A new method to detect snoring episodes in sleep sound recordings is proposed. Sleep sound segments (i.e., 'sound episodes' or simply 'episodes') are classified as snores and nonsnores according to their subband energy distributions. The similarity of inter- and intra-individual spectral energy distributions motivated the representation of the feature vectors in a lower dimensional space. Episodes have been efficiently represented in two dimensions using principal component analysis, and classified as snores or nonsnores. The sound recordings were obtained from individuals who are suspected of OSAS pathology while they were connected to the polysomnography in Gülhane Military Medical Academy Sleep Studies Laboratory (GMMA-SSL), Ankara, Turkey. The data from 30 subjects (18 simple snorers and 12 OSA patients) with different apnoea/hypopnea indices were classified using the proposed algorithm. The system was tested by using the manual annotations of an ENT specialist as a reference. The accuracy for simple snorers was found to be 97.3% when the system was trained using only simple snorers' data. It drops to 90.2% when the training data contain both simple snorers' and OSA patients' data. (Both of these results were obtained by using training and testing sets of different individuals.) In the case of snore episode detection with OSA patients the accuracy is 86.8%. All these results can be considered as acceptable values to use the system for clinical purposes including the diagnosis and treatment of OSAS. The method proposed here has been used to develop a tool for the ENT clinic of GMMA-SSL that provides information for objective evaluation of sleep sounds.

In this paper, 'snore regularity' is studied in terms of the variations of snoring sound episode durations, separations and average powers in simple snorers and in obstructive sleep apnoea (OSA) patients. The goal was to explore the possibility of distinguishing among simple snorers and OSA patients using only sleep sound recordings of individuals and to ultimately eliminate the need for spending a whole night in the clinic for polysomnographic recording. Sequences that contain snoring episode durations (SED), snoring episode separations (SES) and average snoring episode powers (SEP) were constructed from snoring sound recordings of 30 individuals (18 simple snorers and 12 OSA patients) who were also under polysomnographic recording in Gülhane Military Medical Academy Sleep Studies Laboratory (GMMA-SSL), Ankara, Turkey. Snore regularity is quantified in terms of mean, standard deviation and coefficient of variation values for the SED, SES and SEP sequences. In all three of these sequences, OSA patients' data displayed a higher variation than those of simple snorers. To exclude the effects of slow variations in the base-line of these sequences, new sequences that contain the coefficient of variation of the sample values in a 'short' signal frame, i.e., short time coefficient of variation (STCV) sequences, were defined. The mean, the standard deviation and the coefficient of variation values calculated from the STCV sequences displayed a stronger potential to distinguish among simple snorers and OSA patients than those obtained from the SED, SES and SEP sequences themselves. Spider charts were used to jointly visualize the three parameters, i.e., the mean, the standard deviation and the coefficient of variation values of the SED, SES and SEP sequences, and the corresponding STCV sequences as two-dimensional plots. Our observations showed that the statistical parameters obtained from the SED and SES sequences, and the corresponding STCV sequences, possessed a strong potential to distinguish among simple snorers and OSA patients, both marginally, i.e., when the parameters are examined individually, and jointly. The parameters obtained from the SEP sequences and the corresponding STCV sequences, on the other hand, did not have a strong discrimination capability. However, the joint behaviour of these parameters showed some potential to distinguish among simple snorers and OSA patients.

In bioelectric inverse problems, one seeks to recover bioelectric sources from remote measurements using a mathematical model that relates the sources to the measurements. Due to attenuation and spatial smoothing in the medium between the sources and the measurements, bioelectric inverse problems are generally ill-posed. Bayesian methodology has received increasing attention recently to combat this ill-posedness, since it offers a general formulation of regularization constraints and additionally provides statistical performance analysis tools. These tools include the estimation error covariance and the marginal probability density of the measurements (known as the "evidence") that allow one to predictively quantify and compare experimental designs. These performance analysis tools have been previously applied in inverse electroencephalography and magnetoencephalography, but only in relatively simple scenarios. The main motivation here was to extend the utility of Bayesian estimation techniques and performance analysis tools in bioelectric inverse problems, with a particular focus on electrocardiography. In a simulation study we first investigated whether Bayesian error covariance, computed without knowledge of the true sources and based on instead statistical assumptions, accurately predicted the actual reconstruction error. Our study showed that error variance was a reasonably reliable qualitative and quantitative predictor of estimation performance even when there was error in the prior model. We also examined whether the evidence statistic accurately predicted relative estimation performance when distinct priors were used. In a simple scenario our results support the hypothesis that the prior model that maximizes the evidence is a good choice for inverse reconstructions.

The usual goal in inverse electrocardiography (ECG) is to reconstruct cardiac electrical sources from body surface potentials and a mathematical model that relates the sources to the measurements. Due to attenuation and smoothing that occurs in the thorax, the inverse ECG problem is ill-posed and imposition of a priori constraints is needed to combat this ill-posedness. When the problem is posed in terms of reconstructing heart surface potentials, solutions have not yet achieved clinical utility; limitations include the limited availability of good a priori information about the solution and the lack of a "good" error metric. We describe an approach that combines body surface measurements and standard forward models with two additional information sources: statistical prior information about epicardial potential distributions and sparse simultaneous measurements of epicardial potentials made with multielectrode coronary venous catheters. We employ a Bayesian methodology which offers a general way to incorporate these information sources and additionally provides statistical performance analysis tools. In a simulation study, we first compare solutions using one or more of these information sources. Then, we study the effects of varying the number of sparse epicardial potential measurements on reconstruction accuracy. To evaluate accuracy, we used the Bayesian error covariance as well as traditional error metrics such as relative error. Our results show that including even sparsely sampled information from coronary venous catheters can substantially improve the reconstruction of epicardial potential distributions and that a Bayesian framework provides a feasible approach to using this information. Moreover, computing the Bayesian error standard deviations offers a means to indicate confidence in the results even in the absence of validation data.

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