Detection of body position changes from the ECG using a Laplacian noise model

• A body position change (BPC) detector based on a Laplacian noise model is proposed.
• The KLT domain noise was better characterized by the Laplacian noise model than the Gaussian one.
• The proposed detector overperforms previous ones in both sensitivity/specificity and false alarm rate.
• The BPC detector improves the accuracy of ischemia monitoring.

Body position changes (BPCs) are manifested as shifts in the electrical axis of the heart, which may lead to ST changes in the ECG, misclassified as ischemic events. This paper presents a novel BPC detector based on a Laplacian noise model. It is assumed that a BPC can be modelled as a step-like change in the two coefficient series that result from the Karhunen–Loève transform of the QRS complex and the ST–T segment. The generalized likelihood ratio test is explored for detection, where the statistical parameters of the Laplacian model are subject to estimation. Two databases are studied: one for assessing detection performance in healthy subjects who perform BPCs, and another for assessing the false alarm rate in ECGs recorded during percutaneous transluminal coronary angiography. The resulting probability of detection (PD) and probability of false alarm (PF) are 0.94 and 0.00, respectively, whereas the false alarm rate in ischemic recordings is 1 event/h. The proposed detector outperforms an existing detector based on the Gaussian noise model which achieved a PD/PF of 0.90/0.01 and a false alarm rate of 2 events/h. Analysis of the log-likelihood function for the Gaussian and Laplacian noise models show that latter model is more adequate.