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<p>The blood pressure (BP) estimation plays a crucial role in assessing cardiovascular health and preventing related complications. One of the early warning indicators for heart disorders is elevated blood pressure. Thus, monitoring of blood pressure continuously is needed. The study aims to develop and validate a reliable deep learning-based approach for blood pressure estimation using photoplethysmography from the publicly available database MIMIC-II. The continuous wavelet transform (CWT) was used to transform the photoplethysmogram (PPG) signals into scalograms, which were then input into six different deep learning models: VGG16, ResNet50, InceptionV3, NASNetLarge, InceptionResNetV2 and ConvNeXtTiny. The obtained deep features from each one of these models were employed to estimate BP values using random forest. The proposed approach uses a unique transfer learning framework that integrates deep feature extraction from scalograms with random forest regression, providing a new pathway for blood pressure estimation. The models were assessed using mean absolute error (MAE) and standard deviation (SD) in estimating the systolic and diastolic blood pressure values. Out of six models, ConvNeXtTiny and VGG16 showed good performance. ConvNeXtTiny achieved mean absolute error of 2.95&#xa0;mmHg and standard deviation of 4.11&#xa0;mmHg for systolic blood pressure and mean absolute error of 1.66&#xa0;mmHg and standard deviation of 2.60&#xa0;mmHg for diastolic blood pressure. The achieved result complies with the clinical standards set by Advancement of Medical Instrumentation Standard (AAMI) and the British Hypertension Society standard (BHS). This can enhance cardiovascular health monitoring with continuous, non-invasive and reliable blood pressure measurement, assisting in early detection of the disease. The suggested method shows that reliable blood pressure estimation from photoplethysmography signals is possible with the use of deep learning and transfer learning. Above all, ConvNeXtTiny offers a dependable method for continuous blood pressure monitoring that satisfies clinical requirements and may help in the early identification of cardiovascular problems.</p>

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Integrating transfer learning with scalogram analysis for blood pressure estimation from PPG signals

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月12日

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  • Shyamala Subramanian,
  • Sashikala Mishra,
  • Shruti Patil,
  • Maheshkumar H. Kolekar,
  • Fernando Ortiz-Rodriguez

摘要

The blood pressure (BP) estimation plays a crucial role in assessing cardiovascular health and preventing related complications. One of the early warning indicators for heart disorders is elevated blood pressure. Thus, monitoring of blood pressure continuously is needed. The study aims to develop and validate a reliable deep learning-based approach for blood pressure estimation using photoplethysmography from the publicly available database MIMIC-II. The continuous wavelet transform (CWT) was used to transform the photoplethysmogram (PPG) signals into scalograms, which were then input into six different deep learning models: VGG16, ResNet50, InceptionV3, NASNetLarge, InceptionResNetV2 and ConvNeXtTiny. The obtained deep features from each one of these models were employed to estimate BP values using random forest. The proposed approach uses a unique transfer learning framework that integrates deep feature extraction from scalograms with random forest regression, providing a new pathway for blood pressure estimation. The models were assessed using mean absolute error (MAE) and standard deviation (SD) in estimating the systolic and diastolic blood pressure values. Out of six models, ConvNeXtTiny and VGG16 showed good performance. ConvNeXtTiny achieved mean absolute error of 2.95 mmHg and standard deviation of 4.11 mmHg for systolic blood pressure and mean absolute error of 1.66 mmHg and standard deviation of 2.60 mmHg for diastolic blood pressure. The achieved result complies with the clinical standards set by Advancement of Medical Instrumentation Standard (AAMI) and the British Hypertension Society standard (BHS). This can enhance cardiovascular health monitoring with continuous, non-invasive and reliable blood pressure measurement, assisting in early detection of the disease. The suggested method shows that reliable blood pressure estimation from photoplethysmography signals is possible with the use of deep learning and transfer learning. Above all, ConvNeXtTiny offers a dependable method for continuous blood pressure monitoring that satisfies clinical requirements and may help in the early identification of cardiovascular problems.