Related Experiment Video
Updated: Jul 12, 2025
Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
Published on: June 27, 2025
Optimized deep neural network models for blood pressure classification using Fourier analysis-based time-frequency
Pankaj1, Ashish Kumar2, Manjeet Kumar3
1Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India.
Insights
This study estimates blood pressure (BP) from photoplethysmography (PPG) signals, even with motion artifacts. A deep neural network approach using Fourier decomposition achieved 96.5% accuracy in classifying hypertension, aiding cardiovascular disease prevention.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Artificial Intelligence
Background:
- Cardiovascular diseases (CVDs) pose a significant health risk, with hypertension being a primary cause.
- Effective blood pressure (BP) management through continuous monitoring and rapid diagnosis is crucial for CVD prevention.
- Photoplethysmography (PPG) signals offer a non-invasive method for physiological monitoring, but are susceptible to motion artifacts.
Purpose of the Study:
- To develop a real-time method for estimating BP from motion artifact-affected PPG signals.
- To propose a deep neural network (DNN) methodology for accurate PPG signal classification.
- To classify PPG signals into normotension, pre-hypertension, and hypertension categories.
Main Methods:
- Utilizing the Fourier Decomposition Method (FDM) to transform PPG signals into time-frequency (TF) spectrograms.
- Modifying the last three layers of pre-trained DNNs (GoogleNet, DenseNet, AlexNet) for PPG signal classification.
- Training and testing the framework on MIMIC-III and PPG-BP databases using a five-fold cross-validation approach.
Main Results:
- The proposed framework with the DenseNet-201 network achieved a test accuracy of 96.5%.
- FDM effectively computed TF spectrograms, separating motion artifacts and noise from PPG signals.
- DNNs trained with clean PPG signal features demonstrated improved generalized capability for real-time BP classification.
Conclusions:
- The DNN-based approach using FDM-generated TF spectrograms accurately estimates BP from noisy PPG signals.
- DenseNet-201 demonstrated superior performance in classifying BP states from PPG data.
- This method enhances the potential for real-time, non-invasive BP monitoring and hypertension screening.
Abstract:
Appropriate blood pressure (BP) management through continuous monitoring and rapid diagnosis helps to take preventive care against cardiovascular diseases (CVD). As hypertension is one of the leading causes of CVDs, keeping hypertension under control by a timely screening of subjects becomes lifesaving. This work proposes estimating BP from motion artifact-affected photoplethysmography signals (PPG) by applying signal processing techniques in realtime. This paper proposes a deep neural network-based methodology to accurately classify PPG signals using a Fourier theory-based time-frequency (TF) spectrogram. This work uses the Fourier decomposition method (FDM) to transform a PPG signal into a TF spectrogram. In the proposed work, the last three layers of the pre-trained deep neural network, namely, GoogleNet, DenseNet, and AlexNet, are modified and then used to classify the PPG signal into normotension, pre-hypertension, and hypertension. The proposed framework is trained and tested using the MIMIC-III and PPG-BP databases using five-fold training and testing. Out of the three deep neural networks, the proposed framework with the DenseNet-201 network performs best, with a test accuracy of 96.5%. The proposed work uses FDM to compute the TF spectrogram to accurately separate the motion artifacts and noise components from a noise-corrupted PPG signal. Capturing more frequency components that contain more information from PPG signals makes the deep neural networks extract better and more meaningful features. Thus, training a deep neural network model with clean PPG signal features improves the generalized capability of a BP classification model when tested in realtime.
More Related Videos
08:22Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
Published on: April 26, 2024
11:26Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
Published on: December 10, 2014
Related Concept Videos
01:18Neural Regulation of Blood Pressure
Baroreceptor Reflex
Baroreceptors, located in the carotid sinuses and aortic arch, detect changes in blood pressure. When blood pressure rises, these stretch-sensitive receptors...
01:19Assessing Blood pressure using a doppler ultrasound
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
Preparation of Equipment:
01:30Equipments Used To Measure Blood Pressure
This invasive approach involves cannulating a peripheral artery. During each cardiac contraction, pressure generates mechanical motion within the catheter, transmitted through rigid, fluid-filled tubing to a transducer. This transducer converts mechanical motion into electrical signals displayed as waveforms on a monitor. An automatic flushing system prevents blood backflow. Due to the potential risk of unexpected arterial blood loss, this method is primarily used in intensive...
01:17Measurement of Blood Pressure
01:10Pre-Procedural Guidelines for Assessing Blood Pressure