrid CNN-RNN models.

Practical Implications and Theoretical Contributions

The TransfoRhythm model's strong performance suggests significant practical applications, particularly in continuous, non-invasive blood pressure monitoring, potentially enhancing patient care in settings where traditional cuff devices are impractical. Theoretically, the work extends knowledge on the efficacy of attention mechanisms in medical signal processing, particularly in modeling time-series data such as PPG signals where traditional methods might falter.

Future Directions

While the current paper prominently showcases the potential of MHA in healthcare, future research might explore hybrid models integrating RNN and CNN features, use larger datasets for training and validation, or extend applications to other physiological signal estimations. Further refining the model to address diverse patient demographics or different health conditions could enhance its robustness and applicability in global health contexts.

Conclusion

In conclusion, the TransfoRhythm framework marks a robust advancement in the use of deep learning technologies for health monitoring, specifically leveraging MHA within a blood pressure estimation context from PPG signals. Its success opens avenues for more personalized, efficient, and non-invasive health monitoring solutions, crucial for modern healthcare infrastructures.

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Authors (5)
  1. Amir Arjomand (1 paper)
  2. Amin Boudesh (2 papers)
  3. Farnoush Bayatmakou (4 papers)
  4. Kenneth B. Kent (5 papers)
  5. Arash Mohammadi (69 papers)