ArticleSensors (Basel, Switzerland)2019
Segmentation of the ECG Signal by Means of a Linear Regression Algorithm.
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 10 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
10 citing papers in PubMed.
- Sensing Technologies and Physiological Parameters for Real-Time Driver Drowsiness Detection: A Comprehensive Review.Sensors (Basel, Switzerland) · 2026Review
- Article
- Wearable ECG Device and Machine Learning for Heart Monitoring.Sensors (Basel, Switzerland) · 2024Article
- Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices.Sensors (Basel, Switzerland) · 2023Article
- Comparison of Performance of Predicting the Wear Amount of Tire Tread Depending on Sensing Information.Sensors (Basel, Switzerland) · 2023Article
- A Novel Approach for Sleep Arousal Disorder Detection Based on the Interaction of Physiological Signals and Metaheuristic Learning.Computational intelligence and neuroscience · 2023Article
- Article
- Recognition of Underwater Materials of Bionic and Natural Fishes Based on Blue-Green Light Reflection.Sensors (Basel, Switzerland) · 2022Article
- Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias.Sensors (Basel, Switzerland) · 2021Article
- A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics.Frontiers in cardiovascular medicine · 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
7 authors.
Funding
Abstract
The monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines of research have even been developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple and low computing cost algorithm to process and analyze an ECG signal. Our approach is based on the use of linear regression to segment the signal, with the goal of detecting the R point of the ECG wave and later, to separate the signal in periods for detecting P, Q, S, and T peaks. After pre-processing of ECG signal to reduce the noise, the algorithm was able to efficiently detect fiducial points, information that is transcendental for diagnosis of heart conditions using machine learning classifiers. When tested on 260 ECG records, the detection approach performed with a Sensitivity of 97.5% for Q-point and 100% for the rest of ECG peaks. Finally, we validated the robustness of our algorithm by developing an ECG sensor to register and transmit the acquired signals to a mobile device in real time.
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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.