Quick ECG analysis for on-line holter monitoring systems. Szilágyi, L., Szilágyi, S., M., Fördos, G., & Benyó, Z. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 1(dc):1678-1681, 2006. ![Quick ECG analysis for on-line holter monitoring systems. [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex Computer-aided bedside patient monitoring requires real-time analysis of vital functions. On-line Holter monitors need reliable and quick algorithms to perform all the necessary signal processing tasks. This paper presents the methods that were conceptualized and implemented at the development of such a monitoring system at Medical Clinic No. 4 of Targu-Mures. The system performs the following ECG signal processing steps: (1) Decomposition of the ECG signals using multi-resolution wavelet transform, which also eliminates most of the high and low frequency noises. These components will serve as input for wave classification algorithms; (2) Identification of QRS complexes, P and T waves using two different algorithms: a sequential clustering and a neural-network-based classification. This latter also distinguishes normal R waves from abnormal cases; (3) Localization of several kinds of arrhythmia using a spectral method. An autoregressive model is applied to estimate the series of R-R intervals. The coefficients of the AR model are predicted using the Kalman filter, and these coefficients will determine a local spectrum for each QRS complex. By analyzing this spectrum, different arrhythmia cases are identified. The algorithms were tested using the MIT-BIH signal database and own multichannel ECG registrations. The QRS complex detection ratio is over 99.5%
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abstract = {Computer-aided bedside patient monitoring requires real-time analysis of vital functions. On-line Holter monitors need reliable and quick algorithms to perform all the necessary signal processing tasks. This paper presents the methods that were conceptualized and implemented at the development of such a monitoring system at Medical Clinic No. 4 of Targu-Mures. The system performs the following ECG signal processing steps: (1) Decomposition of the ECG signals using multi-resolution wavelet transform, which also eliminates most of the high and low frequency noises. These components will serve as input for wave classification algorithms; (2) Identification of QRS complexes, P and T waves using two different algorithms: a sequential clustering and a neural-network-based classification. This latter also distinguishes normal R waves from abnormal cases; (3) Localization of several kinds of arrhythmia using a spectral method. An autoregressive model is applied to estimate the series of R-R intervals. The coefficients of the AR model are predicted using the Kalman filter, and these coefficients will determine a local spectrum for each QRS complex. By analyzing this spectrum, different arrhythmia cases are identified. The algorithms were tested using the MIT-BIH signal database and own multichannel ECG registrations. The QRS complex detection ratio is over 99.5%},
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