ArticleFrontiers in physiology2015
Nonlinear digital signal processing in mental health: characterization of major depression using instantaneous entropy measures of heartbeat dynamics.
Article in Frontiers in physiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02990793 (A Prospective, Double Blind, Randomized, Sham-Controlled, Clinical Trial to Evaluate The Safety And Efficacy Of Biometrics-Guided Magnetic EEG Resonance Therapy), which is not on this map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Prospective, Double Blind, Randomized, Sham-Controlled, Clinical Trial to Evaluate The Safety And Efficacy Of Biometrics-Guided Magnetic EEG Resonance Therapy (MeRT) Treatment Of Post-Traumatic Stress Disorder
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Smart Devices and Multimodal Systems for Mental Health Monitoring: From Theory to Application.Bioengineering (Basel, Switzerland) · 2026Review
- Neurophysiological Markers of Regulation Success in Everyday Life in Depression.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Article
- Chaos to clarity: interpreting time series complexity metrics with an application to depression.Discover mental health · 2025Review
- Oscillatory components of bidirectional cardio-respiratory coupling in depression and suicidal ideation: insights from swarm decomposition and entropy analysis.Frontiers in network physiology · 2025Article
- Is Greener Better? Quantifying the Impact of a Nature Walk on Stress Reduction Using HRV and Saliva Cortisol Biomarkers.International journal of environmental research and public health · 2024Article
- Autonomic complexity dynamically indexes affect regulation in everyday life.Journal of psychopathology and clinical science · 2023Article
- Effects of Missing Data on Heart Rate Variability Metrics.Sensors (Basel, Switzerland) · 2022Article
- Multiscale Entropy Analysis of Heart Rate Variability in Neonatal Patients with and without Seizures.Bioengineering (Basel, Switzerland) · 2021Article
- Uncovering complex central autonomic networks at rest: a functional magnetic resonance imaging study on complex cardiovascular oscillations.Journal of the Royal Society, Interface · 2020Article
- Entropy analysis of heart rate variability and its application to recognize major depressive disorder: A pilot study.Technology and health care : official journal of the European Society for Engineering and Medicine · 2019Article
- Validation of the Apple Watch for Heart Rate Variability Measurements during Relax and Mental Stress in Healthy Subjects.Sensors (Basel, Switzerland) · 2018Article
- Complexity Variability Assessment of Nonlinear Time-Varying Cardiovascular Control.Scientific reports · 2017Article
- Delay-correlation landscape reveals characteristic time delays of brain rhythms and heart interactions.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2016Article
- Relationship between cardiac vagal activity and mood congruent memory bias in major depression.Journal of affective disorders · 2016Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nonlinear digital signal processing methods that address system complexity have provided useful computational tools for helping in the diagnosis and treatment of a wide range of pathologies. More specifically, nonlinear measures have been successful in characterizing patients with mental disorders such as Major Depression (MD). In this study, we propose the use of instantaneous measures of entropy, namely the inhomogeneous point-process approximate entropy (ipApEn) and the inhomogeneous point-process sample entropy (ipSampEn), to describe a novel characterization of MD patients undergoing affective elicitation. Because these measures are built within a nonlinear point-process model, they allow for the assessment of complexity in cardiovascular dynamics at each moment in time. Heartbeat dynamics were characterized from 48 healthy controls and 48 patients with MD while emotionally elicited through either neutral or arousing audiovisual stimuli. Experimental results coming from the arousing tasks show that ipApEn measures are able to instantaneously track heartbeat complexity as well as discern between healthy subjects and MD patients. Conversely, standard heart rate variability (HRV) analysis performed in both time and frequency domains did not show any statistical significance. We conclude that measures of entropy based on nonlinear point-process models might contribute to devising useful computational tools for care in mental health.
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