ArticleMeasurement : journal of the International Measurement Confederation2020
On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications.
Article in Measurement : journal of the International Measurement Confederation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Continuous Glucose Monitoring Data Compression Using Peak-Nadir Encoding in Diabetes: Method Development and Evaluation.JMIR biomedical engineering · 2026Article
- ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals.Sensors (Basel, Switzerland) · 2021Article
- [A new lossy compression method for fetal heart rate signals-Convolutional Codec Network].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021Article
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Authors and funding
6 authors.
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Abstract
This paper presents a compression study of electrocardiogram (ECG) signals for e-Health cardiac online diagnostic systems. The study uses 75 real electrocardiogram records sampled with continuous-time level-crossing (LC) analog-to-digital converter (ADC). This signal-dependent LC-ADC compresses signals compared to conventional ADC but further compression is needed especially for long-time monitoring applications. The orthogonal matching pursuit algorithm is simulated to evaluate ECG compression with 54 orthogonal and biorthogonal wavelets. For LC-ADC amplitude output compression, Biorthogonal3.1 (bior3.1) wavelet achieves optimal performances in terms of compression ratio (CR) while ensuring 2-% percentage root-mean-square difference (PRD). The PRD must be limited to this value to ensure a very good quality signals after decompression. For circuit implementation purposes, bior3.1 wavelet is proposed as a multiplier-free decomposition step and a noncomplex global and hard thresholding process is achieved. The average CR is 63% and PRD varies between 0.1 and 2.1% leading to a very good diagnostic quality.
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