Evaluation of the quality of electrocardiograms, seismocardiograms and gyrocardiograms based on characteristics derived from symmetric projection attractor reconstruction

Szymon Sieciński; Laura Pauline Scherf; Marcin Grzegorzek

doi:10.1109/IEEECONF58974.2023.10405047

Evaluation of the quality of electrocardiograms, seismocardiograms and gyrocardiograms based on characteristics derived from symmetric projection attractor reconstruction

Szymon Sieciński^*, Laura Pauline Scherf, Marcin Grzegorzek

^*Corresponding author for this work

3 Citations (Scopus)

Abstract

This study presents the evaluation of cardiovascular signals (electrocardiograms, seismocardiograms, and gyrocardiograms) based on features derived from symmetric projection attractor reconstruction (SPAR). We assessed five classifiers: random forests, bagged trees, gradient boosting, multilayer perceptron, and support vector machine. The highest accuracy was achieved for bagged trees in ECG signals (0.6667) and the lowest overall accuracy for the multilayer perceptron in SCG signals (0.3333). The results showed that five classifiers fed features derived from SPAR can be used to assess the quality of cardiovascular signals.

Original language	English
Pages	161-162
Number of pages	2
DOIs	https://doi.org/10.1109/IEEECONF58974.2023.10405047
Publication status	Published - 29.01.2024
Event	2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology - Hilton Malta, St. Julian's, Malta Duration: 07.12.2023 → 09.12.2023 https://datascience.embs.org/2023

Conference

Conference	2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology
Country/Territory	Malta
City	St. Julian's
Period	07.12.23 → 09.12.23
Internet address	https://datascience.embs.org/2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 4 Quality Education
SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production
SDG 14 Life Below Water
SDG 15 Life on Land

Research Areas and Centers

Centers: Center for Artificial Intelligence Luebeck (ZKIL)

DFG Research Classification Scheme

2.22-32 Medical Physics, Biomedical Technology
2.22-07 Medical Informatics and Medical Bioinformatics
2.22-12 Cardiology, Angiology

Access to Document

10.1109/IEEECONF58974.2023.10405047

Cite this

Sieciński, S., Scherf, L. P., & Grzegorzek, M. (2024). Evaluation of the quality of electrocardiograms, seismocardiograms and gyrocardiograms based on characteristics derived from symmetric projection attractor reconstruction. 161-162. Paper presented at 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, St. Julian's, Malta. https://doi.org/10.1109/IEEECONF58974.2023.10405047

Sieciński, Szymon ; Scherf, Laura Pauline ; Grzegorzek, Marcin. / Evaluation of the quality of electrocardiograms, seismocardiograms and gyrocardiograms based on characteristics derived from symmetric projection attractor reconstruction. Paper presented at 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, St. Julian's, Malta.2 p.

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title = "Evaluation of the quality of electrocardiograms, seismocardiograms and gyrocardiograms based on characteristics derived from symmetric projection attractor reconstruction",

abstract = "This study presents the evaluation of cardiovascular signals (electrocardiograms, seismocardiograms, and gyrocardiograms) based on features derived from symmetric projection attractor reconstruction (SPAR). We assessed five classifiers: random forests, bagged trees, gradient boosting, multilayer perceptron, and support vector machine. The highest accuracy was achieved for bagged trees in ECG signals (0.6667) and the lowest overall accuracy for the multilayer perceptron in SCG signals (0.3333). The results showed that five classifiers fed features derived from SPAR can be used to assess the quality of cardiovascular signals.",

author = "Szymon Sieci{\'n}ski and Scherf, \{Laura Pauline\} and Marcin Grzegorzek",

year = "2024",

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doi = "10.1109/IEEECONF58974.2023.10405047",

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Sieciński, S , Scherf, LP & Grzegorzek, M 2024, 'Evaluation of the quality of electrocardiograms, seismocardiograms and gyrocardiograms based on characteristics derived from symmetric projection attractor reconstruction', Paper presented at 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, St. Julian's, Malta, 07.12.23 - 09.12.23 pp. 161-162. https://doi.org/10.1109/IEEECONF58974.2023.10405047

Evaluation of the quality of electrocardiograms, seismocardiograms and gyrocardiograms based on characteristics derived from symmetric projection attractor reconstruction. / Sieciński, Szymon ; Scherf, Laura Pauline ; Grzegorzek, Marcin.
2024. 161-162 Paper presented at 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, St. Julian's, Malta.

Research output: Contribution to conference › Conference Papers › peer-review

TY - CONF

T1 - Evaluation of the quality of electrocardiograms, seismocardiograms and gyrocardiograms based on characteristics derived from symmetric projection attractor reconstruction

AU - Sieciński, Szymon

AU - Scherf, Laura Pauline

AU - Grzegorzek, Marcin

PY - 2024/1/29

Y1 - 2024/1/29

N2 - This study presents the evaluation of cardiovascular signals (electrocardiograms, seismocardiograms, and gyrocardiograms) based on features derived from symmetric projection attractor reconstruction (SPAR). We assessed five classifiers: random forests, bagged trees, gradient boosting, multilayer perceptron, and support vector machine. The highest accuracy was achieved for bagged trees in ECG signals (0.6667) and the lowest overall accuracy for the multilayer perceptron in SCG signals (0.3333). The results showed that five classifiers fed features derived from SPAR can be used to assess the quality of cardiovascular signals.

AB - This study presents the evaluation of cardiovascular signals (electrocardiograms, seismocardiograms, and gyrocardiograms) based on features derived from symmetric projection attractor reconstruction (SPAR). We assessed five classifiers: random forests, bagged trees, gradient boosting, multilayer perceptron, and support vector machine. The highest accuracy was achieved for bagged trees in ECG signals (0.6667) and the lowest overall accuracy for the multilayer perceptron in SCG signals (0.3333). The results showed that five classifiers fed features derived from SPAR can be used to assess the quality of cardiovascular signals.

UR - https://www.mendeley.com/catalogue/51558529-478b-3adc-bd5a-0314d6c3f55b/

U2 - 10.1109/IEEECONF58974.2023.10405047

DO - 10.1109/IEEECONF58974.2023.10405047

M3 - Conference Papers

SP - 161

EP - 162

T2 - 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology

Y2 - 7 December 2023 through 9 December 2023

ER -

Sieciński S , Scherf LP , Grzegorzek M. Evaluation of the quality of electrocardiograms, seismocardiograms and gyrocardiograms based on characteristics derived from symmetric projection attractor reconstruction. 2024. Paper presented at 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, St. Julian's, Malta. doi: 10.1109/IEEECONF58974.2023.10405047

Evaluation of the quality of electrocardiograms, seismocardiograms and gyrocardiograms based on characteristics derived from symmetric projection attractor reconstruction

Abstract

Conference

UN SDGs

Research Areas and Centers

DFG Research Classification Scheme

Access to Document

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