Body surface potential mapping for detection of myocardial infarct sites

Zarychta, Piotr; Smith, Fiona E.; King, Susan T; Haigh, A.J.; Klinge, Annette; Zheng, Dingchang; Stevens, S.; Allen, John; Okelarin, A.; Langley, Philip; Murray, Alan

doi:10.1109/cic.2007.4745451

Cited by 18 publications

(9 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ECG signal is a representation of the bioelectrical activity of the heart representing the cyclical contractions and relaxations of the human heart muscles. To acquire the signal, ECG devices with varying number of electrodes (3)(4)(5)(6)(7)(8)(9)(10)(11)(12) can be used. Multilead systems exceeding 12 and up to 120 electrodes are also available [3].…”

Section: A Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

A Patient-Adaptive Profiling Scheme for ECG Beat Classification

Faezipour

Saeed

Bulusu

et al. 2010

IEEE Trans. Inform. Technol. Biomed.

View full text Add to dashboard Cite

Abstract-Recent trends in clinical and telemedicine applications highly demand automation in electrocardiogram (ECG) signal processing and heart beat classification. A patient-adaptive cardiac profiling scheme using repetition-detection concept is proposed in this paper. We first employ an efficient wavelet-based beat-detection mechanism to extract precise fiducial ECG points. Then, we implement a novel local ECG beat classifier to profile each patient's normal cardiac behavior. ECG morphologies vary from person to person and even for each person, it can vary over time depending on the person's physical condition and/or environment. Having such profile is essential for various diagnosis (e.g., arrhythmia) purposes. One application of such profiling scheme is to automatically raise an early warning flag for the abnormal cardiac behavior of any individual. Our extensive experimental results on the MIT-BIH arrhythmia database show that our technique can detect the beats with 99.59% accuracy and can identify abnormalities with a high classification accuracy of 97.42%.

show abstract

Section: A Backgroundmentioning

confidence: 99%

“…To acquire the signal, ECG devices with varying number of electrodes (3)(4)(5)(6)(7)(8)(9)(10)(11)(12) can be used. Multilead systems exceeding 12 and up to 120 electrodes are also available [3]. Accurate detection of the ECG beats is the key requirement for detecting all types of arrhythmia.…”

Section: A Backgroundmentioning

confidence: 99%

A Patient-Adaptive Profiling Scheme for ECG Beat Classification

Faezipour

Saeed

Bulusu

et al. 2010

IEEE Trans. Inform. Technol. Biomed.

View full text Add to dashboard Cite

show abstract

“…The ECG can be recorded with the surface electrodes placed on the limbs and the chest. The ECG devices use a varying number of electrodes ranging from 3 to 12 for signal acquisition; while systems using more electrodes exceeding 12 and up to 120 are also available [25]. Each normal cycle of an ECG signal contains P, QRS, and T waves (e.g., see Figure 1).…”

Section: Methods Of Ecg Characterizationmentioning

confidence: 99%

Identifying Individuals Using Eigenbeat Features of Electrocardiogram

Singh

2013

Journal of Engineering

View full text Add to dashboard Cite

The authors of this paper present a new method to characterize the electrocardiogram (ECG) for individual identification. We propose an ECG biometric system which is insensitive to noise signals and muscle flexure. The method utilizes the principal of linearly projecting the heartbeat features into a subspace of lower dimension using an orthogonal basis that represents the most significant features to distinguish the individuals. The performance of the proposed biometric system is evaluated on the subjects of both health statuses such as the ECG recordings of MIT-BIH Arrhythmia database and the ECG recordings of normal subjects prepared at IIT(BHU). The result demonstrates that the derived eigenbeat features from proposed ECG characterization perform better and achieve the recognition accuracy of 91.42% and 95.55% on the subjects of MIT-BIH Arrhythmia database and IIT(BHU) database, respectively.

show abstract

“…It can be recorded with the surface electrodes placed on the limbs and chest. ECG devices use varying number of electrodes ranging from 3 to 12 for signal acquisition while the system using more electrodes exceeding 12 and up to 120 is also available [22]. Each normal cycle of an ECG signal contains P, QRS, and T waves (for instance see Figure 1).…”

Section: Ecg Preamblementioning

confidence: 99%

Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Singh

Ray

2012

ISRN Signal Processing

View full text Add to dashboard Cite

This paper presents the effectiveness of bioelectrical signals such as the electrocardiogram (ECG) and the electroencephalogram (EEG) for biometric applications. Studies show that the impulses of cardiac rhythm and electrical activity of the brain recorded in ECG and EEG, respectively; have unique features among individuals, therefore they can be suggested to be used as biometrics for identity verification. The favourable characteristics to use the ECG or EEG signals as biometric include universality, measurability, uniqueness and robustness. In addition, they have the inherent feature of vitality that signifies the life signs offering a strong protection against spoof attacks. Unlike conventional biometrics, the ECG or EEG is highly confidential and secure to an individual which is difficult to be forged. We present a review of methods used for the ECG and EEG as biometrics for individual authentication and compare their performance on the datasets and test conditions they have used. We illustrate the challenges involved in using the ECG or EEG as biometric primarily due to the presence of drastic acquisition variations and the lack of standardization of signal features. In order to determine the large-scale performance, individuality of the ECG or EEG is another challenge that remains to be addressed.

show abstract

Body surface potential mapping for detection of myocardial infarct sites

Abstract: Using the additional information from multi-lead body

Cited by 18 publications

References 5 publications

A Patient-Adaptive Profiling Scheme for ECG Beat Classification

A Patient-Adaptive Profiling Scheme for ECG Beat Classification

Identifying Individuals Using Eigenbeat Features of Electrocardiogram

Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Contact Info

Product

Resources

About