ECG-based detection of body position changes in ischemia monitoring

García, J.; Åström, Magnus; Mendive, J.; Laguna, Pablo; Sörnmo, Leif

doi:10.1109/tbme.2003.812208

Cited by 43 publications

(33 citation statements)

References 17 publications

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“…First, the class-mean is subtracted from for all the BMA classes to get (5) where is a mean-subtracted residual BMA vector for the BMA class. The BMA vector is reconstructed from projections on the computed set of eigenvectors to capture its contents in the motion artifact subspace defined by in the prior training as (6) where is the reconstructed motion artifact. A measure of error in the reconstruction in motion artifact is denoted by and defined as…”

Section: Body Movement Classificationmentioning

confidence: 99%

“…However, the reported performance is not very satisfactory as the wavelet based representation does not separate the in-band BMA signal from the ECG. In other works related to BMA analysis from nonambulatory ECG, body position changes are detected for ischemia monitoring in [4]- [6]. In [4], [6], Karhunen-Loeve transform features of the ECG beats are analyzed to detect position changes.…”

mentioning

confidence: 99%

“…In other works related to BMA analysis from nonambulatory ECG, body position changes are detected for ischemia monitoring in [4]- [6]. In [4], [6], Karhunen-Loeve transform features of the ECG beats are analyzed to detect position changes. A synthesized vectorcardiograph based approach has been proposed in [5], [6], where a series of angles for the three orthogonal leads X, Y and Z are derived using a loop alignment method [5], [7].…”

mentioning

confidence: 99%

“…In [4], [6], Karhunen-Loeve transform features of the ECG beats are analyzed to detect position changes. A synthesized vectorcardiograph based approach has been proposed in [5], [6], where a series of angles for the three orthogonal leads X, Y and Z are derived using a loop alignment method [5], [7]. The angle series is then analyzed to detect the body position changes.…”

mentioning

confidence: 99%

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Body Movement Activity Recognition for Ambulatory Cardiac Monitoring

Pawar

Chaudhuri

Duttagupta

2007

IEEE Trans. Biomed. Eng.

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Abstract-Wearable electrocardiogram (W-ECG) recorders are increasingly in use by people suffering from cardiac abnormalities who also choose to lead an active lifestyle. The challenge presently is that the ECG signal is influenced by motion artifacts induced by body movement activity (BMA) of the wearer. The usual practice is to develop effective filtering algorithms which will eliminate artifacts. Instead, our goal is to detect the motion artifacts and classify the type of BMA from the ECG signal itself. We have recorded the ECG signals during specified BMAs, e.g., sitting still, walking, movements of arms and climbing stairs, etc. with a singlelead system. The collected ECG signal during BMA is presumed to be an additive mix of signals due to cardiac activities, motion artifacts and sensor noise. A particular class of BMA is characterized by applying eigen decomposition on the corresponding ECG data. The classification accuracies range from 70% to 98% for various class combinations of BMAs depending on their uniqueness based on this technique. The above classification is also useful for analysis of P and T waves in the presence of BMA.

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Section: Body Movement Classificationmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Body Movement Activity Recognition for Ambulatory Cardiac Monitoring

Pawar

Chaudhuri

Duttagupta

2007

IEEE Trans. Biomed. Eng.

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“…These authors have characterised these noise sources effects on data exploitation. The noise sources are defined as powerline interference (Huhta and Webster, 1973), electrode contact noise (Oster, 2000), motion artefacts (Garcia et al, 2003), muscle flexor, baseline drift/ sensor thermal noise (Barros et al, 1995) and ECG amplitude drift with respiration (Lindberg and Oberg, 1991;Cysarz et al, 2008), instrumentation noise (Fernandez and Pallas-Arney, 2000), and electrosurgical noise, which is not relevant to biometrics. The noise sources are expressed in the heartbeat trace as high frequency (intra-beat) and low frequency (inter-beat) components.…”

Section: Signal Processing 221 Noise Removalmentioning

confidence: 99%

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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Detection of body position changes using the surface electrocardiogram

Åström

García

Laguna

et al. 2003

Med. Biol. Eng. Comput.

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A method for detecting body position changes that uses the surface vectorcardiogram (VCG) is presented. Such changes are often manifested as sudden shifts in the electrical axis of the heart and can erroneously be interpreted as acute ischaemic events. Axis shifts were detected by analysing the rotation angles obtained from the alignment of successive VCG loops to a reference loop. Following the rejection of angles originating from noise events, the detection of body position changes was performed on the angle series using a Bayesian approach. On a database of ECG recordings from normal subjects performing a predefined sequence of body position changes, a detection rate of 92% and a false alarm rate of 7% was achieved.

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ECG-based detection of body position changes in ischemia monitoring

Cited by 43 publications

References 17 publications

Body Movement Activity Recognition for Ambulatory Cardiac Monitoring

Body Movement Activity Recognition for Ambulatory Cardiac Monitoring

Heartbeat biometrics: a sensing system perspective

Detection of body position changes using the surface electrocardiogram

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