Deep learning-based robust automatic non-invasive measurement of blood pressure using Korotkoff sounds

Abstract

This paper proposes a method that automatically measures non-invasive blood pressure (BP) based on an auscultatory approach using Korotkoff sounds (K-sounds). There have been methods utilizing K-sounds that were more accurate in general than those using cuff pressure signals only under well-controlled environments, but most were vulnerable to the measurement conditions and to external noise because blood pressure is simply determined based on threshold values in the sound signal. The proposed method enables robust and precise BP measurements by evaluating the probability that each sound pulse is an audible K-sound based on a deep learning using a convolutional neural network (CNN). Instead of classifying sound pulses into two categories, audible K-sounds and others, the proposed CNN model outputs probability values. These values in a Korotkoff cycle are arranged in time order, and the blood pressure is determined. The proposed method was tested with a dataset acquired in practice that occasionally contains considerable noise, which can degrade the performance of the threshold-based methods. The results demonstrate that the proposed method outperforms a previously reported CNN-based classification method using K-sounds. With larger amounts of various types of data, the proposed method can potentially achieve more precise and robust results.

Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives

“…One of the oldest methods that has been the gold standard in clinics for BP measurements in NIBP is to use the auscultatory method [ 157 ], where a cuff is wrapped around a patient’s upper arm and the observer listens to K-sounds, also known as Korotkoff sounds, using a stethoscope and this principle has been applied to automatic BP systems [ 158 , 159 ]. However, a lot of studies have shown that external noises and personnel differences create differences in the BP values [ 160 , 161 , 162 , 163 ].…”

Korotkoff sounds dynamically reflect changes in cardiac function based on deep learning methods

“…The CNN was initially proposed for processing images, speech, and time series (2). Guided by the CNN, BP measurements using K-sounds have better accuracy, compared to the automatic oscillometric method.…”

“…Pan et al used a 10.3389/fcvm.2022.940615 DL method to assess variations in K-sounds (46). Chang et al improved on the former and added the ResNet module for automatic identification of the association between pulse oscillation waves and K-sounds (2). The filtered signal stack can also be used as a picture input.…”

“…Blood pressure (BP) measurement using Korotkoff sounds (K-sounds), which are considered the gold standard for BP measurement, has been performed for over 100 years (1). When done by a trained clinical practitioner, the K-sounds approach may yield more accurate results than the automated oscillometric method (2). Accurate BP measurement facilitates daily monitoring of an individual's vital signs, while inaccurate results can cause unnecessary panic, as a 5-mmHg error can halve or double the number of hypertensive patients (2,3).…”

Blood Pressure Estimation From Korotkoff Sound Signals Using an End-to-End Deep-Learning-Based Algorithm

“…A possible solution is to use AI, and especially deep learning, methods. AI-based methods proposed for NIBP estimation from KSs can be summarized as: (1) methods that ignore the temporal dependencies between the segments derived from KSs [27]- [29] and classify each pulse independent of others; (2) methods that consider the digitized KSs as time series, and estimate BP from sequences of KSs through dealing with a sequence-to-sequence (seq-toseq) classification problem using classification algorithms such as HMM [15], [30] and LSTM-RNN [31].…”