Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Farin Kahroba; Maryam Mohebbi; Hamed Danandeh Hesar
Volume 11, Issue 2 , June 2017, , Pages 187-199
Abstract
Sudden cardiac death (SCD) is one of the most significant and common causes of heart related deaths around the world. It is believed that SCD can be predicted using signatures and features extracted from ECG signal. These signatures may be seen as arrhythmia or abnormalities in the ECG signal. In this ...
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Sudden cardiac death (SCD) is one of the most significant and common causes of heart related deaths around the world. It is believed that SCD can be predicted using signatures and features extracted from ECG signal. These signatures may be seen as arrhythmia or abnormalities in the ECG signal. In this paper, a monitoring index is introduced for early detection of SCD. This index is acquired by filtering the ECG signal using a nonlinear ECG dynamical model and extended Kalman filter (EKF). The nonlinear dynamical model was a modified version of polar ECG dynamical model proposed by Mc. Sharry et.al. In our algorithm, first the ECG dynamical model is extracted. Then an EKF is applied on the signal. Using the fidelity index extracted from the innovation signal yielded by EKF, a novel algorithm detects the SCD related arrhythmias and abnormalities. The proposed method was evaluated on Physionet Sudden Cardiac Death Holter database. Twenty records corresponding to patients having SCD and eighteen records corresponding to healthy patients were extracted from this database. The evaluation results showed that our proposed monitoring index correctly detected 17 SCDs out of 20 (85% accuracy).
