ArticleSensors (Basel, Switzerland)2020
Arrhythmia Diagnosis by Using Level-Crossing ECG Sampling and Sub-Bands Features Extraction for Mobile Healthcare.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Level-crossing processing and deep convolutional neural network for arrhythmia classification in telehealth services.Physical and engineering sciences in medicine · 2026Article
- Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review.Sensors (Basel, Switzerland) · 2023Review
- Hybrid-Pattern Recognition Modeling with Arrhythmia Signal Processing for Ubiquitous Health Management.Sensors (Basel, Switzerland) · 2022Article
- Event-Driven Acquisition and Machine-Learning-Based Efficient Prediction of the Li-Ion Battery Capacity.SN computer science · 2022Article
- Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification.Sensors (Basel, Switzerland) · 2021Article
- Signal-piloted processing and machine learning based efficient power quality disturbances recognition.PloS one · 2021Article
- Baseline wander and power-line interference elimination of ECG signals using efficient signal-piloted filtering.Healthcare technology letters · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Mobile healthcare is an emerging technique for clinical applications. It is usually based on cloud-connected biomedical implants. In this context, a novel solution is presented for the detection of arrhythmia by using electrocardiogram (ECG) signals. The aim is to achieve an effective solution by using real-time compression, efficient signal processing, and data transmission. The system utilizes level-crossing-based ECG signal sampling, adaptive-rate denoising, and wavelet-based sub-band decomposition. Statistical features are extracted from the sub-bands and used for automated arrhythmia classification. The performance of the system was studied by using five classes of arrhythmia, obtained from the MIT-BIH dataset. Experimental results showed a three-fold decrease in the number of collected samples compared to conventional counterparts. This resulted in a significant reduction of the computational cost of the post denoising, features extraction, and classification. Moreover, a seven-fold reduction was achieved in the amount of data that needed to be transmitted to the cloud. This resulted in a notable reduction in the transmitter power consumption, bandwidth usage, and cloud application processing load. Finally, the performance of the system was also assessed in terms of the arrhythmia classification, achieving an accuracy of 97%.
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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.