ArticlePloS one2016
Detection and Classification of Measurement Errors in Bioimpedance Spectroscopy.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 27 citations in OpenAlex.
- Applications of bioimpedance measurement in clinical and allied fields: a comprehensive review.Biomedical engineering online · 2026Review
- Electrical impedance myography as a marker of muscle mass in rats with simulated Anorexia Nervosa.Journal of electrical bioimpedance · 2025Article
- A New Technique to Estimate the Cole Model for Bio-impedance Spectroscopy with the High-Frequency Characteristics Estimation.... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks · 2023Article
- Parasitic Effects on Electrical Bioimpedance Systems: Critical Review.Sensors (Basel, Switzerland) · 2022Review
- Preliminary study on parameterization of raw electrical bioimpedance data with 3 frequencies.Scientific reports · 2022Article
- Machine Learning Model Based on Transthoracic Bioimpedance and Heart Rate Variability for Lung Fluid Accumulation Detection: Prospective Clinical Study.JMIR medical informatics · 2020Article
- Analysis of Consistency of Transthoracic Bioimpedance Measurements Acquired with Dry Carbon Black PDMS Electrodes, Adhesive Electrodes, and Wet Textile Electrodes.Sensors (Basel, Switzerland) · 2018Article
- Extracting Parasite Effects of Electrical Bioimpedance Measurements.Journal of electrical bioimpedance · 2018Article
- Electrical Impedance Myography and Its Applications in Neuromuscular Disorders.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2017Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bioimpedance spectroscopy (BIS) measurement errors may be caused by parasitic stray capacitance, impedance mismatch, cross-talking or their very likely combination. An accurate detection and identification is of extreme importance for further analysis because in some cases and for some applications, certain measurement artifacts can be corrected, minimized or even avoided. In this paper we present a robust method to detect the presence of measurement artifacts and identify what kind of measurement error is present in BIS measurements. The method is based on supervised machine learning and uses a novel set of generalist features for measurement characterization in different immittance planes. Experimental validation has been carried out using a database of complex spectra BIS measurements obtained from different BIS applications and containing six different types of errors, as well as error-free measurements. The method obtained a low classification error (0.33%) and has shown good generalization. Since both the features and the classification schema are relatively simple, the implementation of this pre-processing task in the current hardware of bioimpedance spectrometers is possible.
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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.