A new method for automated blood pressure measurement

Regueiro-Gómez, Ángel; Pallás-Areny, R.

doi:10.1088/0967-3334/19/2/008

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…Rogueiro-Gómez and Pallás-Areny [10] were able to use the spectral energy dispersion ratio to determine systolic and diastolic blood pressure with high precision: in 97% of all cases, the determined values for 15 persons were within ±1 heart beat.…”

Section: Auscultatory Methodsmentioning

confidence: 99%

Noninvasive Blood Pressure Monitor Designed for Patients With Heart Failure Supported with Continuous-Flow Left Ventricular Assist Devices

et al. 2019

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The gold standard for noninvasive blood pressure (BP) measurement, the Doppler technique, does not provide systolic blood pressure (SBP) and diastolic blood pressure (DBP) and may limit therapy outcomes. To improve patient care, we tested specifically designed experimental BP (ExpBP) monitor and the Doppler technique by comparing noninvasive measures to the intraarterial (I-A) BP in 31 patients with end-stage heart failure (4 females) 2.6 ± 3.4 days post-LVAD implantation (20 HeartMate II and 11 HeartWare). Bland-Altman plots revealed that the ExpBP monitor overestimated mean arterial pressure (MAP) by 1.2 (4.8) mm Hg (mean difference [standard deviation]), whereas the Doppler by 6.7 (5.8) mm Hg. The ExpBP SBP was overestimated by 0.8 (6.1) mm Hg and DBP by 1.9 (5.3) mm Hg compared with the respective I-A pressures. Both techniques achieved similar measurement reliability. In the measurement "success rate" expressed as a frequency (percent) of readable BP values per measurement attempts, Doppler accomplished 100% vs. 97%, 97%, and 94% of successful detections of MAP, SBP, and DBP provided by the ExpBP monitor. The ExpBP monitor demonstrated higher accuracy in the MAP assessment than the Doppler in addition to providing SBP and DBP in majority of subjects. Improved BP control may help to mitigate related neurologic adverse event rates.

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Section: Auscultatory Methodsmentioning

confidence: 99%

Noninvasive Blood Pressure Monitor Designed for Patients With Heart Failure Supported with Continuous-Flow Left Ventricular Assist Devices

et al. 2019

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“…(11,12) Because of the incapability of highly accurate BP estimation of oscillometric technique, researchers have attempted to employ KorS on automatic BP measurements. (13)(14)(15)(16)(17) Determining audible or inaudible stethoscope sound accurately is the key to achieve accurate BP measurement, and most reported methods were based on traditional technology (e.g., time-frequency analysis or power spectrum analysis), which are easily influenced by measurement conditions. Hong et al proposed an advanced signal processing method to convert the main frequencies of KorS into sound pressure levels that were then mapped to equal loudness contours for BP determination.…”

Section: Introductionmentioning

confidence: 99%

A novel deep learning based automatic auscultatory method to measure blood pressure

Pan

Chen

et al. 2019

International Journal of Medical Informatics

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Background: It is clinically important to develop innovative techniques that can accurately measure blood pressures (BP) automatically. Objectives: This study aimed to present and evaluate a novel automatic BP measurement method based on deep learning method, and to confirm the effects on measured BPs of the position and contact pressure of stethoscope. Methods: 30 healthy subjects were recruited. 9 BP measurements (from three different stethoscope contact pressures and three repeats) were performed on each subject. The convolutional neural network (CNN) was designed and trained to identify the Korotkoff sounds at a beat-by-beat level. Next, a mapping algorithm was developed to relate the identified Korotkoff beats to the corresponding cuff pressures for systolic and diastolic BP (SBP and DBP) determinations. Its performance was evaluated by investigating the effects of the position and contact pressure of stethoscope on measured BPs in comparison with reference manual auscultatory method. Results: The overall measurement errors of the proposed method were 1.4±2.4 mmHg for SBP and 3.3±2.9 mmHg for DBP from all the measurements. In addition, the method demonstrated that there were small SBP differences between the 2 stethoscope positions, respectively at the 3 stethoscope contact pressures, and that DBP from the stethoscope under the cuff was significantly lower than that from outside the cuff by 2.0 mmHg (P < 0.01). Conclusion: Our findings suggested that the deep learning based method was an effective technique to measure BP, and could be developed further to replace the current oscillometric based automatic blood pressure measurement method.

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“…Most of these methods are based on the thresholds of processed values from acoustic signals, showing good results under well-controlled conditions. For example, Regueiro-Gomez and Pallas-Areny defined an indicator called the ratio of spectral energy dispersion (RSED) and detected peaks that have the maximum RSED 7 . Sebald et al calculated intensity of individual pulses, and 50% and 30% of the plateau were determined correspondingly as the SBP and the DBP 8 .…”

Section: Introductionmentioning

confidence: 99%

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

Chang

Doh

2021

Sci Rep

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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.

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A new method for automated blood pressure measurement

Cited by 8 publications

References 6 publications

Noninvasive Blood Pressure Monitor Designed for Patients With Heart Failure Supported with Continuous-Flow Left Ventricular Assist Devices

Noninvasive Blood Pressure Monitor Designed for Patients With Heart Failure Supported with Continuous-Flow Left Ventricular Assist Devices

A novel deep learning based automatic auscultatory method to measure blood pressure

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

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