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Abstract-For assessment of specific cardiac pathologies, vectorcardiography is generally considered superior with respect to electrocardiography. Existing vectorcardiography methods operate by calculating the vectorcardiogram (VCG) as a fixed linear combination of ECG signals. These methods, with the inverse Dower matrix method the current standard, are therefore not flexible with respect to different body compositions and geometries. Hence, they cannot be applied with accuracy on patients that do not conform to the fixed standard. Typical examples of such patients are obese patients or fetuses. For the latter category, when recording the fetal ECG from the maternal abdomen the distance of the fetal heart with respect to the electrodes is unknown. Consequently, also the signal attenuation/transformation per electrode is not known. In this paper, a Bayesian method is developed that estimates the VCG and, to some extent, also the signal attenuation in multichannel ECG recordings from either the adult 12-lead ECG or the maternal abdomen. This is done by determining for which VCG and signal attenuation the joint probability over both these variables is maximal given the observed ECG signals. The underlying joint probability distribution is determined by assuming the ECG signals to originate from scaled VCG projections and additive noise. With this method, a VCG, tailored to each specific patient, is determined. The method is compared to the inverse Dower matrix method by applying both methods on standard 12-lead ECG recordings and evaluating the performance in predicting ECG signals from the determined VCG. In addition, to model nonstandard patients, the 12-lead ECG signals are randomly scaled and, once more, the performance in predicting ECG signals from the VCG is compared between both methods. Finally, both methods are also compared on fetal ECG signals that are obtained from the maternal abdomen. For patients conforming to the standard, both methods perform similarly, with the developed method performing marginally better. For scaled ECG signals and fetal ECG signals, the developed method significantly outperforms the inverse Dower matrix method.

Hierarchical mixture of experts (HME) is a widely adopted probabilistic divide-and-conquer regression model. We extend the variational inference algorithm for HME by using automatic relevance determination (ARD) priors. Unlike Gaussian priors, ARD allows for a few model parameters to take on large values, while forcing others to zero. Thus, using ARD priors encourages sparse models. Sparsity is known to be advantageous to the generalization capability as well as interpretability of the models. We present the variational inference algorithm for sparse HME in detail. Subsequently, we evaluate the sparse HME approach in building objective speech quality assessment algorithms, that are required to determine the quality of service in telecommunication networks.

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In this work we propose the calibrated-mean-opinion-score (CMOS), which accounts for varying levels of precision and bias across subjects in a listening test. We adopt the Bayesian statistical framework, where the hyper-parameters of priors are learned via empirical Bayes, and the posterior is approximated by a computationally inexpensive variational technique. As our experimental results show, CMOS is more robust to noisy and biased subjects than MOS. As a result, CMOS can be used to improve the reliability of listening test results when a small test panel is used. To correct for the subjects in the test panel, calibration signals are required. Calibration signals are rated by a panel larger than the test panel. The key to saving human labor and cost is that only a few calibration signals are required, and that it is possible to share calibration signals across listening tests.