Tremor suppression in ECG

Dotsinsky, Ivan; Mihov, Georgy

doi:10.1186/1475-925x-7-29

Cited by 12 publications

(4 citation statements)

References 25 publications

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“…For the medical interpretation, efforts are usually made to recover the signals from motion artifact-contaminated data, aiming to minimize the signal distortion and retain most of the clinical information. For example, Dotsinsky and Mihov have proposed a moving averaging approach followed by a signal restoring method and a smoothing filter, which can effectively recover the ECG signals with negligible distortion of the wave shapes (Dotsinsky and Mihov 2008). They also reported a conditional partial filtration approach which dynamically applies filters with different parameters to different ECG parts; therefore, no distortions can be found in the critical ECG parts (Dotsinsky and Mihov 2010).…”

Section: Discussionmentioning

confidence: 99%

A novel machine learning-enabled framework for instantaneous heart rate monitoring from motion-artifact-corrupted electrocardiogram signals

2016

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This paper proposes a novel machine learning-enabled framework to robustly monitor the instantaneous heart rate (IHR) from wrist-electrocardiography (ECG) signals continuously and heavily corrupted by random motion artifacts in wearable applications. The framework includes two stages, i.e. heartbeat identification and refinement, respectively. In the first stage, an adaptive threshold-based auto-segmentation approach is proposed to select out heartbeat candidates, including the real heartbeats and large amounts of motion-artifact-induced interferential spikes. Then twenty-six features are extracted for each candidate in time, spatial, frequency and statistical domains, and evaluated by a spare support vector machine (SVM) to select out ten critical features which can effectively reveal residual heartbeat information. Afterwards, an SVM model, created on the training data using the selected feature set, is applied to find high confident heartbeats from a large number of candidates in the testing data. In the second stage, the SVM classification results are further refined by two steps: (1) a rule-based classifier with two attributes named 'continuity check' and 'locality check' for outlier (false positives) removal, and (2) a heartbeat interpolation strategy for missing-heartbeat (false negatives) recovery. The framework is evaluated on a wrist-ECG dataset acquired by a semi-customized platform and also a public dataset. When the signal-to-noise ratio is as low as -7 dB, the mean absolute error of the estimated IHR is 1.4 beats per minute (BPM) and the root mean square error is 6.5 BPM. The proposed framework greatly outperforms well-established approaches, demonstrating that it can effectively identify the heartbeats from ECG signals continuously corrupted by intense motion artifacts and robustly estimate the IHR. This study is expected to contribute to robust long-term wearable IHR monitoring for pervasive heart health and fitness management.

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Section: Discussionmentioning

confidence: 99%

A novel machine learning-enabled framework for instantaneous heart rate monitoring from motion-artifact-corrupted electrocardiogram signals

2016

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“…In addition to the methods of processing biomedical signals, the application itself allows improvements, including the monolithic structure, which can be rethought for a microservice architecture 34 , which retains self-reliance characteristics and distribute the parts of the business rule. Independent developers can implement it in different languages and data storage technologies, and for these same reasons it also has greater complexity in its design, but also allows to be implemented under Wildfly 35 .…”

Section: User Friendliness and Usefulness Of Applicationmentioning

confidence: 99%

REMOTE#RESEARCH - A Telediagnosis Assistance System for Multiple-Lead Electrocardiography

Bentes¹,

Nadal²

2020

Preprint

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Background : The diffusion of telemedicine opens up a new perspective for the development of technologies furthered by Biomedical Engineering. In particular, herein we deal with those related to telediagnosis through multiple-lead electrocardiographic signals. Objectives : This study focuses on the proof-of-concept of an internet-based telemedicine system as a use case that attests to the feasibility for the development, within the university environment, of techniques for remote processing of biomedical signals for adjustable detection of myocardial ischemia episodes. Methods : At each signal lead, QRS complexes are detected and delimited with the J -point marking. The same procedure to detect the complex is used to identify the respective T wave, then the area over the ST segment is applied to detect ischemia-related elevations. The entire system is designed on web-based telemedicine services using multiuser, remote access technologies, and database.Results : The measurements for sensitivity and precision had their respective averages calculated at 11.79% and 24.21% for the lead s of lower noise. T he evaluations regarding the aspects of user friendliness and the usefulness of the application, resulted in 88.57% and 89.28% of broad or total acceptance, respectively .Conclusions : They are robust enough to enable scalability and can be offered by cloud computing, besides enabling the development of new biomedical signal processing techniques within the concept of distance services, using a modular architecture with collaborative bias.

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“…Figure 2 shows mismatched impedance values verses frequency for the two types of electrodes used; the solid green curve represents the reference electrode (ear lobe electrode), and the dashed line represents the ear electrode (each measurement was repeated 4 times and averaged; the worst case coefficient of variance (CV) for measurements was 5.2%), that would lead to large common mode signals. To attenuate common mode signals, successful digital filtering (post processing) approaches have been reported for ECG applications [ 18 , 19 ]. However, post processing alone does not suffice for large gain (>85 dB) applications, since saturation of the recording device can often occur due to PLI.…”

Section: Introductionmentioning

confidence: 99%

“…Noise added by the recording apparatus (system noise) has a substantial effect on low amplitude recordings. Figure 3 illustrates a power spectrum of a 5 minute long ECOG recording, where the horizontal dashed line is indicative of system noise (a collection of noise generated by the amplifier, electrodes, and the electrode/skin interface [ 19 , 21 , 22 ]), and the low frequency (<1 kHz) component (solid line) corresponds to biological activity. The current OEM amplifier measures to have a 0 Ω load noise floor of

when tested at the connector box located approximately 1 m away from the device, which is also required to be minimized.…”

Section: Introductionmentioning

confidence: 99%

Development of an ultra low noise, miniature signal conditioning device for vestibular evoked response recordings

Kumaragamage

Lithgow

Moussavi

2014

BioMedical Engineering OnLine

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BackgroundInner ear evoked potentials are small amplitude (<1 μVpk) signals that require a low noise signal acquisition protocol for successful extraction; an existing such technique is Electrocochleography (ECOG). A novel variant of ECOG called Electrovestibulography (EVestG) is currently investigated by our group, which captures vestibular responses to a whole body tilt. The objective is to design and implement a bio-signal amplifier optimized for ECOG and EVestG, which will be superior in noise performance compared to low noise, general purpose devices available commercially.MethodA high gain configuration is required (>85 dB) for such small signal recordings; thus, background power line interference (PLI) can have adverse effects. Active electrode shielding and driven-right-leg circuitry optimized for EVestG/ECOG recordings were investigated for PLI suppression. A parallel pre-amplifier design approach was investigated to realize low voltage, and current noise figures for the bio-signal amplifier.ResultsIn comparison to the currently used device, PLI is significantly suppressed by the designed prototype (by >20 dB in specific test scenarios), and the prototype amplifier generated noise was measured to be 4.8 nV/Hz @ 1 kHz (0.45 μVRMS with bandwidth 10 Hz-10 kHz), which is lower than the currently used device generated noise of 7.8 nV/Hz @ 1 kHz (0.76 μVRMS). A low noise (<1 nV/Hz) radio frequency interference filter was realized to minimize noise contribution from the pre-amplifier, while maintaining the required bandwidth in high impedance measurements. Validation of the prototype device was conducted for actual ECOG recordings on humans that showed an increase (p < 0.05) of ~5 dB in Signal-to-Noise ratio (SNR), and for EVestG recordings using a synthetic ear model that showed a ~4% improvement (p < 0.01) over the currently used amplifier.ConclusionThis paper presents the design and evaluation of an ultra-low noise and miniaturized bio-signal amplifier tailored for EVestG and ECOG. The increase in SNR for the implemented amplifier will reduce variability associated with bio-features extracted from such recordings; hence sensitivity and specificity measures associated with disease classification are expected to increase. Furthermore, immunity to PLI has enabled EVestG and ECOG recordings to be carried out in a non-shielded clinical environment.

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Tremor suppression in ECG

Cited by 12 publications

References 25 publications

A novel machine learning-enabled framework for instantaneous heart rate monitoring from motion-artifact-corrupted electrocardiogram signals

A novel machine learning-enabled framework for instantaneous heart rate monitoring from motion-artifact-corrupted electrocardiogram signals

REMOTE#RESEARCH - A Telediagnosis Assistance System for Multiple-Lead Electrocardiography

Development of an ultra low noise, miniature signal conditioning device for vestibular evoked response recordings

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