Preliminary Laboratory Vibration Testing of a Complete Neonatal Patient Transport System.

Premature infants or neonates in need of advanced clinical care must be transported to specialized hospitals. Past studies have examined vibrations experienced by patients during transport; however, multiple confounding factors limit the utility of on-road data. Hence, the development of a standardized test environment is warranted. The overall purpose of this project is to characterize vibrations during neonatal patient transport and develop mitigation strategies to reduce exposure. This paper focusses on the development of a laboratory test environment and procedure that enables studying the equipment vibration in a comprehensive and repeatable manner. For the first time, a complete neonatal patient transport system, including a stretcher, has been mounted on an industrial shaker. Results largely validate the system's ability to simulate on-road vibrations with high repeatability.

Automated segmentation of villi in histopathology images of placenta.

PURPOSE: Manual analysis of clinical placenta pathology samples under the microscope is a costly and time-consuming task. Computer-aided diagnosis might offer a means to obtain fast and reliable results and also substantially reduce inter- and intra-rater variability. Here, we present a fully automated segmentation method that is capable of distinguishing the complex histological features of the human placenta (i.e., the chorionic villous structures). METHODS: The proposed pipeline consists of multiple steps to segment individual placental villi structures in hematoxylin and eosin (H&E) stained placental images. Artifacts and undesired objects in the histological field of view are detected and excluded from further analysis. One of the challenges in our new algorithm is the detection and segmentation of touching villi in our dataset. The proposed algorithm uses the top-hat transformation to detect candidate concavities in each structure, which might represent two distinct villous structures in close proximity. The detected concavities are classified by extracting multiple features from each candidate concavity. Our proposed pipeline is evaluated against manual segmentations, confirmed by an expert pathologist, on 12 scans from three healthy control patients and nine patients diagnosed with preeclampsia, containing nearly 5000 individual villi. The results of our method are compared to a previously published method for villi segmentation. RESULTS: Our algorithm detected placental villous structures with an F1 score of 80.76% and sensitivity of 82.18%. These values are substantially better than the previously published method, whose F1 score and sensitivity are 65.30% and 55.12%, respectively. CONCLUSION: Our method is capable of distinguishing the complex histological features of the human placenta (i.e., the chorionic villous structures), removing artifacts over a large histopathology sample of human placenta, and (importantly) account for touching adjacent villi structures. Compared to existing methods, our developed method yielded high accuracy in detecting villi in placental images.

CleanEMG--power line interference estimation in sEMG using an adaptive least squares algorithm.

This paper presents an adaptive least squares algorithm for estimating the power line interference in surface electromyography (sEMG) signals. The algorithm estimates the power line interference, without the need for a reference input. Power line interference can be removed by subtracting the estimate from the original sEMG signal. The algorithm is evaluated with simulated sEMG based on its ability to accurately estimate power line interference at different frequencies and at various signal-to-noise ratios. Power line estimates produced by the algorithm are accurate for signal-to-noise ratios below 15 dB (SNR estimation error at 15 dB is 14.7995 dB + 1.6547 dB).

A multi-modal approach for hand motion classification using surface EMG and accelerometers.

For decades, electromyography (EMG) has been used for diagnostics, upper-limb prosthesis control, and recently even for more general human-machine interfaces. Current commercial upper limb prostheses usually have only two electrode sites due to cost and space limitations, while researchers often experiment with multiple sites. Micro-machined inertial sensors are gaining popularity in many commercial and research applications where knowledge of the postures and movements of the body is desired. In the present study, we have investigated whether accelerometers, which are relatively cheap, small, robust to noise, and easily integrated in a prosthetic socket; can reduce the need for adding more electrode sites to the prosthesis control system. This was done by adding accelerometers to a multifunction system and also to a simplified system more similar to current commercially available prosthesis controllers, and assessing the resulting changes in classification accuracy. The accelerometer does not provide information on muscle force like EMG electrodes, but the results show that it provides useful supplementary information. Specifically, if one wants to improve a two-site EMG system, one should add an accelerometer affixed to the forearm rather than a third electrode.

Aspects of nonlinear dielectric spectroscopy of biological cell suspensions.

In this paper the technique of nonlinear dielectric spectroscopy is employed to examine the nonlinear response of a suspension of the yeast S. cerevisiae to a low frequency perturbating ac electric field. Metabolically active and resting yeast states, as well as the electrolyte medium are considered, and experimental time-course spectral data are presented. Conductivity is found to increase in the active case, resulting in variations in magnitude of the applied field. An empirical model is fitted to the experimental data at discrete points over time, enabling simulation and resulting in a software-based method to compensate for these variations in effective field strength.

Approximated mutual information training for speech recognition using myoelectric signals.

A new training algorithm called the approximated maximum mutual information (AMMI) is proposed to improve the accuracy of myoelectric speech recognition using hidden Markov models (HMMs). Previous studies have demonstrated that automatic speech recognition can be performed using myoelectric signals from articulatory muscles of the face. Classification of facial myoelectric signals can be performed using HMMs that are trained using the maximum likelihood (ML) algorithm; however, this algorithm maximizes the likelihood of the observations in the training sequence, which is not directly associated with optimal classification accuracy. The AMMI training algorithm attempts to maximize the mutual information, thereby training the HMMs to optimize their parameters for discrimination. Our results show that AMMI training consistently reduces the error rates compared to these by the ML training, increasing the accuracy by approximately 3% on average.

Optimized Gaussian mixture models for upper limb motion classification.

This paper introduces the use of Gaussian mixture models (GMM) for discriminating multiple classes of limb motions using continuous myoelectric signals (MES). The purpose of this work is to investigate an optimum configuration of a GMM-based limb motion classification scheme. For this effort, a complete experimental evaluation of the Gaussian mixture motion model is conducted on a 12-subject database. The experiments examine algorithmic issues of the GMM including the model order selection and variance limiting. The final classification performance of this GMM system has been compared with that of three other classifiers (a linear discriminant analysis (LDA), a linear perceptron neural network (LP) and a multilayer perceptron (MLP) neural network) . The Gaussian mixture motion model attains 96.3% classification accuracy using four channel MES for distinguishing six limb motions and is shown to outperform the other motion modeling techniques on an identical six limb motion task.

Continuous classifier training for myoelectrically controlled prostheses.

Myoelectrically controlled prostheses use pattern recognition systems to classify input motions. Typically, these systems are initially trained offline using a set of training data. Changing conditions cause an increase in signal variation, leading to higher error rates. For better adaptability, a continuously trained classifier was developed. Data with valid class decisions are used to retrain the classifier with the class decisions used as classification targets. In this implementation the classifier validates decisions by using a retraining buffer to locate consecutive, identical majority vote decisions. Retraining is performed by incorporating new valid feature vectors, selected from the retraining buffer, into the training set, while discarding older vectors. Using the continuously trained classifier, an average improvement of 2.57% was seen over the noncontinuously trained classifier.