A wearable silent speech interface (SSI) is a promising platform that enables verbal communication without vocalization. The most widely studied methodology for SSI focuses on surface electromyography (sEMG). However, sEMG suffers from low scalability because of signal quality-related issues, including signal-to-noise ratio and interelectrode interference. Hence, here, we present a novel SSI by utilizing crystalline-silicon-based strain sensors combined with a 3D convolutional deep learning algorithm. Two perpendicularly placed strain gauges with minimized cell dimension (<0.1 mm2) could effectively capture the biaxial strain information with high reliability. We attached four strain sensors near the subject’s mouths and collected strain data of unprecedently large wordsets (100 words), which our SSI can classify at a high accuracy rate (87.53%). Several analysis methods were demonstrated to verify the system’s reliability, as well as the performance comparison with another SSI using sEMG electrodes with the same dimension, which exhibited a relatively low accuracy rate (42.60%).
Exposure to opioids during pregnancy can lead to adverse infant outcomes, including neonatal abstinence syndrome (1) and birth defects (2). Ascertaining opioid prescriptions for women who become pregnant or have no indication of contraceptive use is important to determine the number of women who are at potential risk for adverse fetal outcomes. The New York State (NYS) Department of Health (DOH) analyzed data for women aged 15-44 years (i.e., reproductive-aged women) enrolled in Medicaid to examine opioid drug prescriptions during [2008][2009][2010][2011][2012][2013]. On the basis of Medicaid drug claims for any drug with an opioid ingredient, prescriptions were identified for the enrolled population of reproductive-aged women and for three subgroups: women whose diagnosis, procedure, and drug codes indicated contraceptive use or infertility; women who were not using contraceptives and not infertile; and women who had had a live birth during the reporting year. During 2008-2013, among all women of reproductive age, 20.0% received a prescription for a drug with an opioid component; the proportion was highest (27.3%) among women with an indication of contraceptive use or infertility, intermediate (17.3%) among women who had no indication of contraceptive use, and lowest (9.5%) among women who had had a live birth. Although New York's proportion of opioid prescriptions among female Medicaid recipients who had a live birth is lower than a recent U.S. estimate (3), these results suggest nearly one in 10 women in this group may have been exposed to opioids in the prenatal period.To understand patterns of prescribing opioid medications for women of reproductive age, NYS DOH examined Medicaid fee-for-service and managed care data during 2008-2013 for females aged 15-44 years who were continuously enrolled in Medicaid during each reporting year. NYS DOH used a list of medications derived from the NYS Medicaid formulary with First Data Bank hierarchical ingredient codes indicating opioids, and defined opioid prescription as any outpatient claim for a drug that contained an opioid ingredient for any woman during each reporting year (4). Live births were identified based on an International Classification of Diseases, Ninth Revision (ICD-9) primary diagnosis code indicating live birth (641.01-676.64, V27) and a principal procedure code indicating live birth (vaginal and cesarean delivery Current Procedural Terminology codes 59400, 59409, 59410, 59510, 59514, 59515, 59610, 59612, 59614, 59620, and 59622; ICD-9 procedure codes 73.51, 73.59, 74.0, 74.1, and 72.0-72.7) within 2 days of the diagnosis code.To determine the prenatal period, Medicaid records for a 1-year cohort of women were matched with vital statistics birth records. Among enrolled women who had a live birth, the mean gestational age in days for each pregnancy-related ICD-9 primary diagnosis code was calculated and used to compute the average prenatal period. Using this approach, the prenatal period was defined as the 280 days preceding the date of a live birth ...
By automatically classifying the stomach, small bowel, and colon, the reading time of the wireless capsule endoscopy (WCE) can be reduced. In addition, it is an essential first preprocessing step to localize the small bowel in order to apply automated small bowel lesion detection algorithms based on deep learning. The purpose of the study was to develop an automated small bowel detection method from long untrimmed videos captured from WCE. Through this, the stomach and colon can also be distinguished. The proposed method is based on a convolutional neural network (CNN) with a temporal filtering on the predicted probabilities from the CNN. For CNN, we use a ResNet50 model to classify three organs including stomach, small bowel, and colon. The hybrid temporal filter consisting of a Savitzky–Golay filter and a median filter is applied to the temporal probabilities for the “small bowel” class. After filtering, the small bowel and the other two organs are differentiated with thresholding. The study was conducted on dataset of 200 patients (100 normal and 100 abnormal WCE cases), which was divided into a training set of 140 cases, a validation set of 20 cases, and a test set of 40 cases. For the test set of 40 patients (20 normal and 20 abnormal WCE cases), the proposed method showed accuracy of 99.8% in binary classification for the small bowel. Transition time errors for gastrointestinal tracts were only 38.8 ± 25.8 seconds for the transition between stomach and small bowel and 32.0 ± 19.1 seconds for the transition between small bowel and colon, compared to the ground truth organ transition points marked by two experienced gastroenterologists.
A wearable silent speech interface (SSI) is a promising platform that enables verbal communication without vocalization. The most widely studied methodology for SSI focuses on surface electromyography (sEMG). However, sEMG suffers from low scalability because of signal quality-related issues, including signal-to-noise ratio and interelectrode interference. Hence, in this study, we present a novel SSI by utilizing crystalline-silicon-based strain sensors combined with a 3D convolutional deep learning algorithm. Two perpendicularly placed strain gauges with minimized cell dimension (< 0.1 mm2) could capture the biaxial strain information with high reliability. We attached four strain sensors near the subject’s mouths and collected strain data of unprecedently large wordsets (100 words), which our SSI can classify at a high accuracy rate (87.53%). Several analysis methods were demonstrated to verify the system’s reliability, as well as the performance comparison with another SSI using sEMG electrodes with the same dimension, which exhibited an accuracy rate of 35.00%.
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