ArticleScientific reports2017
Efficient ECG Compression and QRS Detection for E-Health Applications.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Continuous Glucose Monitoring Data Compression Using Peak-Nadir Encoding in Diabetes: Method Development and Evaluation.JMIR biomedical engineering · 2026Article
- An Open-Source Graphical User Interface-Embedded Automated Electrocardiogram Quality Assessment: A Balanced Class Representation Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Robust, self-adhesive and anti-bacterial silk-based LIG electrodes for electrophysiological monitoring.RSC advances · 2023Article
- On the Efficient Delivery and Storage of IoT Data in Edge-Fog-Cloud Environments.Sensors (Basel, Switzerland) · 2022Article
- ECGAssess: A Python-Based Toolbox to Assess ECG Lead Signal Quality.Frontiers in digital health · 2022Article
- Telemedicine for detecting Brugada Syndrome in eastern Indonesia: A multi-center prospective observational study.Annals of medicine and surgery (2012) · 2021Article
- Computational Diagnostic Techniques for Electrocardiogram Signal Analysis.Sensors (Basel, Switzerland) · 2020Review
- On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications.Measurement : journal of the International Measurement Confederation · 2020Article
- An Adaptive Median Filter Based on Sampling Rate for R-Peak Detection and Major-Arrhythmia Analysis.Sensors (Basel, Switzerland) · 2020Article
- Event-Driven ECG Sensor in Healthcare Devices for Data Transfer Optimization.Arabian journal for science and engineering · 2020Article
- Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals.Frontiers in physiology · 2020Article
- Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction.Scientific reports · 2019Article
- Effective high compression of ECG signals at low level distortion.Scientific reports · 2019Article
- How Effective Is Pulse Arrival Time for Evaluating Blood Pressure? Challenges and Recommendations from a Study Using the MIMIC Database.Journal of clinical medicine · 2019Article
- Assessing Anxiety Disorders Using Wearable Devices: Challenges and Future Directions.Brain sciences · 2019Review
- Efficient QRS complex detection algorithm based on Fast Fourier Transform.Biomedical engineering letters · 2019Article
- Article
- Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database.Diagnostics (Basel, Switzerland) · 2018Article
- On the Beat Detection Performance in Long-Term ECG Monitoring Scenarios.Sensors (Basel, Switzerland) · 2018Article
- Less Is More in Biosignal Analysis: Compressed Data Could Open the Door to Faster and Better Diagnosis.Diseases (Basel, Switzerland) · 2018Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.
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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.