Acute Stress Data-Based Fast Biometric System Using Contrastive Learning and Ultra-Short ECG Signal Segments

Rajdeep K Nath; Jaakko Tervonen; Johanna Närväinen; Kati Pettersson; Jani Mäntyjärvi

doi:10.1145/3594739.3612878

Acute Stress Data-Based Fast Biometric System Using Contrastive Learning and Ultra-Short ECG Signal Segments

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

5 Citations (Scopus)

Abstract

This paper presents a novel approach of an ECG-based mental health biometric system that relies on ultra-short duration (2 seconds) of one-channel ECG signal segments from acute stress data for accurate user identification and authentication. The proposed method uses a simple framework for contrastive learning (SimCLR) to train the user identification and authentication models. The performance of the proposed ECG-based biometric system was evaluated for a single-session use case using an in-house dataset. The dataset consisted of ECG signals acquired during a study protocol designed to induce physical and mental stress. The proposed biometric system was able to achieve an accuracy of 98% for user identification and an equal error rate (EER) of 0.02 when trained and tested with a balanced condition with stress and baseline/recovery. Our proposed system was able to retain its accuracy to 95% and the EER to 0.05 even when the training size was significantly reduced.

Original language	English
Title of host publication	Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing, UbiComp/ISWC ’23
Publisher	Association for Computing Machinery ACM
Pages	642-647
Number of pages	6
ISBN (Electronic)	979-8-4007-0200-6
DOIs	https://doi.org/10.1145/3594739.3612878
Publication status	Published - 8 Oct 2023
MoE publication type	A4 Article in a conference publication
Event	2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing, UbiComp/ISWC ’23 - Cancun, Mexico Duration: 8 Oct 2023 → 12 Oct 2023

Conference

Conference	2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing, UbiComp/ISWC ’23
Country/Territory	Mexico
City	Cancun
Period	8/10/23 → 12/10/23

Funding

The work was funded by the Academy of Finland under GrantNos.: 334092, 313401, 351282 and VTT.

Keywords

Biometrics
Contrastive Learning
Acute stress
Authentication
Identification
Mental Health
Electrocardiogram (ECG)

Access to Document

10.1145/3594739.3612878Licence: CC BY

Cite this

Nath, R. K., Tervonen, J., Närväinen, J., Pettersson, K., & Mäntyjärvi, J. (2023). Acute Stress Data-Based Fast Biometric System Using Contrastive Learning and Ultra-Short ECG Signal Segments. In Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing, UbiComp/ISWC ’23 (pp. 642-647). Association for Computing Machinery ACM. https://doi.org/10.1145/3594739.3612878

Nath, Rajdeep K ; Tervonen, Jaakko ; Närväinen, Johanna et al. / Acute Stress Data-Based Fast Biometric System Using Contrastive Learning and Ultra-Short ECG Signal Segments. Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing, UbiComp/ISWC ’23. Association for Computing Machinery ACM, 2023. pp. 642-647

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title = "Acute Stress Data-Based Fast Biometric System Using Contrastive Learning and Ultra-Short ECG Signal Segments",

abstract = "This paper presents a novel approach of an ECG-based mental health biometric system that relies on ultra-short duration (2 seconds) of one-channel ECG signal segments from acute stress data for accurate user identification and authentication. The proposed method uses a simple framework for contrastive learning (SimCLR) to train the user identification and authentication models. The performance of the proposed ECG-based biometric system was evaluated for a single-session use case using an in-house dataset. The dataset consisted of ECG signals acquired during a study protocol designed to induce physical and mental stress. The proposed biometric system was able to achieve an accuracy of 98\% for user identification and an equal error rate (EER) of 0.02 when trained and tested with a balanced condition with stress and baseline/recovery. Our proposed system was able to retain its accuracy to 95\% and the EER to 0.05 even when the training size was significantly reduced.",

keywords = "Biometrics, Contrastive Learning, Acute stress, Authentication, Identification, Mental Health, Electrocardiogram (ECG)",

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note = "2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing \& the 2023 ACM International Symposium on Wearable Computing, UbiComp/ISWC {\textquoteright}23 ; Conference date: 08-10-2023 Through 12-10-2023",

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Nath, RK , Tervonen, J , Närväinen, J , Pettersson, K & Mäntyjärvi, J 2023, Acute Stress Data-Based Fast Biometric System Using Contrastive Learning and Ultra-Short ECG Signal Segments. in Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing, UbiComp/ISWC ’23. Association for Computing Machinery ACM, pp. 642-647, 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing, UbiComp/ISWC ’23, Cancun, Mexico, 8/10/23. https://doi.org/10.1145/3594739.3612878

Acute Stress Data-Based Fast Biometric System Using Contrastive Learning and Ultra-Short ECG Signal Segments. / Nath, Rajdeep K ; Tervonen, Jaakko ; Närväinen, Johanna et al.
Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing, UbiComp/ISWC ’23. Association for Computing Machinery ACM, 2023. p. 642-647.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Acute Stress Data-Based Fast Biometric System Using Contrastive Learning and Ultra-Short ECG Signal Segments

AU - Nath, Rajdeep K

AU - Tervonen, Jaakko

AU - Närväinen, Johanna

AU - Pettersson, Kati

AU - Mäntyjärvi, Jani

PY - 2023/10/8

Y1 - 2023/10/8

N2 - This paper presents a novel approach of an ECG-based mental health biometric system that relies on ultra-short duration (2 seconds) of one-channel ECG signal segments from acute stress data for accurate user identification and authentication. The proposed method uses a simple framework for contrastive learning (SimCLR) to train the user identification and authentication models. The performance of the proposed ECG-based biometric system was evaluated for a single-session use case using an in-house dataset. The dataset consisted of ECG signals acquired during a study protocol designed to induce physical and mental stress. The proposed biometric system was able to achieve an accuracy of 98% for user identification and an equal error rate (EER) of 0.02 when trained and tested with a balanced condition with stress and baseline/recovery. Our proposed system was able to retain its accuracy to 95% and the EER to 0.05 even when the training size was significantly reduced.

AB - This paper presents a novel approach of an ECG-based mental health biometric system that relies on ultra-short duration (2 seconds) of one-channel ECG signal segments from acute stress data for accurate user identification and authentication. The proposed method uses a simple framework for contrastive learning (SimCLR) to train the user identification and authentication models. The performance of the proposed ECG-based biometric system was evaluated for a single-session use case using an in-house dataset. The dataset consisted of ECG signals acquired during a study protocol designed to induce physical and mental stress. The proposed biometric system was able to achieve an accuracy of 98% for user identification and an equal error rate (EER) of 0.02 when trained and tested with a balanced condition with stress and baseline/recovery. Our proposed system was able to retain its accuracy to 95% and the EER to 0.05 even when the training size was significantly reduced.

KW - Biometrics

KW - Contrastive Learning

KW - Acute stress

KW - Authentication

KW - Identification

KW - Mental Health

KW - Electrocardiogram (ECG)

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DO - 10.1145/3594739.3612878

M3 - Conference article in proceedings

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BT - Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing, UbiComp/ISWC ’23

PB - Association for Computing Machinery ACM

T2 - 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing, UbiComp/ISWC ’23

Y2 - 8 October 2023 through 12 October 2023

ER -

Nath RK , Tervonen J , Närväinen J , Pettersson K , Mäntyjärvi J. Acute Stress Data-Based Fast Biometric System Using Contrastive Learning and Ultra-Short ECG Signal Segments. In Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing, UbiComp/ISWC ’23. Association for Computing Machinery ACM. 2023. p. 642-647 doi: 10.1145/3594739.3612878

Acute Stress Data-Based Fast Biometric System Using Contrastive Learning and Ultra-Short ECG Signal Segments

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