ArticleFrontiers in psychology2018
Biometric and Emotion Identification: An ECG Compression Based Method.
Article in Frontiers in psychology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Eye Tracking Technology in Aviation, Maritime, and Construction Industries: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Emotional recognition technologies applied to health: review and challenges.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2025Review
- Extraction of compression indices from maternal-fetal heart rate simultaneous signals.PloS one · 2025Article
- Encoding of multi-modal emotional information via personalized skin-integrated wireless facial interface.Nature communications · 2024Article
- Electrocardiogram Biometrics Using Transformer's Self-Attention Mechanism for Sequence Pair Feature Extractor and Flexible Enrollment Scope Identification.Sensors (Basel, Switzerland) · 2022Article
- Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare-A Review.Sensors (Basel, Switzerland) · 2021Review
- On the Impact of the Data Acquisition Protocol on ECG Biometric Identification.Sensors (Basel, Switzerland) · 2021Article
- The Analysis of Emotion Authenticity Based on Facial Micromovements.Sensors (Basel, Switzerland) · 2021Article
- Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings.Frontiers in human neuroscience · 2021Article
- Multimodal Emotion Evaluation: A Physiological Model for Cost-Effective Emotion Classification.Sensors (Basel, Switzerland) · 2020Article
- Consumers Emotional Responses to Functional and Hedonic Products: A Neuroscience Research.Frontiers in psychology · 2020Article
- A Review on Automatic Facial Expression Recognition Systems Assisted by Multimodal Sensor Data.Sensors (Basel, Switzerland) · 2019Review
- Comparison of Compression-Based Measures with Application to the Evolution of Primate Genomes.Entropy (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG). The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1) conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2) conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model.
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