# Deep Learning Classification of Photoplethysmogram Signal for Hypertension Levels

arXiv: https://arxiv.org/abs/2405.14556 · HTML: https://mlsanity.com/paper/2405.14556/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 98 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 6 of 6)

- [FPGA-based Accelerators of Deep Learning Networks for Learning and Classification: A Review](https://mlsanity.com/paper/1901.00121/) (2019)
- [Quantization and Deployment of Deep Neural Networks on Microcontrollers](https://mlsanity.com/paper/2105.13331/) (2021)
- [Analysis of CNN-based remote-PPG to understand limitations and sensitivities](https://mlsanity.com/paper/1911.02736/) (2019)
- [Cuffless Blood Pressure Estimation from Electrocardiogram and Photoplethysmogram Using Waveform Based ANN-LSTM Network](https://mlsanity.com/paper/1811.02214/) (2018)
- [A Deep Learning Approach to Predict Blood Pressure from PPG Signals](https://mlsanity.com/paper/2108.00099/) (2021)
- [End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables](https://mlsanity.com/paper/1807.10707/) (2018)

## Abstract (arXiv)

Continuous photoplethysmography (PPG)-based blood pressure monitoring is necessary for healthcare and fitness applications. In Artificial Intelligence (AI), signal classification levels with the machine and deep learning arrangements need to be explored further. Techniques based on time-frequency spectra, such as Short-time Fourier Transform (STFT), have been used to address the challenges of motion artifact correction. Therefore, the proposed study works with PPG signals of more than 200 patients (650+ signal samples) with hypertension, using STFT with various Neural Networks (Convolution Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), followed by machine learning classifiers, such as, Support Vector Machine (SVM) and Random Forest (RF). The classification has been done for two categories: Prehypertension (normal levels) and Hypertension (includes Stage I and Stage II). Various performance metrics have been obtained with two batch sizes of 3 and 16 for the fusion of the neural networks. With precision and specificity of 100% and recall of 82.1%, the LSTM model provides the best results among all combinations of Neural Networks. However, the maximum accuracy of 71.9% is achieved by the LSTM-CNN model. Further stacked Ensemble method has been used to achieve 100% accuracy for Meta-LSTM-RF, Meta- LSTM-CNN-RF and Meta- STFT-CNN-SVM.