Multisensor fusion for atrial and ventricular activity detection in coronary care monitoring

Hernàndez, A.; Carrault, Guy; Mora, F.; Thoraval, Laurent; Passariello, G.; Schleich, Jean Marc

doi:10.1109/10.790494

Cited by 37 publications

(24 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite its good SNR enhancement and the improvement of P wave detection (Hernández et al,1999;Jeras et al,2003), this technique is rarely used due to its semi-invasive aspect.…”

Section: Esophageal Electrocardiogram (Eecg) Basedmentioning

confidence: 99%

P wave detector with PP rhythm tracking: evaluation in different arrhythmia contexts

Portet¹

2008

Physiol. Meas.

View full text Add to dashboard Cite

Abstract. Automatic detection of atrial activity (P waves) in electrocardiogram (ECG) is a crucial task to diagnose the presence of arrhythmias. P wave is difficult to detect and most of the approaches in the literature have been evaluated on normal sinus rhythms and rarely considered arrhythmia contexts other than atrial flutter and fibrillation. A novel knowledge-based P wave detector algorithm is presented. It is self adaptive to the patient and able to deal with certain arrhythmias by tracking the PP rhythm. The detector has been tested on 12 records of the MIT-BIH arrhythmia database containing several ventricular and supra-ventricular arrhythmias. On the overall records, the detector demonstrates Se=96.60% and Pr=95.46%; for the normal sinus rhythm, it reaches Se=97.76% and Pr=96.80% and, in the case of Mobitz type II, it demonstrates Se=72.79% and Pr=99.51%. It also shows good performance for trigeminy, and bigeminy and outperforms some more sophisticated techniques. Although the results emphasize the difficulty of P wave detection in difficult arrhythmias (supra and ventricular tachycardias), it shows that domain knowledge can support efficiently signal processing techniques.

show abstract

“…Despite its good SNR enhancement and the improvement of P wave detection (Hernández et al,1999;Jeras et al,2003), this technique is rarely used due to its semi-invasive aspect.…”

Section: Esophageal Electrocardiogram (Eecg) Basedmentioning

confidence: 99%

P wave detector with PP rhythm tracking: evaluation in different arrhythmia contexts

Portet¹

2008

Physiol. Meas.

View full text Add to dashboard Cite

show abstract

“…the Pd versus Pfa curves), and corresponds to the nearest point of the ideal detection point (i.e. Pfa = 0; Pd = 1) (HERNÁNDEZ et al, 1999). 3.4.…”

Section: Scoring Criterionmentioning

confidence: 99%

Evaluation of real-time QRS detection algorithms in variable contexts

Portet

Hernández

Carrault

2005

Med. Biol. Eng. Comput.

Self Cite

View full text Add to dashboard Cite

A method is presented to evaluate the detection performance of real-time QRS detection algorithms to propose a strategy for the adaptive selection of QRS detectors, under variable signal contexts. Signal contexts are defined as different combinations of QRS morphologies and clinical noise. Four QRS detectors are compared under these contexts by means of a multivariate analysis. This evaluation strategy is general and can be easily extended to a larger number of detectors.A set of morphology contexts, corresponding to 8 QRS morphologies (Normal, PVC, premature atrial beat, paced beat, LBBB, fusion, RBBB, junctional premature beat), has been extracted from 17 standard ECG records.For each morphology context, the set of extracted beats, ranging from 30 to 23000, are resampled to generate 50 realizations of 20 concatenated beats. These realizations are then used as input to the QRS detectors, without noise, and with 3 different types of additive clinical noise (electrode motion artefact, muscle artefact, baseline wander) at 3 signal-to-noise ratios (5dB, -5dB, -15dB). Performance is assessed by the number of errors, which reflects both false alarms and missed beats.The results show that the evaluated detectors are indeed complementary. For example, the Pan and Tompkins's detector is the best in most contexts but the Okada's detector generates less errors in presence of electrode motion artefact. These results will be particularly useful to the development of a real-time system that will be able to choose the best QRS detector according to the current context.

show abstract

“…(1) where k denotes the discrete time index, while E(k), R(k), B(k), P(k) refer, respectively to ECG, Respiratory, ABP, and PLETH channels in equation (1).…”

Section: Data Fusionmentioning

confidence: 99%

Data Fusion of Multimodal Cardiovascular Signals

Kenneth

Acharya

Kannathal

et al. 2005

2005 IEEE Engineering in Medicine and Biology 27th Annual Conference

View full text Add to dashboard Cite

The electrocardiogram (ECG) is a representative signal containing useful information about the condition of the heart. The shape and size of the P-QRS-T wave, the R-R interval etc. may help to identify the nature of disease afflicting the heart. However, human observer can not directly monitor these subtle details. Hence, the fusion of ECG, blood pressure, saturated oxygen content and respiratory data for achieving improved clinical diagnosis of patients in cardiac care units. Therefore, computer based analysis and display, is highly useful in diagnostics. The study demonstrates the feasibility of fuzzy logic based data fusion of the heterogeneous signals for the detection of life threatening states. Important parameters are derived from multimodal data and rule based approaches have been used. Fuzzified region for various abnormality conditions have been obtained which demonstrate the efficacy of the approach in various test cases. Comprehensive pictures showing the condition of the patient in various states will help physician in making a timely assessment in an intensive care set up.

show abstract

Multisensor fusion for atrial and ventricular activity detection in coronary care monitoring

Cited by 37 publications

References 13 publications

P wave detector with PP rhythm tracking: evaluation in different arrhythmia contexts

P wave detector with PP rhythm tracking: evaluation in different arrhythmia contexts

Evaluation of real-time QRS detection algorithms in variable contexts

Data Fusion of Multimodal Cardiovascular Signals

Contact Info

Product

Resources

About