ArticleBiosensors2021
Integrating ECG Monitoring and Classification via IoT and Deep Neural Networks.
Article in Biosensors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed, 40 citations in OpenAlex.
- Real-Time Cardiac Arrhythmia Classification Using TinyML on Ultra-Low-Cost Microcontrollers: A Feasibility Study for Resource-Constrained Environments.Bioengineering (Basel, Switzerland) · 2026Article
- Development and validation of an artificial intelligence model based on liver CSE-MRI fat maps for predicting dyslipidemia.PLOS digital health · 2026Article
- The Role of Artificial Intelligence in Advancing Biosensor Technology: Past, Present, and Future Perspectives.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Artificial intelligence revolutionizing anesthesia management: advances and prospects in intelligent anesthesia technology.Frontiers in medicine · 2025Review
- AI-Driven Real-Time Classification of ECG Signals for Cardiac Monitoring Using i-AlexNet Architecture.Diagnostics (Basel, Switzerland) · 2024Article
- Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction.Sensors (Basel, Switzerland) · 2023Article
- Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic.World journal of clinical cases · 2022Review
- Smart Electronic Textiles for Wearable Sensing and Display.Biosensors · 2022Review
- Development and Validation of Embedded Device for Electrocardiogram Arrhythmia Empowered with Transfer Learning.Computational intelligence and neuroscience · 2022Article
- ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches.Computational intelligence and neuroscience · 2022Article
- Evaluation of electrocardiogram: numerical vs. image data for emotion recognition system.F1000Research · 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
10 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anesthesia assessment is most important during surgery. Anesthesiologists use electrocardiogram (ECG) signals to assess the patient's condition and give appropriate medications. However, it is not easy to interpret the ECG signals. Even physicians with more than 10 years of clinical experience may still misjudge. Therefore, this study uses convolutional neural networks to classify ECG image types to assist in anesthesia assessment. The research uses Internet of Things (IoT) technology to develop ECG signal measurement prototypes. At the same time, it classifies signal types through deep neural networks, divided into QRS widening, sinus rhythm, ST depression, and ST elevation. Three models, ResNet, AlexNet, and SqueezeNet, are developed with 50% of the training set and test set. Finally, the accuracy and kappa statistics of ResNet, AlexNet, and SqueezeNet in ECG waveform classification were (0.97, 0.96), (0.96, 0.95), and (0.75, 0.67), respectively. This research shows that it is feasible to measure ECG in real time through IoT and then distinguish four types through deep neural network models. In the future, more types of ECG images will be added, which can improve the real-time classification practicality of the deep model.
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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.