Robust Arm Impedocardiography Signal Quality Enhancement Using Recursive Signal Averaging and Multi-Stage Wavelet Denoising Methods for Long-Term Cardiac Contractility Monitoring Armbands

Omar Escalona, Nicole Cullen, Idongesit Weli, Niamh McCallan, Kok Yew Ng, Dewar Finlay

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
36 Downloads (Pure)

Abstract

Impedance cardiography (ICG) is a low-cost, non-invasive technique that enables the clinical assessment of haemodynamic parameters, such as cardiac output and stroke volume (SV). Conventional ICG recordings are taken from the patient’s thorax. However, access to ICG vital signs from the upper-arm brachial artery (as an associated surrogate) can enable user-convenient wearable armband sensor devices to provide an attractive option for gathering ICG trend-based indicators of general health, which offers particular advantages in ambulatory long-term monitoring settings. This study considered the upper arm ICG and control Thorax-ICG recordings data from 15 healthy subject cases. A prefiltering stage included a third-order Savitzky–Golay finite impulse response (FIR) filter, which was applied to the raw ICG signals. Then, a multi-stage wavelet-based denoising strategy on a beat-by-beat (BbyB) basis, which was supported by a recursive signal-averaging optimal thresholding adaptation algorithm for Arm-ICG signals, was investigated for robust signal quality enhancement. The performance of the BbyB ICG denoising was evaluated for each case using a 700 ms frame centred on the heartbeat ICG pulse. This frame was extracted from a 600-beat ensemble signal-averaged ICG and was used as the noiseless signal reference vector (gold standard frame). Furthermore, in each subject case, enhanced Arm-ICG and Thorax-ICG above a threshold of correlation of 0.95 with the noiseless vector enabled the analysis of beat inclusion rate (BIR%), yielding an average of 80.9% for Arm-ICG and 100% for Thorax-ICG, and BbyB values of the ICG waveform feature metrics A, B, C and VET accuracy and precision, yielding respective error rates (ER%) of 0.83%, 11.1%, 3.99% and 5.2% for Arm-IG, and 0.41%, 3.82%, 1.66% and 1.25% for Thorax-ICG, respectively. Hence, the functional relationship between ICG metrics within and between the arm and thorax recording modes could be characterised and the linear regression (Arm-ICG vs. Thorax-ICG) trends could be analysed. Overall, it was found in this study that recursive averaging, set with a 36 ICG beats buffer size, was the best Arm-ICG BbyB denoising process, with an average of less than 3.3% in the Arm-ICG time metrics error rate. It was also found that the arm SV versus thorax SV had a linear regression coefficient of determination (R2) of 0.84.
Original languageEnglish
Article number5892
Pages (from-to)1-30
Number of pages30
JournalSensors
Volume23
Issue number13
Early online date25 Jun 2023
DOIs
Publication statusPublished online - 25 Jun 2023

Bibliographical note

Funding Information:
Partly funded (for equipment and accessories) by the Eastern Corridor Medical Engineering Centre (ECME) Project grant from the European Union’s INTERREG VA program (grant ID: IVA5034), which is managed by the Special EU Programs Body (SEUPB).

Publisher Copyright:
© 2023 by the authors.

Keywords

  • armband ICG sensing methods
  • impedance cardiography
  • Arm-ICG signal enhancement
  • recursive signal averaging
  • thorax impedocardiography
  • brachial-artery-based ICG surrogate
  • ambulatory hemodynamics
  • heart contractility monitoring
  • two-stage Daubechies wavelet denoising
  • arm stroke volume
  • signal-averaged ICG

Fingerprint

Dive into the research topics of 'Robust Arm Impedocardiography Signal Quality Enhancement Using Recursive Signal Averaging and Multi-Stage Wavelet Denoising Methods for Long-Term Cardiac Contractility Monitoring Armbands'. Together they form a unique fingerprint.

Cite this