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Cardiovascular disease is known as the number one cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. 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, some automated NIBP measurement methods have been developed based on the auscultatory technique. By utilizing (relatively) large BP data annotated by experts, models can be trained using machine learning and statistical concepts to develop novel NIBP estimation algorithms. 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 reviews AI-based BP estimation methods with a focus on recent advances in deep learning-based approaches within the field. Various architectures and methodologies proposed todate are discussed to clarify their strengths and weaknesses. Based on the literature reviewed, deep learning brings plausible benefits to the field of BP estimation. We also discuss some limitations which can hinder the widespread adoption of deep learning in the field and suggest frameworks to overcome these challenges. [ABSTRACT FROM AUTHOR]

It is well known that non-invasive blood pressure measurements significantly underestimate true systolic blood pressure (SBP), and overestimate diastolic blood pressure (DBP). The aetiology for these errors has not yet been fully established. This study aimed to investigate the accuracy of Korotkoff sounds for detection of SBP and DBP points as used in brachial cuff sphygmomanometry. Brachial cuff pressure and Korotkoff sounds were obtained in 11 patients (6 males: 69.0 ± 6.2 years, 5 females: 71.8 ± 5.5 years) undergoing diagnostic coronary angiography. K2 Korotkoff sounds were obtained by high-pass filtering (>20 Hz) the microphone-recorded signal to eliminate low frequency components. Analysis of the timing of K2 Korotkoff sounds relative to cuff pressure and intra-arterial pressure shows that the onset of K2 Korotkoff sounds reliably detect the start of blood flow under the brachial cuff and their termination, marks the cuff pressure closely coincident with DBP. We have made the critical observation that blood flow under the cuff does not begin when cuff pressure falls just below SBP as is conventionally assumed, and that the delay in the opening of the artery following occlusion, and the consequent delay in the generation of K2 Korotkoff sounds, may lead to significant errors in the determination of SBP of up to 24 mmHg. Our data suggest a potential role of arterial stiffness as a major component of the errors recorded, with underestimation of SBP much more significant for subjects with stiff arteries than for subjects with more compliant arteries. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article presents a novel method for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) from time-domain features extracted from oscillometric waveforms (OWs) using a long short-term memory (LSTM) recurrent neural network (RNN) method. First, we extract seven time-domain features from each cycle of OW, including the cuff pressure, the cardiac period, the trough-to-peak amplitude of OW, the time between the trough and the peak of the OW, the slopes of the oscillometric waveform envelope (OWE), and the maximum upslope of individual OWs. Second, we locate each feature vector in an noninvasive blood pressure (NIBP) record in one of three different phases (classes), namely, presystolic (PS), between systolic and diastolic (BSD), and after diastolic (AD), and form a target sequence. Then, we propose an LSTM-RNN approach to effectively learn the complex nonlinear relationship between the feature vector sequences and the target sequence. The SBP and DBP points are then obtained by mapping the beats at which the network output sequence switches from PS phase to BSD phase and from BSD phase to AD phase, respectively, to the deflation curve. Adopting a tenfold cross-validation scheme and using a database of 350 NIBP recordings gave an average mean error of −1.2 ± 5.9 mmHg for SBP and 1.8 ± 8.8 mmHg for DBP relative to reference values derived from a visual method of determining SBP and DBP. The proposed RNN-based approach uses all time-domain features available from each NIBP recording and can outperform traditional methods in blood pressure (BP) estimation. [ABSTRACT FROM AUTHOR]

This article presents a novel method of estimating systolic blood pressure (SBP) and diastolic BP (DBP) from time-domain features extracted from auscultatory and oscillometric waveforms and using Gaussian Mixture Models and Hidden Markov Model (GMM-HMM). The nine time-domain features selected include the cuff pressure (CP), the cardiac period (T), the energy of the Korotkoff pulses (KE), the oscillometric waveform envelope (OWE), the lag between the trough of the oscillometric waveforms (OWs) and the peak of the Korotkoff energy (Lag), the time between the trough and the peak of the OW (OWD), the slopes of the KE and OWE (SKE, SOWE), and the maximum upslope of individual OWs (MSOW). Adopting a fivefold cross-validation scheme and using a database of 350 noninvasive BP (NIBP) recordings gave an average mean error (± standard deviation of error) of −0.3 ± 4.2 mmHg for SBP and 2.9 ± 8.1 mmHg for DBP relative to reference values derived from a visual method of determining SBP and DBP. The significantly larger spread of DBP estimates relative to SBP, suggests that the criteria for determining DBP are poorly defined and would benefit from further experimental studies involving simultaneous invasive and noninvasive methods of measuring arterial pressure. We conclude that the proposed GMM-HMM BP estimation method outperforms previously reported methods in the literature and is a very promising method in improving the accuracy of automated noninvasive measurement of BP. [ABSTRACT FROM AUTHOR]