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Goal: Although photoplethysmogram (PPG) and electrocardiogram (ECG) signalscan be used to estimate blood pressure (BP) by extracting various features, thechanges in morphological contours of both PPG and ECG signals due to variousdiseases of circulatory system and interaction of other physiological systemsmake the extraction of such features very difficult. Methods: In this work, wepropose a waveform-based hierarchical Artificial Neural Network - Long ShortTerm Memory (ANN-LSTM) model for BP estimation. The model consists of twohierarchy levels, where the lower hierarchy level uses ANNs to extractnecessary morphological features from ECG and PPG waveforms and the upperhierarchy level uses LSTM layers to account for the time domain variation ofthe features extracted by lower hierarchy level. Results: The proposed model isevaluated on 39 subjects using the Association for the Advancement of MedicalInstrumentations (AAMI) standard and the British Hypertension Society (BHS)standard. The method satisfies both the standards in the estimation of systolicblood pressure (SBP) and diastolic blood pressure (DBP). For the proposednetwork, the mean absolute error (MAE) and the root mean square error (RMSE)for SBP estimation are 1.10 and 1.56 mmHg, respectively, and for DBP estimationare 0.58 and 0.85 mmHg, respectively. Conclusion: The performance of theproposed hierarchical ANN-LSTM model is found to be better than the otherfeature engineering-based networks. It is shown that the proposed model is ableto automatically extract the necessary features and their time domainvariations to estimate BP reliably in a noninvasive continuous manner.Significance: The method is expected to greatly facilitate the presentlyavailable mobile health-care gadgets in continuous BP estimation.

Cuffless Blood Pressure Estimation from Electrocardiogram and Photoplethysmogram Using Waveform Based ANN-LSTM Network