ArticleScientific reports2020
Detecting cardiac pathologies via machine learning on heart-rate variability time series and related markers.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- A custom analysis of R-R interval properties reveals alterations in the brain-heart axis in experimental animals with metabolic syndrome.Heart rhythm O2 · 2026Article
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- Long-Term Outcomes of Digital Cardiac Rehabilitation.JACC. Asia · 2025Review
- Predictors of Anemia Intolerance for Real-Time Transfusion Decision-Making During Resuscitation of Trauma Subjects: A Machine Learning Approach Using Heart Rate Variability.Critical care explorations · 2025Article
- Key factors in predictive analysis of cardiovascular risks in public health.Scientific reports · 2025Article
- Enhancing Comprehensive Assessments in Chronic Heart Failure Caused by Ischemic Heart Disease: The Diagnostic Utility of Holter ECG Parameters.Medicina (Kaunas, Lithuania) · 2024Article
- Pulse Wave Velocity: Methodology, Clinical Applications, and Interplay with Heart Rate Variability.Reviews in cardiovascular medicine · 2024Review
- Multifractal foundations of biomarker discovery for heart disease and stroke.Scientific reports · 2023Article
- A Disentangled VAE-BiLSTM Model for Heart Rate Anomaly Detection.Bioengineering (Basel, Switzerland) · 2023Article
- Primer on Machine Learning in Electrophysiology.Arrhythmia & electrophysiology review · 2023Review
- Enhanced Heart Rate Prediction Model Using Damped Least-Squares Algorithm.Sensors (Basel, Switzerland) · 2022Article
- Artificial Intelligence for Cardiac Diseases Diagnosis and Prediction Using ECG Images on Embedded Systems.Biomedicines · 2022Article
- Utilizing Artificial Intelligence to Enhance Health Equity Among Patients with Heart Failure.Heart failure clinics · 2022Review
- Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects.Sensors (Basel, Switzerland) · 2022Review
- A Novel, Cardiac-Derived Algorithm for Uterine Activity Monitoring in a Wearable Remote Device.Frontiers in bioengineering and biotechnology · 2022Article
- Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning.Sensors (Basel, Switzerland) · 2021Article
- Extracting Features from Poincaré Plots to Distinguish Congestive Heart Failure Patients According to NYHA Classes.Bioengineering (Basel, Switzerland) · 2021Article
- Assessment of the Impact of Alcohol Consumption Patterns on Heart Rate Variability by Machine Learning in Healthy Young Adults.Medicina (Kaunas, Lithuania) · 2021Article
- Judgement of valence of musical sounds by hand and by heart, a machine learning paradigm for reading the heart.Heliyon · 2021Article
- Trends, Technologies, and Key Challenges in Smart and Connected Healthcare.IEEE access : practical innovations, open solutions · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper we develop statistical algorithms to infer possible cardiac pathologies, based on data collected from 24 h Holter recording over a sample of 2829 labelled patients; labels highlight whether a patient is suffering from cardiac pathologies. In the first part of the work we analyze statistically the heart-beat series associated to each patient and we work them out to get a coarse-grained description of heart variability in terms of 49 markers well established in the reference community. These markers are then used as inputs for a multi-layer feed-forward neural network that we train in order to make it able to classify patients. However, before training the network, preliminary operations are in order to check the effective number of markers (via principal component analysis) and to achieve data augmentation (because of the broadness of the input data). With such groundwork, we finally train the network and show that it can classify with high accuracy (at most ~85% successful identifications) patients that are healthy from those displaying atrial fibrillation or congestive heart failure. In the second part of the work, we still start from raw data and we get a classification of pathologies in terms of their related networks: patients are associated to nodes and links are drawn according to a similarity measure between the related heart-beat series. We study the emergent properties of these networks looking for features (e.g., degree, clustering, clique proliferation) able to robustly discriminate between networks built over healthy patients or over patients suffering from cardiac pathologies. We find overall very good agreement among the two paved routes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.