What is he best known for?
The fields of study he is best known for:
- Statistics
- Internal medicine
- Artificial intelligence
The scientist’s investigation covers issues in Statistics, Autoregressive model, Algorithm, Information transfer and Transfer entropy.
His Statistics research incorporates themes from Cardiac output and k-nearest neighbors algorithm.
His Autoregressive model research is multidisciplinary, relying on both Frequency domain, Coherence, Multivariate statistics and Surrogate data.
Luca Faes has included themes like Heart failure, Series, Entropy rate and Circadian rhythm in his Algorithm study.
His Information transfer study combines topics in areas such as Entropy and Conditional entropy.
His study with Transfer entropy involves better knowledge in Artificial intelligence.
His most cited work include:
- Information-based detection of nonlinear Granger causality in multivariate processes via a nonuniform embedding technique (157 citations)
- Surrogate data analysis for assessing the significance of the coherence function (139 citations)
- A method for quantifying atrial fibrillation organization based on wave-morphology similarity (122 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Internal medicine, Cardiology, Autoregressive model, Heart rate variability and Artificial intelligence.
Luca Faes interconnects Supine position, RR interval and Blood pressure, Heart rate in the investigation of issues within Cardiology.
The study incorporates disciplines such as Coherence, Multivariate statistics, Algorithm, Granger causality and Series in addition to Autoregressive model.
His Algorithm study also includes fields such as
- Parametric statistics which intersects with area such as Representation,
- Surrogate data most often made with reference to Statistics.
Data mining and Nonlinear system is closely connected to Entropy in his research, which is encompassed under the umbrella topic of Heart rate variability.
His studies in Artificial intelligence integrate themes in fields like Machine learning, Information transfer, Electroencephalography and Pattern recognition.
He most often published in these fields:
- Internal medicine (24.70%)
- Cardiology (24.29%)
- Autoregressive model (21.05%)
What were the highlights of his more recent work (between 2018-2021)?
- Heart rate variability (18.62%)
- Autoregressive model (21.05%)
- Cardiology (24.29%)
In recent papers he was focusing on the following fields of study:
Luca Faes focuses on Heart rate variability, Autoregressive model, Cardiology, Internal medicine and Baroreflex.
The Heart rate variability study combines topics in areas such as Epilepsy, Statistics, Conditional entropy and Nonlinear system.
In general Statistics, his work in Time series is often linked to Subnetwork linking many areas of study.
His Autoregressive model study combines topics from a wide range of disciplines, such as Parametric model, Stochastic process, Multivariate statistics, Granger causality and Transfer entropy.
His Transfer entropy research is multidisciplinary, incorporating perspectives in Algorithm and Point process.
Within one scientific family, Luca Faes focuses on topics pertaining to Blood pressure under Cardiology, and may sometimes address concerns connected to Supine position.
Between 2018 and 2021, his most popular works were:
- Critical Comments on EEG Sensor Space Dynamical Connectivity Analysis (72 citations)
- Comparison of short-term heart rate variability indexes evaluated through electrocardiographic and continuous blood pressure monitoring (20 citations)
- Information Dynamics of the Brain, Cardiovascular and Respiratory Network during Different Levels of Mental Stress. (16 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Internal medicine
- Artificial intelligence
His primary areas of investigation include Heart rate variability, Neuroscience, Autoregressive model, Cardiology and Internal medicine.
His study in Heart rate variability is interdisciplinary in nature, drawing from both Supine position and Healthy subjects.
His work in Neuroscience covers topics such as Information transfer which are related to areas like Dynamical systems theory, Data mining, Parametric model, Ordinary least squares and Data point.
His Autoregressive model research includes elements of Stochastic process and Entropy.
His work deals with themes such as Baroreflex, Blood pressure, Linear discriminant analysis and Generalized epilepsy, Epilepsy, which intersect with Cardiology.
Luca Faes interconnects Time–frequency analysis and Standard deviation in the investigation of issues within Internal medicine.
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