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<p>Vital signs such as blood pressure, heart rate, and respiration rate are continuously monitored in intensive care unit patients to assess their condition. Various methods are available for the continuous monitoring of these vital parameters. To extract parameters, current techniques place multiple sensors on the patient’s body. Patients dealing with medical issues may find it challenging and uncomfortable to have multiple electrodes placed on their bodies. To avoid placing multiple sensors on a patient’s body, the proposed method aims to extract three vital parameters—respiration rate (RR), blood pressure, and heart rate—from a single photoplethysmography sensor, using a unified deep learning model to analyze the photoplethysmographic (PPG) signal. The proposed deep learning framework combines a Convolutional Neural Network (CNN) with Bidirectional Long Short-Term Memory (Bi-LSTM) and an attention mechanism. This model effectively extracts features by integrating spatial and temporal correlations within the signal, focusing on the most relevant features necessary for estimating multiple parameters from a PPG signal. Optimized through hyperparameter tuning, the CNN-Bi-LSTM architecture achieved a prediction accuracy of 95.67%. The performance of the proposed method is evaluated using the publicly available Multiparameter Intelligent Monitoring in Intensive Care Database and compared to existing methods. The model demonstrated an average mean absolute error (MAE) ± standard deviation (SD) of 0.084 ± 0.20 for heart rate, 0.034 ± 0.23 for blood pressure, and 0.009 ± 0.05 for respiration rate.</p>

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Estimation of vital parameters from photoplethysmography using deep learning architecture

  • 期刊论文

  • 发表日期：

    2025年1月2日

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  • C. Helen Sulochana,
  • S. L. Siva Dharshini,
  • S. A. Praylin Selva Blessy

摘要

Vital signs such as blood pressure, heart rate, and respiration rate are continuously monitored in intensive care unit patients to assess their condition. Various methods are available for the continuous monitoring of these vital parameters. To extract parameters, current techniques place multiple sensors on the patient’s body. Patients dealing with medical issues may find it challenging and uncomfortable to have multiple electrodes placed on their bodies. To avoid placing multiple sensors on a patient’s body, the proposed method aims to extract three vital parameters—respiration rate (RR), blood pressure, and heart rate—from a single photoplethysmography sensor, using a unified deep learning model to analyze the photoplethysmographic (PPG) signal. The proposed deep learning framework combines a Convolutional Neural Network (CNN) with Bidirectional Long Short-Term Memory (Bi-LSTM) and an attention mechanism. This model effectively extracts features by integrating spatial and temporal correlations within the signal, focusing on the most relevant features necessary for estimating multiple parameters from a PPG signal. Optimized through hyperparameter tuning, the CNN-Bi-LSTM architecture achieved a prediction accuracy of 95.67%. The performance of the proposed method is evaluated using the publicly available Multiparameter Intelligent Monitoring in Intensive Care Database and compared to existing methods. The model demonstrated an average mean absolute error (MAE) ± standard deviation (SD) of 0.084 ± 0.20 for heart rate, 0.034 ± 0.23 for blood pressure, and 0.009 ± 0.05 for respiration rate.