Dynamic threshold algorithm to evaluate trustworthiness of the estimated blood pressure in oscillometry

Koohi, Iraj; Ahmad, Saif; Batkin, Izmail; Groza, Voicu; Shirmohammadi, Shervin; Dajani, Hilmi R.

doi:10.1109/mim.2016.7579067

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…While the MMSA is a coefficient-free BP estimation method, it is very sensitive to noise such as motion artifacts. Dynamic Threshold Algorithm (DTA) [15] has been proposed to evaluate trustworthiness of the BP estimations. DTA employs the subject's heart rate (HR) and derives a specific threshold by which maximum and minimum limits for trustable measurements are determined.…”

Section: Introductionmentioning

confidence: 99%

Blood Pressure Estimation From Beat-by-Beat Time-Domain Features of Oscillometric Waveforms Using Deep-Neural-Network Classification Models

Argha

Wu²,

et al. 2019

IEEE Access

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In general, existing machine learning based approaches, developed for systolic and diastolic blood pressure (SBP and DBP) estimation from oscillometric waveforms (OWs), employ features extracted from the OW envelope (OWE) alone and ignore important beat-by-beat (BBB) features which represent fundamental physical properties of the entire non-invasive blood pressure (NIBP) measurement system. Unlike the existing literature, this paper proposes a novel deep-learning based method for BP estimation trained with BBB time-domain features extracted from OWs. First, we extract six time-domain features from each beat of the OW, relative to the preceding beat. Second, using the extracted BBB features along with the corresponding cuff pressures, we form a feature vector for each OW beat and locate it in one of three different classes, namely pre-systolic (PS), between systolic and diastolic (BSD) and after diastolic (AD). We then devise a deep-belief network (DBN)-deep neural network (DNN) classification model as well as a novel artificial feature extraction method for estimating SBP and DBP from feature vectors extracted from OWs and their corresponding deflation curves. The proposed DBN-DNN classification approach can effectively learn the complex nonlinear relationship between the artificial feature vectors and target classes. 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 5-fold cross-validation scheme and using a data base of 350 NIBP recordings gave an average mean absolute error of 1.1±2.9 mmHg for SBP and 3.0±5.6 mmHg for DBP relative to reference values. We experimentally show that the proposed DBN-DNN-based classification algorithm trained with BBB time-domain features can outperform traditional deep-learning based methods for BP estimation trained with features extracted only from OWEs. INDEX TERMS DBN-DNN classification model, systolic and diastolic blood pressure estimation, beat-bybeat time-domain features of oscillometric waveform.

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Section: Introductionmentioning

confidence: 99%

Blood Pressure Estimation From Beat-by-Beat Time-Domain Features of Oscillometric Waveforms Using Deep-Neural-Network Classification Models

Argha

Wu²,

et al. 2019

IEEE Access

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“…Their study employed the multiple linear regression (MLR) and the support vector regression (SVR) for the relationship discovery. Moreover, Koohi et al [ 18 ] presented a dynamic threshold algorithm to evaluate trustworthiness of the estimated blood pressure in oscillometry.…”

Section: Introductionmentioning

confidence: 99%

Development of a Blood Pressure Measurement Instrument with Active Cuff Pressure Control Schemes

Kuo

Chou

et al. 2017

Journal of Healthcare Engineering

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This paper presents an oscillometric blood pressure (BP) measurement approach based on the active control schemes of cuff pressure. Compared with conventional electronic BP instruments, the novelty of the proposed BP measurement approach is to utilize a variable volume chamber which actively and stably alters the cuff pressure during inflating or deflating cycles. The variable volume chamber is operated with a closed-loop pressure control scheme, and it is activated by controlling the piston position of a single-acting cylinder driven by a screw motor. Therefore, the variable volume chamber could significantly eliminate the air turbulence disturbance during the air injection stage when compared to an air pump mechanism. Furthermore, the proposed active BP measurement approach is capable of measuring BP characteristics, including systolic blood pressure (SBP) and diastolic blood pressure (DBP), during the inflating cycle. Two modes of air injection measurement (AIM) and accurate dual-way measurement (ADM) were proposed. According to the healthy subject experiment results, AIM reduced 34.21% and ADM reduced 15.78% of the measurement time when compared to a commercial BP monitor. Furthermore, the ADM performed much consistently (i.e., less standard deviation) in the measurements when compared to a commercial BP monitor.

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Performance Analysis of Oscillometric Blood Pressure Estimation Techniques in Cardiac Patients

Sidhu

Yamaguchi

Groza

et al. 2019

2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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Dynamic threshold algorithm to evaluate trustworthiness of the estimated blood pressure in oscillometry

Cited by 6 publications

References 23 publications

Blood Pressure Estimation From Beat-by-Beat Time-Domain Features of Oscillometric Waveforms Using Deep-Neural-Network Classification Models

Blood Pressure Estimation From Beat-by-Beat Time-Domain Features of Oscillometric Waveforms Using Deep-Neural-Network Classification Models

Development of a Blood Pressure Measurement Instrument with Active Cuff Pressure Control Schemes

Performance Analysis of Oscillometric Blood Pressure Estimation Techniques in Cardiac Patients

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