Diagnostic biomarkers for Alzheimer's disease using dynamic nonlinear models based on principal dynamic modes

V. Z. Marmarelis; D. C. Shin; R. Diaz-Arrastia; R. Zhang

doi:10.1109/IEMBS.2011.6091799

Diagnostic biomarkers for Alzheimer's disease using dynamic nonlinear models based on principal dynamic modes

V. Z. Marmarelis, D. C. Shin, R. Diaz-Arrastia, R. Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Sensitive and robust diagnostic biomarkers for Alzheimer's disease (AD) were sought using dynamic nonlinear models of the causal interrelationships among time-series (beat-to-beat) data of arterial blood pressure, end-tidal CO ₂ and cerebral blood flow velocity collected in human subjects (4 AD patients and 4 control subjects). These models were based on Principal Dynamic Modes (PDM) and yielded a reliable biomarker for AD diagnosis in the form of the Effective CO ₂ Reactivity Index (ECRI). The results from this initial set of subjects corroborated the efficacy of the ECRI biomarker for accurate AD diagnosis.

Original language	English (US)
Title of host publication	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Pages	7119-7122
Number of pages	4
DOIs	https://doi.org/10.1109/IEMBS.2011.6091799
State	Published - 2011
Event	33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 - Boston, MA, United States Duration: Aug 30 2011 → Sep 3 2011

Other

Other	33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Country/Territory	United States
City	Boston, MA
Period	8/30/11 → 9/3/11

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Signal Processing
Biomedical Engineering
Health Informatics

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10.1109/IEMBS.2011.6091799

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Marmarelis, V. Z., Shin, D. C., Diaz-Arrastia, R., & Zhang, R. (2011). Diagnostic biomarkers for Alzheimer's disease using dynamic nonlinear models based on principal dynamic modes. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 7119-7122). Article 6091799 https://doi.org/10.1109/IEMBS.2011.6091799

Diagnostic biomarkers for Alzheimer's disease using dynamic nonlinear models based on principal dynamic modes. / Marmarelis, V. Z.; Shin, D. C.; Diaz-Arrastia, R. et al.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2011. p. 7119-7122 6091799.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Marmarelis, VZ, Shin, DC, Diaz-Arrastia, R & Zhang, R 2011, Diagnostic biomarkers for Alzheimer's disease using dynamic nonlinear models based on principal dynamic modes. in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS., 6091799, pp. 7119-7122, 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011, Boston, MA, United States, 8/30/11. https://doi.org/10.1109/IEMBS.2011.6091799

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abstract = "Sensitive and robust diagnostic biomarkers for Alzheimer's disease (AD) were sought using dynamic nonlinear models of the causal interrelationships among time-series (beat-to-beat) data of arterial blood pressure, end-tidal CO 2 and cerebral blood flow velocity collected in human subjects (4 AD patients and 4 control subjects). These models were based on Principal Dynamic Modes (PDM) and yielded a reliable biomarker for AD diagnosis in the form of the Effective CO 2 Reactivity Index (ECRI). The results from this initial set of subjects corroborated the efficacy of the ECRI biomarker for accurate AD diagnosis.",

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AB - Sensitive and robust diagnostic biomarkers for Alzheimer's disease (AD) were sought using dynamic nonlinear models of the causal interrelationships among time-series (beat-to-beat) data of arterial blood pressure, end-tidal CO 2 and cerebral blood flow velocity collected in human subjects (4 AD patients and 4 control subjects). These models were based on Principal Dynamic Modes (PDM) and yielded a reliable biomarker for AD diagnosis in the form of the Effective CO 2 Reactivity Index (ECRI). The results from this initial set of subjects corroborated the efficacy of the ECRI biomarker for accurate AD diagnosis.

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Diagnostic biomarkers for Alzheimer's disease using dynamic nonlinear models based on principal dynamic modes

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