Signal, Image and Video Processing https://doi.org/10.1007/s11760-021-01952-z ORIGINAL PAPER Wavelet scattering transform and long short-term memory network-based noninvasive blood pressure estimation from photoplethysmograph signals N. Jean Effil 1,2 · R. Rajeswari 1,2 Received: 22 June 2020 / Revised: 5 October 2020 / Accepted: 29 May 2021 © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Measuring blood pressure from photoplethysmograph (PPG) signals is gaining popularity as the PPG devices are inexpensive, convenient to use and much portable. The advent of wearable PPG devices, machine learning and signal processing has motivated in the development of cuffless blood pressure calculation from PPG signals captured from fingertip. The conventional pulse transit time-based method of measuring blood pressure from PPG is inconvenient as it requires electrocardiogram signals and PPG signals or PPG signals captured simultaneously from two different sites of the body. The proposed system uses the PPG signals alone to estimate blood pressure (BP). A signal analysis method called wavelet scattering transform is applied on the preprocessed PPG signals to extract features. Predictor model that estimates BP are derived by training the support vector regression model and long short term memory prediction model. The derived models are evaluated with testing dataset and the results are compared with ground truth values. The results show that the accuracy of the proposed method achieves grade B for the estimation of the diastolic blood pressure and grade C for the mean arterial pressure under the standard British Hypertension Society protocol. On comparing the results of the proposed system with the benchmark machine learning algorithms, it is observed that the proposed model outperforms others by a considerable margin. A comparative analysis with prior studies shows that the results obtained from proposed work are comparable with existing works in the literature. Keywords Photoplethysmography · Wavelet scattering · Support vector machine · Blood pressure · LSTM · Regression 1 Introduction According to World Health Organization, presently, 1.13 bil- lion people in the world have high BP and among them less than 1 in 5 people have taken remedial measures to keep BP under control. Hypertension is one of the biggest causes of life threatening cardiovascular diseases such as stroke and heart attack. Cardiovascular disease caused 17.9 mil- lion (31%) deaths worldwide in the year 2016. Hence blood pressure needs to be checked regularly, and if found high, remedial measures such as medications, healthy diet and physical activity need to be taken to keep the blood pres- sure under control [1]. B R. Rajeswari rajeswari@buc.edu.in 1 Government Arts and Science College, Kadayanallur, Tamilnadu, India 2 Bharathiar University, Coimbatore, Tamilnadu, India Hypertension, also known as high blood pressure is a con- dition in which the blood vessels have constricted due to deposit of fats and free radicals and the individual has per- sistently ‘raised’ blood pressure. Blood is carried from the heart to all parts of the body through the blood vessels. Each time the heart beats, it pumps blood into the vessels. Blood pressure is created due to the force exerted by blood, which pushes against the blood vessel walls and the arteries, when it is pumped by the heart. The unit for measuring blood pres- sure is millimetres of mercury (“mmHg”). Blood pressure is recorded with the systolic reading followed by the diastolic reading. Systolic blood pressure (SBP) is the pressure created when the heart pumps out blood. Diastolic blood pressure (DBP) is created while the heart muscle is resting between beats and is being refilled with blood. Mean arterial pressure (MAP) refers to the average of SBP and DBP values for one cardiac cycle. The sphygmomanometer is the gold standard for measur- ing blood pressure. This is not convenient for the continuous monitoring of blood pressure and also it requires trained 123