Optimized deep neural network models for blood pressure classification using Fourier analysis-based time–frequency spectrogram of photoplethysmography signal

• Published in: Biomedical engineering letters Vol. 13; no. 4; pp. 739 - 750
• Main Authors: Pankaj, Kumar, Ashish, Kumar, Manjeet, Komaragiri, Rama
• Format: Journal Article
• Language: English
• Published: Korea The Korean Society of Medical and Biological Engineering 01.11.2023; Springer Nature B.V; 대한의용생체공학회
• Subjects: Artificial neural networks; Biological and Medical Physics; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Blood pressure; Cardiovascular diseases; Classification; Engineering; Fourier analysis; Hypertension; Medical and Radiation Physics; Model testing; Neural networks; Original; Original Article; Real time; Signal classification; Signal processing; Time-frequency analysis; Training; 의공학; Hypertension; Time–frequency spectrogram; Deep learning; Transfer learning; Arterial blood pressure; Fourier decomposition method; Photoplethysmography
• DOI: 10.1007/s13534-023-00296-6
• ISSN: 2093-9868; 2093-985X

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.