Inertial Measurements for Tongue Motion Tracking Based on Magnetic Localization with Orientation Compensation.

Permanent magnet localization (PML) is designed for applications requiring non-line-of-sight motion tracking with millimetric accuracy. Current PML-based tongue tracking is not only impractical for daily use due to many sensors being placed around the mouth, but also requires a large training set of tracer motion. Our method was designed to overcome these shortcomings by generating a local magnetic field and removing the need for the localization to be trained with tracer rotations. An inertial measurement unit (IMU) is used as a tracer that moves in a local magnetic field generated by a magnet strip. The magnetic strength can be optimized to enable the strip to be placed further away from the tracer, thus hidden from view. The tracer is small (6×6×0.8 mm3) to reduce hindrance to natural tongue movements, and the strip is designed to be worn as a neckband. The IMU's magnetometer measures the local magnetic field which is compensated for the tracer's orientation by using the IMU's accelerometer and gyroscope. The orientation-compensated magnetic measurements are then fed into a localization algorithm that estimates the tracer's 3D position. The objective of this study is to evaluate the tracking accuracy of our method. In a 8×8×5 cm3 volume, positional errors of 1.6 mm (median) and 2.4 mm (third quartile, Q3) were achieved on a tracer being rotated ±50° along both pitch and roll. These results indicate this technology is promising for tongue tracking applications.

Sparse Coding Using the Locally Competitive Algorithm on the TrueNorth Neurosynaptic System.

The Locally Competitive Algorithm (LCA) is a biologically plausible computational architecture for sparse coding, where a signal is represented as a linear combination of elements from an over-complete dictionary. In this paper we map the LCA algorithm on the brain-inspired, IBM TrueNorth Neurosynaptic System. We discuss data structures and representation as well as the architecture of functional processing units that perform non-linear threshold, vector-matrix multiplication. We also present the design of the micro-architectural units that facilitate the implementation of dynamical based iterative algorithms. Experimental results with the LCA algorithm using the limited precision, fixed-point arithmetic on TrueNorth compare favorably with results using floating-point computations on a general purpose computer. The scaling of the LCA algorithm within the constraints of the TrueNorth is also discussed.

AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning.

OBJECTIVE: The objective of this paper is to provide an algorithm for accurate, automated detection of atrial fibrillation (AF) from ECG signals. Four types of ECG signals are considered: normal signals, signals representing symptoms of AF, other signals, and noisy signals. This paper represents follow-up work to the authors' entry in the 2017 PhysioNet Challenge as reported in the 2017 Computing in Cardiology Conference. APPROACH: Our approach involves extracting features from the ECG waveform and training a machine learning classifier. In feature extraction, we calculate several statistical features related to the ECG signal and fiduciary points. We also used a disciplined method of feature selection to reduce the dimensionality of the feature space. We also employ sparse coding as an unsupervised feature extraction tool. The classifier we use is a decision tree-based ensemble learning classifier. MAIN RESULTS: When applied to the hidden test data reserved by the PhysioNet Challenge organizers, our classifier reports F1 scores of 0.91, 0.78, and 0.71 for the Normal, AF, and Other classes, respectively. The overall test score is 0.80, and is obtained by averaging the F1 scores for these three classes. SIGNIFICANCE: This work demonstrates that feature selection and ensemble learning can be used to improve the performance of ECG-based classification of AF.

Combining sparse coding and time-domain features for heart sound classification.

OBJECTIVE: This paper builds upon work submitted as part of the 2016 PhysioNet/CinC Challenge, which used sparse coding as a feature extraction tool on audio PCG data for heart sound classification. APPROACH: In sparse coding, preprocessed data is decomposed into a dictionary matrix and a sparse coefficient matrix. The dictionary matrix represents statistically important features of the audio segments. The sparse coefficient matrix is a mapping that represents which features are used by each segment. Working in the sparse domain, we train support vector machines (SVMs) for each audio segment (S1, systole, S2, diastole) and the full cardiac cycle. We train a sixth SVM to combine the results from the preliminary SVMs into a single binary label for the entire PCG recording. In addition to classifying heart sounds using sparse coding, this paper presents two novel modifications. The first uses a matrix norm in the dictionary update step of sparse coding to encourage the dictionary to learn discriminating features from the abnormal heart recordings. The second combines the sparse coding features with time-domain features in the final SVM stage. MAIN RESULTS: The original algorithm submitted to the challenge achieved a cross-validated mean accuracy (MAcc) score of 0.8652 (Se = 0.8669 and Sp = 0.8634). After incorporating the modifications new to this paper, we report an improved cross-validated MAcc of 0.8926 (Se = 0.9007 and Sp = 0.8845). SIGNIFICANCE: Our results show that sparse coding is an effective way to define spectral features of the cardiac cycle and its sub-cycles for the purpose of classification. In addition, we demonstrate that sparse coding can be combined with additional feature extraction methods to improve classification accuracy.

Traumatic Hip Dislocation.

In brief Traumatic posterior hip dislocation is a rare but potentially disabling injury that is most often associated with severe trauma. Prompt recognition and early reduction are essential to a good outcome. An x-ray rules out fracture of the femoral head or acetabulum and should be routinely performed before reduction of the dislocation under anesthesia.