Modelling bodyweight to avoid anthelmintic underdosing of goats in resources-limited settings.

Improved management of livestock in resource-limited settings can provide a means towards improved human nutrition and livelihoods. However, gastrointestinal nematodes (GIN) are a significant production-limiting factor. Anthelmintics play a role in GIN management; however, few anthelmintic classes are available in many low-middle-income countries. Utilising a limited range of classes may increase selection for anthelmintic resistance; therefore, strategies to reduce other selective pressures are of heightened importance. Avoiding anthelmintic underdosing is one such strategy, but it can be challenging without access to accurate bodyweight measurement. Many previous studies have used thoracic girth as a practical proxy for bodyweight in goats; however, they have rarely considered the potential impact of natural variation on therapeutic doses. Here, the relationship between bodyweight and thoracic girth was modelled using data from 820 goats from three Malawian biomes in two seasons, with the specific aim of avoiding underestimation of bodyweight. The internally cross-validated linear regression (∛Weight ~ 0.053 + 0.040*Girth, R2 = 0.92, rounded up to the nearest 5 kg) was validated against data from an additional 352 Malawian goats (1.4% of goats allocated an underdose and 10.2% allocated a dose > 200% of bodyweight). The equation was further externally validated using an historical dataset of 150 goats from Assam, India (2.7% of goats were allocated to an underdose and 24.8% allocated to a > 200% of bodyweight). These results suggest that a more globally generalisable approach may be feasible, provided the accuracy of the estimate is considered alongside the therapeutic index of the pharmaceutical.

Dependent component analysis for the magnetogastrographic detection of human electrical response activity.

The detection of the basic electric rhythm (BER), composed of a 3 cycles min(-1) oscillation, can be performed using SQUID magnetometers. However, the electric response activity (ERA), which is generated when the stomach is performing a mechanical activity, was detected mainly by invasive electrical measurements and only recently was one report published describing its detection by magnetic measurements. This study was performed with the aim of detecting the ERA noninvasively after a meal. MGG recordings were made with a 74-channel first-order gradiometer (Magnes II, biomagnetic technologies) housed in a shielded room. Seven nonsymptomatic volunteers were measured in the study. Initially a 10 min recording was performed with the subject in the fasted state. A 250 kcal meal was given to the subject without moving out of the magnetometers and two epochs of 10 min each were acquired. The signals were processed to remove cardiac interference by an algorithm based on a variation of independent component analysis (ICA), then autoregressive and wavelet analysis was performed. Preliminary results have shown that there is an increase in the signal power at higher frequencies around (0.6 Hz-1.3 Hz) usually associated with the basic electric rhythm. The center of the frequency band and its width varied from subject to subject, demonstrating the importance of pre-prandial acquisition as a control. Another interesting finding was an increase in power after about 5 min of meal ingestion. This period roughly agrees with the lag phase of gastric emptying, measured by scintigraphy and other techniques. We confirm that MGG can detect the electric response activity in normal volunteers. Further improvements in signal processing and standardization of signal acquisition are necessary to ascertain its possible use in clinical situations.

Independent component analysis and neural networks applied for classification of malignant, benign and normal tissue in digital mammography.

OBJECTIVES: This paper proposes an efficient method for the discrimination and classification of mammograms with benign, malignant and normal tissues. METHODS: The proposed method consists of selection of tissues, feature extraction using independent component analysis, feature selection by the forward-selection technique and classification of the tissue by the multilayer perceptron. RESULTS: The method is tested for a mammogram set of the MIAS database, resulting in a 97.83% success rate, with 98.0% specificity and 97.5% sensitivity. CONCLUSION: The proposed method showed a good classification rate. The method will be useful for early cancer diagnosis.

Estimation of speech embedded in a reverberant and noisy environment by independent component analysis and wavelets.

In this paper, we develop a system for enhancement of the speech signal with highest energy from a linear convolutive mixture of n statistically independent sound sources recorded by m microphones, where m

Heart instantaneous frequency (HIF): an alternative approach to extract heart rate variability.

Our study focuses on a new method of estimating the heart rate variability (HRV) which does not require the use of electrocardiogram (ECG) R-wave detection. Contrary to the R-wave detection method which requires a sampling frequency higher than 100 Hz, the one proposed here can be used to calculate the HRV from an ECG signal sampled at a frequency of approximately 5 Hz with a relative mean error of 0.03. This new method is based on extracting the instantaneous fundamental frequency from the ECG. The method could be efficiently used to extract the HRV from an ECG measured for healthy subjects performing an exercise in which the HRV increases linearly with time, and for subjects with respiratory and cardiac problems. The overall error decreased as we low-pass filtered the HRV with lower cut-off frequencies. Moreover, it was shown that the method could be efficiently used to calculate the HRV from blood pressure measurements and to be robust to noise.

Extraction of specific signals with temporal structure.

In this work we develop a very simple batch learning algorithm for semiblind extraction of a desired source signal with temporal structure from linear mixtures. Although we use the concept of sequential blind extraction of sources and independent component analysis, we do not carry out the extraction in a completely blind manner; neither do we assume that sources are statistically independent. In fact, we show that the a priori information about the autocorrelation function of primary sources can be used to extract the desired signals (sources of interest) from their linear mixtures. Extensive computer simulations and real data application experiments confirm the validity and high performance of the proposed algorithm.

Extraction of event-related signals from multichannel bioelectrical measurements.

Independent component analysis (ICA) is a powerful tool for separating signals from their mixtures. In this field, many algorithms were proposed, but they poorly use a priori information in order to find the desired signal. Here, we propose a fixed point algorithm which uses a priori information to find the signal of interest out of a number of sensors. We particularly applied the algorithm to cancel cardiac artifacts from a magnetoencephalogram.

MSE behavior of biomedical event-related filters.

The mean-squared error (MSE) behavior for Fourier linear combiner (FLC)-based filters is analyzed, using the independence assumption. The advantage of this analysis is its simplicity compared with previous results. The MSE transient behavior for this kind of filters is also presented for the first time. Moreover, a time-varying sequence for the least mean square (LMS) algorithm step-size is proposed to provide fast convergence with small misadjustment error. It is shown that for this sequence, the MSE behaves better as the input signal-to-noise ratio (SNR) decreases, but increases with the number of harmonics. Lastly, we make a brief analysis on the nonstationary behavior of these filters, and again we find simple expressions for the MSE behavior.

Filtering noncorrelated noise in impedance cardiography.

Impedance cardiography (ICG) may be altered by noises as respiration and movement artifacts, mainly during exercise. In this work, a scaled Fourier linear combiner (SFLC) event-related to the R-R interval of ECG is proposed. It estimates the deterministic component of the impedance cardiographic signal and removes the noises uncorrelated to this interval. The impedance cardiographic signal is modeled as Fourier series with the coefficients estimated by the least mean square (LMS) algorithm. Simulations have been carried out to evaluate the filter performance for different noise conditions. Moreover, the method capability to remove uncorrelated noises was also examined in physiological data obtained in rest and exercise, by synchronizing respiration and pedaling with a metronome. Analyzing the ICG power spectrum, it was concluded that the proposed filter could remove the noises that are not synchronized with heart rate.