Capacitive multi-electrode array with real-time electrode selection for unobtrusive ECG &amp; BIOZ monitoring

Castro, Ivan D.; Patel, Aakash; Torfs, Tom; Puers, Robert; Hoof, Chris Van

doi:10.1109/embc.2019.8857150

Cited by 16 publications

(22 citation statements)

References 12 publications

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“…(i.e. [5][6][7][8][9][10][11][12][13][14] Hz and [0-50] Hz). This was initially used in [19] for contact ECG for different limits, and evaluated by the authors for ccECG in [10].…”

Section: Resultsmentioning

confidence: 99%

“…This type of classification is relevant not only as a postprocessing tool, but also for real-time hardware adaptation approaches such as the modification of hardware settings [5] or the selection of electrodes from high-density arrays [6]. These tools are expected to result in increased coverage when acquiring signals from real-life scenarios and a reduction in the error of specific features of interest such as heart rate and heart rate variability.…”

Section: Discussionmentioning

confidence: 99%

“…A promising approach to increase the robustness of extracted cardiac information is the use of signal quality indicators (SQIs) and quality-based classification models (CMs). These CMs enable MA handling methods, such as offline post-processing and real-time hardware adaptation methods [5,6], to be applied in real-life scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Data Quality Assessment of Capacitively-coupled ECG Signals

Castro¹,

Varon²,

Moeyersons³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

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Acquisition of capacitively-coupled ECG (ccECG) from daily life scenarios is limited by its sensitivity to motion and its variability in signal quality. 48 features, in combination with different classifiers, were evaluated for quality classification on a dataset of 10000 ccECG segments of 15 seconds. Feature subsets with potential high discriminatory power were identified and evaluated in multiple supervised models, for two classification problems with different tolerance to artefacts. This resulted in balanced accuracies of 94.02% and 92.4%, achieved using a Linear SVM and a fine KNN respectively. These models are useful tools for real-time and offline processing of ccECG signals recorded in real-life scenarios

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“…(i.e. [5][6][7][8][9][10][11][12][13][14] Hz and [0-50] Hz). This was initially used in [19] for contact ECG for different limits, and evaluated by the authors for ccECG in [10].…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Data Quality Assessment of Capacitively-coupled ECG Signals

Castro¹,

Varon²,

Moeyersons³

et al. 2019

2019 Computing in Cardiology Conference (CinC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…This dataset consists of 10,000 ccECG signals originating from two recording devices. The first recording device is described in [ 18 , 19 ] and the second one was used to create the publicly available UnoVis dataset [ 7 ]. The dataset comprises ccECG signals from different scenarios, such as signals recorded while sitting in a static car seat, while driving a car, while sleeping and while working on an office chair.…”

Section: Materials and Methodsmentioning

confidence: 99%

Supervised SVM Transfer Learning for Modality-Specific Artefact Detection in ECG

Moeyersons

Morales

Villa

et al. 2021

Sensors

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The electrocardiogram (ECG) is an important diagnostic tool for identifying cardiac problems. Nowadays, new ways to record ECG signals outside of the hospital are being investigated. A promising technique is capacitively coupled ECG (ccECG), which allows ECG signals to be recorded through insulating materials. However, as the ECG is no longer recorded in a controlled environment, this inevitably implies the presence of more artefacts. Artefact detection algorithms are used to detect and remove these. Typically, the training of a new algorithm requires a lot of ground truth data, which is costly to obtain. As many labelled contact ECG datasets exist, we could avoid the use of labelling new ccECG signals by making use of previous knowledge. Transfer learning can be used for this purpose. Here, we applied transfer learning to optimise the performance of an artefact detection model, trained on contact ECG, towards ccECG. We used ECG recordings from three different datasets, recorded with three recording devices. We showed that the accuracy of a contact-ECG classifier improved between 5 and 8% by means of transfer learning when tested on a ccECG dataset. Furthermore, we showed that only 20 segments of the ccECG dataset are sufficient to significantly increase the accuracy.

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“…The capacitive sensor system that was evaluated is shown in Figure 1 and is described in detail in [27]. For this evaluation, the system was implemented on a seat-covering pillow for easy transfer between vehicles, but it can also be integrated directly into a car seat in case of a permanent installation.…”

Section: Capacitively-coupled Ecg and Bio-impedance System And Positimentioning

confidence: 99%

Physiological Driver Monitoring Using Capacitively Coupled and Radar Sensors

et al. 2019

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Unobtrusive monitoring of drivers’ physiological parameters is a topic gaining interest, potentially allowing to improve the performance of safety systems to prevent accidents, as well as to improve the driver’s experience or provide health-related services. In this article, two unobtrusive sensing techniques are evaluated: capacitively coupled sensing of the electrocardiogram and respiration, and radar-based sensing of heartbeat and respiration. A challenge for use of these techniques in vehicles are the vibrations and other disturbances that occur in vehicles to which they are inherently more sensitive than contact-based sensors. In this work, optimized sensor architectures and signal processing techniques are proposed that significantly improve the robustness to artefacts. Experimental results, conducted under real driving conditions on public roads, demonstrate the feasibility of the proposed approach. R peak sensitivities and positive predictivities higher than 98% both in highway and city traffic, heart rate mean absolute error of 1.02 bpm resp. 2.06 bpm in highway and city traffic and individual beat R-R interval 95% percentile error within ±27.3 ms are demonstrated. The radar experimental results show that respiration can be measured while driving and heartbeat can be recovered from vibration noise using an accelerometer-based motion reduction algorithm.

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Capacitive multi-electrode array with real-time electrode selection for unobtrusive ECG & BIOZ monitoring

Cited by 16 publications

References 12 publications

Data Quality Assessment of Capacitively-coupled ECG Signals

Data Quality Assessment of Capacitively-coupled ECG Signals

Supervised SVM Transfer Learning for Modality-Specific Artefact Detection in ECG

Physiological Driver Monitoring Using Capacitively Coupled and Radar Sensors

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