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A generalized Bayesian inference nets model (GBINM) is proposed to aid researchers to construct Bayesian inference nets for various applications. The benefit of such a model is well demonstrated by applying GBINM in constructing a hierarchical Bayesian fuzzy inference nets (HBFIN) to diagnose five important types of cardiovascular diseases (CVD). The patients' medical records with doctors' confirmed diagnostic results obtained from two hospitals in China are used to design and verify HBFIN. Bayesian theorem is used to calculate the propagation of probability and address the uncertainties involved in each sequential stage of inference nets to deduce the disease(s). The validity and effectiveness of proposed approach is witnessed clearly from testing results obtained.

The law of energy distribution of white noise (WN) in Haar wavelet decomposition levels (WDLs) of cardiovascular bio-signals including Phonocardiogram (PCG), Electrocardiogram (ECG), and Sphygmogram (SPG) is analyzed statistically. The analytical results indicate that for WN signals, energy proportion of the 2nd WDL to the 1st WDL is 0.5 approximately, which is verified by Monte Carlo test. For PCG, ECG and SPG signals, when sampling frequency is 4 kHz for PCG, 400 Hz for ECG, and 160 Hz for SPG, energies of the 1st WDL and 2nd WDLs are almost the same; this is validated by theoretical analysis and practical observation on collected datasets. Based on the wavelet energy distribution laws of WN and cardiovascular bio-signals, equations are created to estimate energy of WN, energy of bio-signals, and SNR. Experimental results show that the proposed SNR estimation method for cardiovascular bio-signals mixed with WN has the normalized mean square error of less than 0.1 in all cases considered. [ABSTRACT FROM AUTHOR]

This paper presents a novel electrocardiogram (ECG) compression method for e-health applications by adapting an adaptive Fourier decomposition (AFD) algorithm hybridized with a symbol substitution (SS) technique. The compression consists of two stages: first stage AFD executes efficient lossy compression with high fidelity; second stage SS performs lossless compression enhancement and built-in data encryption, which is pivotal for e-health. Validated with 48 ECG records from MIT-BIH arrhythmia benchmark database, the proposed method achieves averaged compression ratio (CR) of 17.6–44.5 and percentage root mean square difference (PRD) of 0.8–2.0% with a highly linear and robust PRD-CR relationship, pushing forward the compression performance to an unexploited region. As such, this paper provides an attractive candidate of ECG compression method for pervasive e-health applications. [ABSTRACT FROM PUBLISHER]

A soft-computing method attenuating noise from heart sound (HS) signal for wearable e-healthcare device is proposed. The HS signal is decomposed by third-level wavelet packet transform (WPT). An automatic HS cycle detection algorithm is applied to find HS cycles in the (3, 0) leaf signal of WPT tree. Based on the quasi-cyclic feature of HS, short-time Fourier transform is implemented for cycles of each WPT tree leaf signal to decompose each cycle into time-frequency fragments which are called particles. Furthermore, the novel cuboid method is proposed to identify constituents of HS and noise from such generated particles. The particles representing HS are then retained and merged into noise-quasi-free WPT tree leaf signals. Eventually the inverse WPT (IWPT) is employed to build the noise-quasi-free HS signal. The method is assessed using mean square error (MSE) and compared with wavelet multi-threshold method (WMTM) and Tang's method. The experimental results show that the proposed method not only filters HS signal effectively but also well retains its pathological information. [ABSTRACT FROM AUTHOR]

A novel swarm intelligence approach for combinatorial optimization is proposed, which we call probability increment based swarm optimization (PIBSO). The population evolution mechanism of PIBSO is depicted. Each state in search space has a probability to be chosen. The rule of increasing the probabilities of states is established. Incremental factor is proposed to update probability of a state, and its value is determined by the fitness of the state. It lets the states with better fitness have higher probabilities. Usual roulette wheel selection is employed to select states. Population evolution is impelled by roulette wheel selection and state probability updating. The most distinctive feature of PIBSO is because roulette wheel selection and probability updating produce a trade-off between global and local search; when PIBSO is applied to solve the printed circuit board assembly optimization problem (PCBAOP), it performs superiorly over existing genetic algorithm and adaptive particle swarm optimization on length of tour and CPU running time, respectively. The reason for having such advantages is analyzed in detail. The success of PCBAOP application verifies the effectiveness and efficiency of PIBSO and shows that it is a good method for combinatorial optimization in engineering. [ABSTRACT FROM PUBLISHER]

In new advocated “smart grid” development, an electric load forecaster should possess high-level intelligence in order to handle higher uncertainty, indefiniteness, and variability on electric load demand. The intelligence is referred to as self-learning, self-adaptability, and the highest capability of handling various uncertainties, which the forecaster should possess. In this study, a novel methodology, self-developing and self-adaptive fuzzy neural networks using type-2 fuzzy Bayesian Ying-Yang learning algorithm (SDSA-FNN-T2BYYL) is proposed. Its novelty is that (1) the Bayesian Ying-Yang learning algorithm (BYYL) is used to construct a compact system structure automatically. (2) Further, a novel T2 fuzzy BYYL is presented, which integrates type-2 (T2) fuzzy theory and BYYL in order to achieve two objectives simultaneously: compact system structure and better handling of data uncertainty. (3) Because a training dataset cannot include all possible operation conditions, the system should be able to restructure continuously for good generalization. Consequently, a T2 fuzzy BYY split-and-merge algorithm is proposed. The proposed method is validated using a real operational dataset collected from a Macao electric utility. Simulation and test results reveal that SDSA-FNN-T2BYYL has superior accuracy for load forecasting over other existing relevant techniques. [ABSTRACT FROM AUTHOR]