Automatic Parameter Extraction from Capacitive ECG Measurements

Eilebrecht, Benjamin; Henriques, J.; Rocha, Teresa; Walter, Marian; Paredes, S.; Carvalho, Paulo; Czaplik, Michael; Leonhardt, Steffen

doi:10.1007/s13239-012-0101-y

Cited by 4 publications

(7 citation statements)

References 18 publications

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“…These features are not readily available and require more sophisticated computing or manual extraction by a medical professional. Although segmentation algorithms for ECG exist, they are reported to fail for, e.g., the capacitive ECG [30].…”

Section: Discussionmentioning

confidence: 99%

Correction of the Unobtrusive ECG Using System Identification

Boehm

Leonhardt

et al. 2017

Electronics

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Unobtrusively acquired electrocardiograms (ECG) could substantially improve the comfort of patients. However, such ECGs are not used in clinical practice because (among other reasons) signal deformations impede correct diagnosis of the ECG. Here, methods are proposed for correction of the unobtrusive ECG, based on system identification. Knowing the reference ECG, models were developed to correct the unobtrusively acquired ECG. A finite impulse response (FIR) model, a state space model and an autoregressive model were developed. The models were trained and evaluated on the Goldberger leads recorded from an ECG T-shirt with dry electrodes, and from a gold standard ECG. It was found that the FIR model corrects the unobtrusive ECG with good agreement (ρ aVR = 0.84 ± 0.10, ρ aVL = 0.65 ± 0.24, ρ aVF = 0.88 ± 0.04), while the other models do not yield significant improvements, or become unstable. The R-peaks were also accurately corrected by the FIR model (MSE aVR = 0.10 ± 0.10, MSE aVL = 0.14 ± 0.27, MSE aVF = 0.03 ± 0.02). To conclude, the proposed FIR method succeeded in significantly correcting the unobtrusive ECG signal.

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Section: Discussionmentioning

confidence: 99%

Correction of the Unobtrusive ECG Using System Identification

Boehm

Leonhardt

et al. 2017

Electronics

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“…The time coverage is thus the percentage of time during which the instantaneous heart rate is reliable. To analyse the shape of the ECG signal, we calculated temporal features related to peaks on the ECG curve, in a similar manner to Eilebrecht et al (2012). For this purpose, we detected the PQRST peaks using the method by Martinez et al (2004), which consists of a waveletbased ECG delineation enabling accurate detection.…”

Section: Alignmentmentioning

confidence: 99%

“…and slight motion shows excellent correspondence. In a similar study, Eilebrecht et al (2012) asked subjects to sit on a chair containing an array of capacitive sensors. They obtained a mean deviation of RR-interval differences of 50 ms, and compared averaged values over 5 s intervals.…”

Section: Ecg Wave Feature Analysismentioning

confidence: 99%

“…This set-up, also used in Oehler et al (2008), is not quite unobtrusive despite embedding the sensors in a portable system. Work by Eilebrecht et al (2012) investigated ECG feature extraction from a capacitive ECG system embedded in a pillow placed between a subject's back and a chair. Although their results were promising, their work cannot be directly translated to the NICU environment.…”

Section: Introductionmentioning

confidence: 99%

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Unobtrusive ECG monitoring in the NICU using a capacitive sensing array

et al. 2014

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Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Link to publication• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. AbstractThe thin skin of preterm babies is easily damaged by adhesive electrodes, tapes, chest drains and needle-marks. The scars caused could be disfiguring or disabling to 10% of preterm newborns. Capacitive sensors present an attractive option for pervasively monitoring neonatal ECG, and can be embedded in a support system or even a garment worn by the neonate. This could improve comfort and reduce pain aiding better recovery as well as avoiding the scars caused by adhesive electrodes. In this work, we investigate the use of an array of capacitive sensors unobtrusively embedded in a mattress and used in a clinical environment for 15 preterm neonates. We also describe the analysis framework including the fusion of information from all sensors to provide a more accurate ECG signal. We propose a channel selection strategy as well as a method using physiological information to obtain a reliable ECG signal. When sensor coverage is well attained, results for both instantaneous heart rate and ECG signal shape analysis are very encouraging. The study also provides several insights on important factors affecting the results. These include the effect of textile type, number of layers, interferences (e.g. people walking by), motion severity and interventions. Incorporating this knowledge in the design of a capacitive sensing system would be crucial in ensuring that these sensors provide a reliable ECG signal when embedded in a neonatal support system.

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“…The existing R‐peak detection strategies generally influenced by some QRS complex [6]. For detection of R‐peaks [7], Pan‐Tompkins method [8], FPGA‐based method [9], empirical wavelet transform [10], Hilbert transform (HT) and adaptive thresholding [11], derivative‐based algorithm [12], neural network filter [13], lifting wavelet transform and HT [14], entropy method [15], mathematical morphology [16], empirical mode decomposition [17], geometrical matching approach [18], wavelet and artificial neural network [19], are the various methods reported by researchers.…”

Section: Introductionmentioning

confidence: 99%

Efficient FPGA‐based VLSI architecture for detecting R‐peaks in electrocardiogram signal by combining Shannon energy with Hilbert transform

Kumar¹,

Chari

2018

IET signal process.

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Electrocardiogram (ECG) is a critical application in light of R-peak detection. The R-peaks are impacted by some QRS complex and noises in the current method. Basic testability, snappier execution, and confirmation choices are accomplished by the utilization of the field programmable gate array (FPGA). But, FPGA execution gives less accuracy. To battle execution trouble, another R-peak detector is proposed and relying upon the propelled peak finding logic, which incorporates a Bandpass filter and first-order differentiation process, which are done in the primary phase of the strategy. The noises in the input ECG signal are decreased in the first stage. In the subsequent stage, the smooth Shannon energy envelope (SEE) is acquired by utilizing SEE extraction and the zero phases filtering process. The false R peak is stifled by proposing a Hilbert transform (HT) in the third stage. The HT requires more hardware space and high-power utilization in the current technique. Due to this reason, Real valued Fast Fourier Transform (RFFT) and Inverse RFFT (IRFFT) techniques are proposed in the HT. In the fourth stage, R-peak acknowledgment is evaluated using Massachusetts Institute of Technology, Beth Israel Hospital (MIT-BIH) arrhythmia database and produced the average accuracy of 99.86%, sensitivity of 99.95% and the positive predictivity of 99.90%.

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Automatic Parameter Extraction from Capacitive ECG Measurements

Cited by 4 publications

References 18 publications

Correction of the Unobtrusive ECG Using System Identification

Correction of the Unobtrusive ECG Using System Identification

Unobtrusive ECG monitoring in the NICU using a capacitive sensing array

Efficient FPGA‐based VLSI architecture for detecting R‐peaks in electrocardiogram signal by combining Shannon energy with Hilbert transform

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