AI Article Synopsis
- Beat detection is crucial for accurately processing electrocardiogram (ECG) signals, which can suffer from various distortions due to noise.
- The proposed method builds on Christov's algorithm by using an adaptive threshold on a transformed ECG signal, called the complex lead, to improve beat detection.
- This new approach includes estimating independent components to create a 'complex component' signal that enhances ECG activity, making it more reliable in noisy environments like Holter ECGs and telemedicine applications.
Article Abstract
Beat detection is a basic and fundamental step in electrocardiogram (ECG) processing. In many ECG applications strong artifacts from biological or technical sources could appear and cause distortion of ECG signals. Beat detection algorithm desired property is to avoid these distortions and detect beats in any situation. Our developed method is an extension of Christov's beat detection algorithm, which detects beat using combined adaptive threshold on transformed ECG signal (complex lead). Our offline extension adds estimation of independent components of measured signal into the transformation of ECG creating a signal called complex component, which enhances ECG activity and enables beat detection in presence of strong noises. This makes the beat detection algorithm much more robust in cases of unpredictable noise appearances, typical for holter ECGs and telemedicine applications of ECG. We compared our algorithm with the performance of our implementation of the Christov's and Hamilton's beat detection algorithm.
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| http://dx.doi.org/10.1016/j.medengphy.2012.07.017 | DOI Listing |
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