TransfoRhythm: A Transformer Architecture Conductive to Blood Pressure Estimation via Solo PPG Signal Capturing
CoRR(2024)
摘要
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 Artificial Intelligence (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.
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