Schrödinger spectrum based continuous cuff-less blood pressure estimation using clinically relevant features from PPG signal and its second derivative

The presented study estimates cuff-less blood pressure (BP) from photoplethysmography (PPG) signals using multiple machine-learning (ML) models and the semi-classical signal analysis (SCSA) technique. The study proposes a novel signal reconstruction algorithm, which optimizes the semi-classical cons...

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Bibliographic Details
Published in:Computers in biology and medicine Vol. 166; p. 107558
Main Authors: Sarkar, Sayan, Ghosh, Aayushman
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.11.2023
Elsevier Limited
Subjects:
Adult
Algorithms
Bio-signal database
Blood pressure
Blood Pressure - physiology
Blood Pressure Determination - methods
Continuous BP monitoring
Datasets
Female
Humans
Hypertension
Intensive care units
Internal Medicine
Machine Learning
Male
Middle Aged
Other
Photoplethysmogram
Photoplethysmography - methods
Pulse Wave Analysis
Pulse wave decomposition
Regression algorithms
Signal analysis
Signal processing
Signal processing framework
Signal Processing, Computer-Assisted
Signal reconstruction
Signal processing framework
Regression algorithms
Bio-signal database
Pulse Wave Analysis
Photoplethysmogram
Pulse wave decomposition
Continuous BP monitoring
ISSN:0010-4825, 1879-0534, 1879-0534
Online Access:Get full text
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Summary:The presented study estimates cuff-less blood pressure (BP) from photoplethysmography (PPG) signals using multiple machine-learning (ML) models and the semi-classical signal analysis (SCSA) technique. The study proposes a novel signal reconstruction algorithm, which optimizes the semi-classical constant of the SCSA approach and eliminates the trade-off between complexity and accuracy during signal reconstruction. It predicts BP values using regression algorithms by processing reconstructed PPG signals’ spectral features, extracting clinically relevant PPG and its second derivative’s (SDPPG) morphological features. The developed method was assessed using a virtual in-silico dataset with more than 4000 subjects and the Multi-Parameter Intelligent Monitoring in Intensive Care Units (MIMIC-II) dataset. Results showed that the method attained a mean absolute error (MAE) of 5.37 and 2.96 mmHg for systolic and diastolic BP, respectively, using the CatBoost algorithm. This approach met the Association for the Advancement of Medical Instrumentation’s standard and achieved Grade A for all BP categories in the British Hypertension Society protocol. The proposed framework performs well even when applied to a combined clinically relevant database originating from MIMIC-III and the Queensland dataset. The proposed method’s performance is further evaluated in a non-clinical setting with noisy and deformed PPG signals to validate the efficacy of the SCSA method. The noise stress tests further showed that the algorithm maintained its key feature detection, signal reconstruction capability, and estimation accuracy up to a 10 dB SNR ratio. The proposed cuff-less BP estimation technique has the potential to perform well in mobile healthcare devices due to its straightforward implementation approach. •Dynamically modified SCSA algorithm to achieve superior reconstruction performance with a minimized computational load.•Combining cross-domain features to enhance the BP estimation method’s performance.•Effectiveness of the proposed algorithm in different BP categories.•Effectiveness of the proposed algorithm against (a) noisy wearable collected; and (b) artificially noise induced datasets.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2023.107558