Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: comparison with signal averaging techniques

Laguna, Pablo; Jané, R.; Meste, Olivier; Poon, P.W.F.; Caminal, P.; Rix, Hervé; Thakor, Nitish V.

doi:10.1109/10.161335

Cited by 139 publications

(69 citation statements)

References 13 publications

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“…Other effective low computation cost filtering includes regression spline reconstruction (Stegle et al, 2008) and local temporal averaging. Researchers have also proposed methods for evaluating the noise source relative to the known ECG trace (Laguna et al, 1992;Olmos and Laguna, 2000). However, these latter techniques provide limited improvement over bandpass filtering because the noise environment is nearly constant.…”

Section: Signal Processing 221 Noise Removalmentioning

confidence: 99%

“…The basic process is to use moving windows (Kohler et al, 2002) or a multi-scale approach (Laciar et al, 2003). A less efficient approach is to employ a Fourier correlation approach, but this has limitations of performance based upon the coarseness of the power spectral density attributes (Laguna et al, 1992). Alignment output can be visualised as a waterfall diagram (Figure 9).…”

Section: Heartbeat Alignmentmentioning

confidence: 99%

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Heartbeat biometrics: a sensing system perspective

Israel¹,

Irvine²

2012

IJCB

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This paper reviews the emerging research into exploitation of heartbeat data as a biometric for human identification. A variety of methods have been proposed for acquiring heartbeat signatures and a range of processing methods has been examined. We approach the biometric identification and verification problem by characterising the three major factors affecting performance: individual variants, environmental variants, and sensor variants. The ability to collect and process the signal, exploit the data for individual identification or verification, and disseminate the information depends on all three of these factors. Within each component, we have identified the relevant research. Where possible, we have tied these research papers to practical examples using high resolution ECG data. The research indicates that the heartbeat contains rich information about the individual, their level of anxiety, and the cardiac state.Keywords: biometrics; heartbeat; ECG; sensing system; cardiovascular function; human identification. areas of interest include novel biometrics, signal and image processing, quantification and modelling of data quality, and cognitive modeling. He was a Principal Investigator for DARPA's Human Identification at a Distance Program (HumanID). He serves on planning committees for IEEE and SPIE, and has served on several advisory panels for the Department of Defence and the Department of Energy. He has authored over a hundred journal and conference papers and holds a PhD in Mathematical Statistics from Yale University.

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Section: Signal Processing 221 Noise Removalmentioning

confidence: 99%

Section: Heartbeat Alignmentmentioning

confidence: 99%

Heartbeat biometrics: a sensing system perspective

Israel¹,

Irvine²

2012

IJCB

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“…The detector is based on an adaptive threshold in the ECG amplitude that has been previously filtered with a band-pass filter (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22).…”

Section: R Wave Detectormentioning

confidence: 99%

“…The procedure implemented for QT pattern cancellation is called Adaptive Impulse Correlated Filter (AICF), which was proposed by Thakor 29 and used by Laguna 16 and Almenar 2 for repetitive component detection in ECG, and removal of the maternal ECG component in a fetal ECG 18 . Another cancellation technique like the one described by Stridh 28 cannot be used because it needs three leads.…”

Section: Qt Pattern Cancellationmentioning

confidence: 99%

“…The proposed adaptive filter behaves like an exponential averager. The adaptive filter coefficients store an averaged version of the signal 16 . The adaptive filter length is essential for this application because the averaged pattern is the signal that begins with the regeneration times and has a length that is equal to the filter length.…”

Section: Qt Pattern Cancellationmentioning

confidence: 99%

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P and R Wave Detection in Complete Congenital Atrioventricular Block

et al. 2008

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Complete atrioventricular block (type III AVB) is characterized by an absence of P wave transmission to ventricles. This implies that QRS complexes are generated in an autonomous way and are not coordinated with P waves. This work introduces a new algorithm for the detection of P waves for this type of pathology using non-invasive electrocardiographic surface leads.The proposed algorithm is divided into three stages. In the first stage, the R waves located by a QRS detector are used to generate the RR series and time references for the other stages of the algorithm. In the second stage, the ventricular activity (QT segment) is removed by using an adaptive filter that obtains an averaged pattern of the QT segment. In the third stage, a new P wave detector is applied to the residual signal obtained after QT cancellation in order to detect P wave locations and get the PP time series.Eight Holter records from patients with congenital type III AVB were used to verify the proposed algorithm. Although further improvements should be made to improve the algorithm's performance, the results obtained show high average values of sensitivity (90.52 %) and positive prediction (90.98%). KeywordsComplete congenital atrioventricular block, P and R wave detection, QRS, adaptive impulse correlated filter, SNEO.2 2

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Evoked Potentials, Adaptive Filtering of

Chan¹,

Qiu

Poon

et al. 2006

Wiley Encyclopedia of Biomedical Engineering

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Evoked potentials (EPs) are time‐varying signals typically buried in relatively large background noise. Tracking variations in both the latency and the amplitude of EPs are important in quantifying properties of the nervous system. Adaptive filtering is a powerful tool for tracking such variations. In this article, EP signal estimation by adaptive filtering was introduced in the framework of an adaptive signal enhancer (ASE). Two inputs exist in ASE: the primary input, or the raw recorded data, that contains a signal plus noise, and the reference input that contains a signal related to, but not necessarily having the same waveform as the signal components in the primary input, and an additive noise, which is not correlated to the noise in the primary input. ASE requires a proper reference input signal for its optimal performance. Several effective reference input designs are introduced in this article. The results confirm the superior performance of ASE over the traditional ensemble average method.

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Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: comparison with signal averaging techniques

Cited by 139 publications

References 13 publications

Heartbeat biometrics: a sensing system perspective

Heartbeat biometrics: a sensing system perspective

P and R Wave Detection in Complete Congenital Atrioventricular Block

Evoked Potentials, Adaptive Filtering of

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