Haemosync:A synchronisation algorithm for multimodal haemodynamic signals
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| Title: | Haemosync:A synchronisation algorithm for multimodal haemodynamic signals |
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| Authors: | Eleveld, Nick, Harmsen, Marije, Elting, Jan Willem J, Maurits, Natasha M |
| Source: | Eleveld, N, Harmsen, M, Elting, J W J & Maurits, N M 2024, 'Haemosync : A synchronisation algorithm for multimodal haemodynamic signals', Computer Methods and Programs in Biomedicine, vol. 254, 108298. https://doi.org/10.1016/j.cmpb.2024.108298 |
| Publication Year: | 2024 |
| Collection: | University of Groningen research database |
| Subject Terms: | Algorithms, Humans, Hemodynamics, Ultrasonography, Doppler, Transcranial/methods, Cerebrovascular Circulation/physiology, Signal Processing, Computer-Assisted, Blood Flow Velocity/physiology, Male, Blood Pressure, Female |
| Description: | BACKGROUND: Synchronous acquisition of haemodynamic signals is crucial for their multimodal analysis, such as dynamic cerebral autoregulation (DCA) analysis of arterial blood pressure (ABP) and transcranial Doppler (TCD)-derived cerebral blood velocity (CBv). Several technical problems can, however, lead to (varying) time-shifts between the different signals. These can be difficult to recognise and can strongly influence the multimodal analysis results. METHODS: We have developed a multistep, cross-correlation-based time-shift detection and synchronisation algorithm for multimodal pulsatile haemodynamic signals. We have developed the algorithm using ABP and CBv measurements from a dataset that contained combinations of several time-shifts. We validated the algorithm on an external dataset with time-shifts. We additionally quantitatively validated the algorithm's performance on a dataset with artificially added time-shifts, consisting of sample clock differences ranging from -0.2 to 0.2 s/min and sudden time-shifts between -4 and 4 s. The influence of superimposed noise and variation in waveform morphology on the time-shift estimation was quantified, and their influence on DCA-indices was determined. RESULTS: The instantaneous median absolute error (MedAE) between the artificially added time-shifts and the estimated time-shifts was 12 ms (median, IQR 12-12, range 11-14 ms) for drifts between -0.1 and 0.1 s/min and sudden time-shifts between -4 and 4 s. For drifts above 0.1 s/min, MedAE was higher (median 753, IQR 19 - 766, range 13 - 772 ms). When a certainty threshold was included (peak cross-correlation > 0.9), MedAE for all drifts-shift combinations decreased to 12 ms, with smaller variability (IQR 12 - 13, range 8 - 22 ms, p < 0.001). The time-shift estimation is robust to noise, as the MedAE was similar for superimposed white noise with variance equal to the signal variance. After time-shift correction, DCA-indices were similar to the original, non-time-shifted signals. Phase shift differed by 0.17° ... |
| Document Type: | article in journal/newspaper |
| File Description: | application/pdf |
| Language: | English |
| Relation: | info:eu-repo/semantics/altIdentifier/pmid/38936154; info:eu-repo/semantics/altIdentifier/hdl/https://hdl.handle.net/11370/d57c05cc-0724-47fc-8c8e-7431bc9a4ef4; info:eu-repo/semantics/altIdentifier/pissn/0169-2607; info:eu-repo/semantics/altIdentifier/eissn/1872-7565 |
| DOI: | 10.1016/j.cmpb.2024.108298 |
| Availability: | https://hdl.handle.net/11370/d57c05cc-0724-47fc-8c8e-7431bc9a4ef4 https://research.rug.nl/en/publications/d57c05cc-0724-47fc-8c8e-7431bc9a4ef4 https://doi.org/10.1016/j.cmpb.2024.108298 https://pure.rug.nl/ws/files/1069760611/1-s2.0-S0169260724002931-main.pdf https://www.scopus.com/pages/publications/85196798737 |
| Rights: | info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: | edsbas.C68A71A8 |
| Database: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | BACKGROUND: Synchronous acquisition of haemodynamic signals is crucial for their multimodal analysis, such as dynamic cerebral autoregulation (DCA) analysis of arterial blood pressure (ABP) and transcranial Doppler (TCD)-derived cerebral blood velocity (CBv). Several technical problems can, however, lead to (varying) time-shifts between the different signals. These can be difficult to recognise and can strongly influence the multimodal analysis results. METHODS: We have developed a multistep, cross-correlation-based time-shift detection and synchronisation algorithm for multimodal pulsatile haemodynamic signals. We have developed the algorithm using ABP and CBv measurements from a dataset that contained combinations of several time-shifts. We validated the algorithm on an external dataset with time-shifts. We additionally quantitatively validated the algorithm's performance on a dataset with artificially added time-shifts, consisting of sample clock differences ranging from -0.2 to 0.2 s/min and sudden time-shifts between -4 and 4 s. The influence of superimposed noise and variation in waveform morphology on the time-shift estimation was quantified, and their influence on DCA-indices was determined. RESULTS: The instantaneous median absolute error (MedAE) between the artificially added time-shifts and the estimated time-shifts was 12 ms (median, IQR 12-12, range 11-14 ms) for drifts between -0.1 and 0.1 s/min and sudden time-shifts between -4 and 4 s. For drifts above 0.1 s/min, MedAE was higher (median 753, IQR 19 - 766, range 13 - 772 ms). When a certainty threshold was included (peak cross-correlation > 0.9), MedAE for all drifts-shift combinations decreased to 12 ms, with smaller variability (IQR 12 - 13, range 8 - 22 ms, p < 0.001). The time-shift estimation is robust to noise, as the MedAE was similar for superimposed white noise with variance equal to the signal variance. After time-shift correction, DCA-indices were similar to the original, non-time-shifted signals. Phase shift differed by 0.17° ... |
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| DOI: | 10.1016/j.cmpb.2024.108298 |