Sensors · 2018 · 68 citations · 39 references
EngineeringBioengineeringBiomedical EngineeringBioinstrumentationBiomedical Signal ProcessingPhysiological Systems ModelingBlood Pressure Signal AnalysisCardiovascular AssessmentCardiovascular MonitoringPressure MeasurementPressure SensorsVital SignsBlood PressureBlood Pressure EstimationBlood Pressure MeasurementCuffless Blood Pressure MeasurementDiastolic Blood Pressure EstimationLinear RegressionSystolic Blood PressureSystolic Blood Pressure Estimation
Pulse transit time (PTT) has received considerable attention for noninvasive cuffless blood pressure measurement. However, this approach is inconvenient to deploy in wearable devices because two sensors are required for collecting two-channel physiological signals, such as electrocardiogram and pulse wave signals. In this study, we investigated the pressure pulse wave (PPW) signals collected from one piezoelectric-induced sensor located at a single site for cuffless blood pressure estimation. Twenty-one features were extracted from PPW that collected from the radial artery, and then a linear regression method was used to develop blood pressure estimation models by using the extracted PPW features. Sixty-five middle-aged and elderly participants were recruited to evaluate the performance of the constructed blood pressure estimation models, with oscillometric technique-based blood pressure as a reference. The experimental results indicated that the mean ± standard deviation errors for the estimated systolic blood pressure and diastolic blood pressure were 0.70 ± 7.78 mmHg and 0.83 ± 5.45 mmHg, which achieved a decrease of 1.33 ± 0.37 mmHg in systolic blood pressure and 1.14 ± 0.20 mmHg in diastolic blood pressure, compared with the conventional PTT-based method. The proposed model also demonstrated a high level of robustness in a maximum 60-day follow-up study. These results indicated that PPW obtained from the piezoelectric sensor has great feasibility for cuffless blood pressure estimation, and could serve as a promising method in home healthcare settings.
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Takao Someya, Tsuyoshi Sekitani, Shingo Iba et al. · Proceedings of the National Academy of Sciences · 2004 · 1.9K citations · Full text
Statistics Notes: Measurement error
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Health Professions, Biostatistics, Individual Differences +16
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