Noise/spike detection in phonocardiogram signal as a cyclic random process with non-stationary period interval.

The major aim of this study is to describe a unified procedure for detecting noisy segments and spikes in transduced signals with a cyclic but non-stationary periodic nature. According to this procedure, the cycles of the signal (onset and offset locations) are detected. Then, the cycles are clustered into a finite number of groups based on appropriate geometrical- and frequency-based time series. Next, the median template of each time series of each cluster is calculated. Afterwards, a correlation-based technique is devised for making a comparison between a test cycle feature and the associated time series of each cluster. Finally, by applying a suitably chosen threshold for the calculated correlation values, a segment is prescribed to be either clean or noisy. As a key merit of this research, the procedure can introduce a decision support for choosing accurately orthogonal-expansion-based filtering or to remove noisy segments. In this paper, the application procedure of the proposed method is comprehensively described by applying it to phonocardiogram (PCG) signals for finding noisy cycles. The database consists of 126 records from several patients of a domestic research station acquired by a 3M Littmann(®) 3200, 4KHz sampling frequency electronic stethoscope. By implementing the noisy segments detection algorithm with this database, a sensitivity of Se=91.41% and a positive predictive value, PPV=92.86% were obtained based on physicians assessments.

An expert electrocardiogram quality evaluation algorithm based on signal mobility factors.

The major concentration of this study is to describe and to develop a new electrocardiogram (ECG) signal measurement binary quality assessment (accept-reject) technique. The proposed algorithm is composed of three major stages: pre-processing, signal mobility-based quality measurement and advanced post-evaluation. The pre-processing step includes baseline wander and high-frequency disturbances removal. The signal mobility-based quality measurement routine includes two separate stages based on energy and concavity of the ECG signal. The post-evaluation quality measurement step is mainly based on the six features inferenced from heuristic experiences and human thinking models. The proposed technique was applied to the test dataset provided by the PhysioNet Computing in Cardiology (CinC) challenge 2011 and accuracy 93.40% was achieved which shows the marginal improvement in this field.

A unified procedure for detecting, quantifying, and validating electrocardiogram T-wave alternans.

The precision of T-wave alternans (TWA) quantification depends certainly upon the way we choose to align T-waves and to get feedbacks from the electrocardiogram (ECG) quality. Quantifying the ECG TWA based on assigning automatically the required number of T-waves along with applying a proper T-wave alignment approach is the purpose of this paper. The structure of the proposed method mainly consists of seven sections: preprocessing, ECG events detection-delineation, alignment of cycles, T-wave template extraction, T-wave delineation, T-wave left- and right-lobes synchronization, and T-wave alternans quantification-detection by getting feedback from ECG quality value. The proposed method is examined in two ways. First, some artificially generated ECGs with predefined TWA patterns and qualities are analyzed in order to regulate the parameters of the method to achieve the maximum performance. Finally, the method is applied to PhysioNet/Computing in Cardiology Challenge 2008 database. In this stage, the achieved accuracy is about 91.0 %, which shows marginal improvement in the area of TWA quantification.