☆ 3.8 Proceedings Paper

Spectrum Estimation of Heart Rate Variability Using Low-rank Matrix Completion

2022 IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI) JOINTLY ORGANISED WITH THE IEEE-EMBS INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN'22) (2022)

Weak Monotonicity With Trend Analysis for Unsupervised Feature Evaluation

Lei Lu et al.

Summary: This study proposes a novel unsupervised feature selection method that can identify common trends in performance degradation in the presence of measurement noises and population variations. The selected key features contribute to high-quality predictive analysis.

IEEE TRANSACTIONS ON CYBERNETICS (2023)

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Article Engineering, Biomedical

Parasympathetic-Sympathetic Causal Interactions Assessed by Time-Varying Multivariate Autoregressive Modeling of Electrodermal Activity and Heart-Rate-Variability

Alejandro Luis Callara et al.

Summary: This study proposes a novel framework to quantify the causal flow of information between the parasympathetic and sympathetic nervous system through the analysis of heart rate variability and electrodermal activity signals. The results show a parasympathetic driven interaction in the resting state, with differences in dynamics of the PNS-SNS interaction under different stressors. The method used could have promising future applicability in investigating more complex contexts such as affective and pathological scenarios.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2021)

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Article Computer Science, Information Systems

Photoplethysmographic Waveform and Pulse Rate Variability Analysis in Hyperbaric Environments

Maria Dolores Pelaez-Coca et al.

Summary: This study aims to investigate alterations in pulse photoplethysmogram (PPG) signal morphology and Pulse Rate Variability (PRV) in subjects exposed to a hyperbaric environment. Results show widened pulse width and increased pulse area with pressure, suggesting sympathetic activation. However, PRV results indicate increased parasympathetic activity and reduced sympathetic response.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

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Article Computer Science, Artificial Intelligence

A Novel Ensemble Deep Learning Approach for Sleep-Wake Detection Using Heart Rate Variability and Acceleration

Zhenghua Chen et al.

Summary: Sleep-wake detection is crucial for assessing sleep quality. A novel ensemble deep learning framework combining heart rate variability and acceleration data has been proposed in this article. By integrating LF-LSTM network and handcrafted HRV features, the performance of sleep-wake classification is significantly boosted.

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2021)

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Article Computer Science, Information Systems