AIMC Topic: Heart Rate

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Randomized Trial on Electroacupuncture for Recovery of Postoperative Gastrointestinal Function Based on Long-Term Monitoring Device.

Annals of surgical oncology
BACKGROUND: This research aimed to explore the efficacy and safety of electroacupuncture in promoting the recovery of postoperative gastrointestinal function and to discuss the potential mechanism on the basis of heart rate variability (HRV).

Integrating deep learning with ECG, heart rate variability and demographic data for improved detection of atrial fibrillation.

Open heart
BACKGROUND: Atrial fibrillation (AF) is a common but often undiagnosed condition, increasing the risk of stroke and heart failure. Early detection is crucial, yet traditional methods struggle with AF's transient nature. This study investigates how au...

Predicting high anger intensity using ecological momentary assessment and wearable-derived physiological data in a trauma-affected sample.

European journal of psychotraumatology
Digital technologies offer tremendous potential to predict dysregulated mood and behavior within an individual's environment, and in doing so can support the development of new digital health interventions. However, no prediction models have been bu...

Physiological Sensor Modality Sensitivity Test for Pain Intensity Classification in Quantitative Sensory Testing.

Sensors (Basel, Switzerland)
Chronic pain is prevalent and disproportionately impacts adults with a lower quality of life. Although subjective self-reporting is the "gold standard" for pain assessment, tools are needed to objectively monitor and account for inter-individual diff...

Non-invasive estimation of beat-by-beat aortic blood pressures from electrical impedance tomography data processed by machine learning.

Journal of clinical monitoring and computing
Hypotension in perioperative and intensive care settings is a significant risk factor associated with complications such as myocardial infarction and kidney injury thereby increasing perioperative complications and mortality. Continuous blood pressur...

Synthetic ECG signal generation using generative neural networks.

PloS one
Electrocardiogram (ECG) datasets tend to be highly imbalanced due to the scarcity of abnormal cases. Additionally, the use of real patients' ECGs is highly regulated due to privacy issues. Therefore, there is always a need for more ECG data, especial...

Automated ADHD detection using dual-modal sensory data and machine learning.

Medical engineering & physics
This study explores using dual-modal sensory data and machine learning to objectively identify Attention-Deficit/Hyperactivity Disorder (ADHD), a neurodevelopmental disorder traditionally diagnosed through subjective clinical evaluations. Six machine...

Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review.

Biosensors
The development of digital instruments for mental health monitoring using biosensor data from wearable devices can enable remote, longitudinal, and objective quantitative benchmarks. To survey developments and trends in this field, we conducted a sys...

Continuous real-time detection and management of comprehensive mental states using wireless soft multifunctional bioelectronics.

Biosensors & bioelectronics
Quantitatively measuring human mental states that profoundly affect cognition, behavior, and recovery would revolutionize personalized digital healthcare. Detecting fatigue, stress, and sleep is particularly important due to their interdependence: pe...

Reconstruction of ECG from ballistocardiogram using generative adversarial networks with attention.

Biomedical physics & engineering express
Electrocardiogram (ECG) is widely used to provide early warning signals for cardiovascular diseases. However, traditional twelve-lead ECG monitoring methods and smartwatch-based home solutions are unable to achieve daily long-term monitoring. Therefo...