Reconstruction of central arterial pressure waveform based on CNN-BILSTM

Xiao, Hanguang; Liu, Chang; Zhang, Banglin

doi:10.1016/j.bspc.2022.103513

Cited by 17 publications

(1 citation statement)

References 32 publications

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“…Waveform and Signal Reconstruction [26]: Signal reconstruction techniques aim to recover a continuous or high-resolution representation from sampled or quantized data. Interpolation methods, such as polynomial or spline interpolation, facilitate the reconstruction of signals for visualization or further analysis.…”

Section: Common Techniques Of Signal Processingmentioning

confidence: 99%

Recent Progresses in Signal Processing for Alzheimer's Disease Detection

Zhao

2023

Preprint

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Alzheimer's Disease (AD) is a neurodegenerative disease that is common in the elderly. This paper introduces the overview of Alzheimer's disease and the application of relevant signal processing methods in its detection. Signal processing is a technique that converts raw data into meaningful information and is widely used in the medical field. This paper lists common signal processing techniques, including Fourier transform, time-frequency analysis and statistical signal processing, and discusses their applications in the detection of Alzheimer's disease. Fourier transform can convert time domain signals into frequency domain representations, providing an effective tool for the study of EEG in Alzheimer's disease. Time-frequency analysis can perform a combined time and frequency analysis of the signal to help detect the signal characteristics of Alzheimer's disease. Statistical signal processing methods can be used to identify the features of Alzheimer's disease by building mathematical models. Finally, the challenges of Alzheimer's disease detection are discussed, including signal noise, diversity, and insufficient data volume. Through in-depth research and development of signal processing methods, it is expected to improve the accuracy and efficiency of early detection of Alzheimer's disease.

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Section: Common Techniques Of Signal Processingmentioning

confidence: 99%

Recent Progresses in Signal Processing for Alzheimer's Disease Detection

Zhao

2023

Preprint

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Aortic Pressure Waveform Estimation Based on Variational Mode Decomposition and Gated Recurrent Unit

Du,

Yang,

Sun

et al. 2024

12th Asian-Pacific Conference on Medical and Biological Engineering

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Personalized Pressure Conditions and Calibration for a Predictive Computational Model of Coronary and Myocardial Blood Flow

Montino Pelagi,

Baggiano,

Regazzoni

et al. 2024

Ann Biomed Eng

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Predictive modeling of hyperemic coronary and myocardial blood flow (MBF) greatly supports diagnosis and prognostic stratification of patients suffering from coronary artery disease (CAD). In this work, we propose a novel strategy, using only readily available clinical data, to build personalized inlet conditions for coronary and MBF models and to achieve an effective calibration for their predictive application to real clinical cases. Experimental data are used to build personalized pressure waveforms at the aortic root, representative of the hyperemic state and adapted to surrogate the systolic contraction, to be used in computational fluid-dynamics analyses. Model calibration to simulate hyperemic flow is performed in a “blinded” way, not requiring any additional exam. Coronary and myocardial flow simulations are performed in eight patients with different clinical conditions to predict FFR and MBF. Realistic pressure waveforms are recovered for all the patients. Consistent pressure distribution, blood velocities in the large arteries, and distribution of MBF in the healthy myocardium are obtained. FFR results show great accuracy with a per-vessel sensitivity and specificity of 100% according to clinical threshold values. Mean MBF shows good agreement with values from stress-CTP, with lower values in patients with diagnosed perfusion defects. The proposed methodology allows us to quantitatively predict FFR and MBF, by the exclusive use of standard measures easily obtainable in a clinical context. This represents a fundamental step to avoid catheter-based exams and stress tests in CAD diagnosis.

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Reconstruction of central arterial pressure waveform based on CNN-BILSTM

Cited by 17 publications

References 32 publications

Recent Progresses in Signal Processing for Alzheimer's Disease Detection

Recent Progresses in Signal Processing for Alzheimer's Disease Detection

Aortic Pressure Waveform Estimation Based on Variational Mode Decomposition and Gated Recurrent Unit

Personalized Pressure Conditions and Calibration for a Predictive Computational Model of Coronary and Myocardial Blood Flow

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