Research of fetal ECG extraction using wavelet analysis and adaptive filtering

Extracting clean fetal electrocardiogram (ECG) signals is very important in fetal monitoring. In this paper, we proposed a new method for fetal ECG extraction based on wavelet analysis, the least mean square (LMS) adaptive filtering algorithm, and the spatially selective noise filtration (SSNF) algorithm. First, abdominal signals and thoracic signals were processed by stationary wavelet transform (SWT), and the wavelet coefficients at each scale were obtained. For each scale, the detail coefficients were processed by the LMS algorithm. The coefficient of the abdominal signal was taken as the original input of the LMS adaptive filtering system, and the coefficient of the thoracic signal as the reference input. Then, correlations of the processed wavelet coefficients were computed. The threshold was set and noise components were removed with the SSNF algorithm. Finally, the processed wavelet coefficients were reconstructed by inverse SWT to obtain fetal ECG. Twenty cases of simulated data and 12 cases of clinical data were used. Experimental results showed that the proposed method outperforms the LMS algorithm: (1) it shows improvement in case of superposition R-peaks of fetal ECG and maternal ECG; (2) noise disturbance is eliminated by incorporating the SSNF algorithm and the extracted waveform is more stable; and (3) the performance is proven quantitatively by SNR calculation. The results indicated that the proposed algorithm can be used for extracting fetal ECG from abdominal signals.     

Longitudinal study of variation in body mass index in middle-aged UK females

The importance of changing patterns of obesity in society and its implications for public health are well recognized. However, the adult life course of body mass index (BMI) changes in individuals over time is largely unknown and has mostly been extrapolated from cross-sectional studies. The present study examines individual specific variation of BMI during a 15-year follow-up period in a community-based sample of UK females. We attempted to establish whether there is a common, generalized pattern which captures variation in BMI over time. The participants of this study belong to a prospective population cohort of British women studied intensively since 1989: the Chingford Study. The sample originally consisted of 1,003 women aged 45-68 years, who were assessed annually for BMI during follow-up period. Polynomial regression models were used to assess longitudinal BMI variation. We observed a great stability in individual BMI variation during the follow-up period, reflected by high correlations between the baseline BMI and follow-up BMI 10 and 15 years later (r = 0.876, N = 810, and r = 0.824, N = 638, respectively). We also found that three different major age-related patterns in BMI could be clearly identified: no change in 30.6% in 58% it increased and in 11.4% it decreased with age. Thus, our data suggest that individual age-related changes in BMI are very different. Therefore, simply combining all individuals into groups by any other criteria (age, sex, etc.) and overlooking the distinctive patterns of BMI change may lead to biased inferences in epidemiologic and etiologic research of the future.     

Epilepsy classification using optimized artificial neural network

ABSTRACT

Objectives: An Electroencephalogram (EEG) is the result of co-operative actions performed by brain cells. In other words, it can be defined as the time course of extracellular field potentials that are generated due to the synchronous action of cells. It is widely used for the analysis and diagnosis of several conditions. But this clinical data use to be multi-dimensional, context-dependent, complex, and it causes a concoction of various computing related research challenges. The objective of this study was to develop a computer-aided diagnosis system for epilepsy detection using EEG signals to ease the diagnosis process.

Materials: In this study, EEG datasets for epilepsy disease detection were taken from a public domain (Bonn University, Germany). These EEG recordings contain 100 single-channel EEG signals with maximum duration of 23.6 seconds. This data set was recorded intra-cranially and extra-cranially with the help of a 128-channel amplifier system using a common reference point.

Results: For a unique set of EEG signal features, the Optimized Artificial Neural Network model for classification and validation was developed with optimum neurons in the hidden layer. Results were tested on the basis of accuracy, sensitivity, precision, and specificity for all classes. The proposed Particle Swarm Optimized Artificial Neural Network provided 99.3% accuracy for EEG signal classification.

Discussion: Our results indicate that artificial neural network has efficiency to provide higher accuracy for epilepsy detection if the statistical features are extracted carefully. It is also possible to improve results for real time diagnosis by using optimization technique for error reduction.

Abbreviations: EEG: Electroencephalogram CAD: Computer-Aided Diagnosis ANN: Artificial Neural Network PSO: Particle Swarm Optimization FIR: Finite Impulse Response IIR: Infinite Impulse Response MSE: Mean Square Error.