Automatic ballistocardiogram (BCG) beat detection using a template matching approach

Shin, Jonghwan; Choi, Byung-Hee; Lim, Yong Gyu; Jeong, Do-Un; Park, K. S.

doi:10.1109/iembs.2008.4649363

Cited by 66 publications

(31 citation statements)

References 8 publications

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“…Thereby we take advantage of a good spatial sampling of the basal temporal and facial area. Some previous groups suggested to cross-correlate a PA artifact template (Mijovic et al 2012) or a signal from a sensor (Shin et al 2008) to detect markers. We propose a novel and simple method to detect peaks of PA for subsequent AAS correction in order to improve the quality of EEG data acquired during fMRI.…”

Section: Introductionmentioning

confidence: 99%

Pulse Artifact Detection in Simultaneous EEG–fMRI Recording Based on EEG Map Topography

et al. 2014

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One of the major artifact corrupting electroencephalogram (EEG) acquired during functional magnetic resonance imaging (fMRI) is the pulse artifact (PA). It is mainly due to the motion of the head and attached electrodes and wires in the magnetic field occurring after each heartbeat. In this study we propose a novel method to improve PA detection by considering the strong gradient and inversed polarity between left and right EEG electrodes. We acquired high-density EEG-fMRI (256 electrodes) with simultaneous electrocardiogram (ECG) at 3 T. PA was estimated as the voltage difference between right and left signals from the electrodes showing the strongest artifact (facial and temporal). Peaks were detected on this estimated signal and compared to the peaks in the ECG recording. We analyzed data from eleven healthy subjects, two epileptic patients and four healthy subjects with an insulating layer between electrodes and scalp. The accuracy of the two methods was assessed with three criteria: (i) standard deviation, (ii) kurtosis and (iii) confinement into the physiological range of the inter-peak intervals. We also checked whether the new method has an influence on the identification of epileptic spikes. Results show that estimated PA improved artifact detection in 15/17 cases, when compared to the ECG method. Moreover, epileptic spike identification was not altered by the correction. The proposed method improves the detection of pulse-related artifacts, particularly crucial when the ECG is of poor quality or cannot be recorded. It will contribute to enhance the quality of the EEG increasing the reliability of EEG-informed fMRI analysis.

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Section: Introductionmentioning

confidence: 99%

Pulse Artifact Detection in Simultaneous EEG–fMRI Recording Based on EEG Map Topography

et al. 2014

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“…Therefore, our algorithm was compared with the algorithms of BCG-based heartbeat segmentation. Shin et al [7] used a template matching approach for beat detection in BCG, and got a sensitivity of 95.16% and a positive predictive value of 94.76% on 145 heartbeats. However, their approach had several disadvantages: Firstly, the template was constructed by the expert with an empirical analysis, making it unusable for automatic application.…”

Section: Discussionmentioning

confidence: 99%

An Approach of Heartbeat Segmentation in Seismocardiogram by Matched-Filtering

Tang

2015

2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics

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Heartbeat segmentation in Seismocardiogram (SCG) provides the fundamentals for automated SCG analysis. Traditionally, waveform detection of each cardiac cycle in SCG was depended on the reference QRS complex in Electrocardiogram (ECG). The dominant wave in SCG, namely W complex, could be used as the feature wave for heartbeat segmentation. In this paper, matched-filtering approach was developed and evaluated for W complex detection in SCG without noise suppression stage. Firstly, the template of W complex was selected automatically by the triangle character in SCG and then was used as the coefficients of the finite impulse response (FIR), which greatly enhanced W complexes and attenuated other regions. Subsequently, W complex was detected using the triangle structure, and cardiac cycles and triangle structures were further analyzed for the reduction of false-positive and false-negative detections. The performance of the proposed algorithm was tested on 20 records (each record lasted 50 min) of the combined measurement of ECG, Breathing and Seismocardiograms Database (CEBSDB). The detection rate of 98.74%, the sensitivity of 99.33% and the positive prediction of 99.41 % was achieved on the CEBSDB, respectively. In conclusion, matched-filtering is reliable for the heartbeat segmentation of Seismocardiogram.

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“…Although PSG is usually used as reference for unobtrusive measurements, only few papers have compared the different sensor systems between each other. In [40], a comparison between an air-mattress, load cell and EMFi-film system was done, but focussed only on the automatic beat detection in the ballistocardiogram with similar results for all systems. As to our knowledge, a detailed comparison including respiration and movements has not been done yet.…”

Section: Scientific Studiesmentioning

confidence: 99%