ArticleSensors (Basel, Switzerland)2019
Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in machine and deep learning for ECG beat classification: a systematic review.Frontiers in digital health · 2025Pooled it
- Covid19-Mexican-Patients' Dataset (Covid19MPD) Classification and Prediction Using Feature Importance.Concurrency and computation : practice & experience · 2022Article
- Real-Time Heart Arrhythmia Detection Using Apache Spark Structured Streaming.Journal of healthcare engineering · 2021Article
- Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics.Sensors (Basel, Switzerland) · 2020Article
- Prediction of heart disease and classifiers' sensitivity analysis.BMC bioinformatics · 2020Article
- Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices.Sensors (Basel, Switzerland) · 2020Article
- EEG-Brain Activity Monitoring and Predictive Analysis of Signals Using Artificial Neural Networks.Sensors (Basel, Switzerland) · 2020Article
- ECG Identification For Personal Authentication Using LSTM-Based Deep Recurrent Neural Networks.Sensors (Basel, Switzerland) · 2020Article
- A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network.Frontiers in computational neuroscience · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Abnormal heart rhythms are one of the significant health concerns worldwide. The current state-of-the-art to recognize and classify abnormal heartbeats is manually performed by visual inspection by an expert practitioner. This is not just a tedious task; it is also error prone and, because it is performed, post-recordings may add unnecessary delay to the care. The real key to the fight to cardiac diseases is real-time detection that triggers prompt action. The biggest hurdle to real-time detection is represented by the rare occurrences of abnormal heartbeats and even more are some rare typologies that are not fully represented in signal datasets; the latter is what makes it difficult for doctors and algorithms to recognize them. This work presents an automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies. Although the algorithm is designed and tested on a computer, it is intended ultimately to run on a portable i.e., field-programmable gate array (FPGA) devices. Our algorithm tested on Massachusetts Institute of Technology- Beth Israel Hospital(MIT-BIH) database as per Association for the Advancement of Medical Instrumentation(AAMI) recommendations. The simulation results show the superiority of the proposed method, especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100%.
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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.