A Novel Automated Blood Pressure Estimation Algorithm Using Sequences of Korotkoff Sounds

The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as they can be used without expertise and BP measurement can be performed by patients at home. Non-invasive cuff-based monitoring is the dominant method for BP measurement. While the oscillometric technique is most common, a few automated NIBP measurement methods have been developed based on the auscultatory technique. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification and feature extraction problems. This paper proposes a novel automated AI-based technique for NIBP estimation from auscultatory waveforms (AWs) based on converting the NIBP estimation problem to a sequence-to-sequence classification problem. To do this, a sequence of segments was first formed by segmenting the AWs and their corresponding decomposed detail and approximation parts obtained by wavelet packet decomposition method, and extracting features from each segment. Then, a label was assigned to each segment, i.e. (i) between systolic and diastolic segments and (ii) otherwise, and a bidirectional long short term memory recurrent neural network (BiLSTM-RNN) was devised to solve the resulting sequence-to-sequence classification problem. Adopting a 5-fold cross-validation scheme and using a data base of 350 NIBP recordings gave an average mean absolute error of 1.7 3.7 mmHg for systolic BP (SBP) and 3.4 5.0 mmHg for diastolic BP (DBP) relative to reference values. Based on the results achieved and comparisons made with the existing literature, it is concluded that the proposed automated BP estimation algorithm based on deep learning methods and auscultatory waveform brings plausible benefits to the field of BP estimation.