This paper is in the following e-collection/theme issue:

Wearable Devices and Sensors (889) Novel Sensors and Data Acquisition Methods in Cardiology (196) mHealth for Data Collection and Research (955) mHealth in a Clinical Setting (615)

Published on in Vol 9, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25415, first published .
Assessing Electrocardiogram and Respiratory Signal Quality of a Wearable Device (SensEcho): Semisupervised Machine Learning-Based Validation Study

Assessing Electrocardiogram and Respiratory Signal Quality of a Wearable Device (SensEcho): Semisupervised Machine Learning-Based Validation Study

Assessing Electrocardiogram and Respiratory Signal Quality of a Wearable Device (SensEcho): Semisupervised Machine Learning-Based Validation Study

Authors of this article:

Haoran Xu1 Author Orcid Image ;   Wei Yan2 Author Orcid Image ;   Ke Lan3 Author Orcid Image ;   Chenbin Ma4 Author Orcid Image ;   Di Wu3 Author Orcid Image ;   Anshuo Wu5 Author Orcid Image ;   Zhicheng Yang6 Author Orcid Image ;   Jiachen Wang1 Author Orcid Image ;   Yaning Zang7 Author Orcid Image ;   Muyang Yan2 Author Orcid Image ;   Zhengbo Zhang8 Author Orcid Image
Article Authors Cited by (11) Tweetations (2) Metrics

Journals

  1. Tiwari A, Cassani R, Kshirsagar S, Tobon D, Zhu Y, Falk T. Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note. Sensors 2022;22(12):4579 View
  2. Cosoli G, Antognoli L, Scalise L. Wearable Electrocardiography for Physical Activity Monitoring: Definition of Validation Protocol and Automatic Classification. Biosensors 2023;13(2):154 View
  3. Pap I, Oniga S. A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence. International Journal of Environmental Research and Public Health 2022;19(18):11413 View
  4. Liu F, Xia S, Wei S, Chen L, Ren Y, Ren X, Xu Z, Ai S, Liu C. Wearable Electrocardiogram Quality Assessment Using Wavelet Scattering and LSTM. Frontiers in Physiology 2022;13 View
  5. Allam J, Samantray S, Sahoo S, Ari S. A deformable CNN architecture for predicting clinical acceptability of ECG signal. Biocybernetics and Biomedical Engineering 2023;43(1):335 View
  6. van der Bijl K, Elgendi M, Menon C. Automatic ECG Quality Assessment Techniques: A Systematic Review. Diagnostics 2022;12(11):2578 View
  7. Huerta Á, Martinez-Rodrigo A, Bertomeu-González V, Ayo-Martin Ó, Rieta J, Alcaraz R. Single-lead electrocardiogram quality assessment in the context of paroxysmal atrial fibrillation through phase space plots. Biomedical Signal Processing and Control 2024;91:105920 View
  8. Rahman S, Pal S, Yearwood J, Karmakar C. Robustness of Deep Learning models in electrocardiogram noise detection and classification. Computer Methods and Programs in Biomedicine 2024;253:108249 View
  9. Bachi L, Varanini M, Costi M, Lombardi D, Billeci L. Automatic noise detection for ambulatory electrocardiogram in presence of ventricular arrhythmias through a machine learning approach. Computers in Biology and Medicine 2024;183:109288 View

Books/Policy Documents

  1. Wang Z, Yang Z, Lan K, Li P, Hao Y, Duan Y, She Y, Li Y, Zhang Z. Wireless Mobile Communication and Healthcare. View
  2. Xu H, Yang Z, Lan K, Yan W, Wang Z, Wang J, Zang Y, Pan J, Yan M, Zhang Z. Wireless Mobile Communication and Healthcare. View