  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.
