We studied the feasibility of increasing the possible number of BCI commands that can be produced through our EEG-fTCD BCI by solving the 3-class problems of both the MI and flickering MR/WG paradigms.

High-resolution cervical auscultation (HRCA) is an evolving clinical method for noninvasive screening of dysphagia that relies on data science, machine learning, and wearable sensors to investigate the characteristics of disordered swallowing function in people with dysphagia. HRCA has shown promising results in categorizing normal and disordered swallowing (i.e., screening) independent of human input, identifying a variety of swallowing physiological events as accurately as trained human judges. The system has been developed through a collaboration of data scientists, computer-electrical engineers, and speech-language pathologists. Its potential to automate dysphagia screening and contribute to evaluation lies in its noninvasive nature (wearable electronic sensors) and its growing ability to accurately replicate human judgments of swallowing data typically formed on the basis of videofluoroscopic imaging data. Potential contributions of HRCA when videofluoroscopic swallowing study may be unavailable, undesired, or not feasible for many patients in various settings are discussed, along with the development and capabilities of HRCA. The use of technological advances and wearable devices can extend the dysphagia clinician's reach and reinforce top-of-license practice for patients with swallowing disorders.

OBJECTIVE Sensing the effects of alcohol consumption in real time could offer numerous opportunities to reduce related harms. This study sought to explore accuracy of gait-related features measured by smartphone accelerometer sensors on detecting alcohol intoxication (breath alcohol concentration [BrAC] > .08%). METHOD In a controlled laboratory study, participants (N = 17; 12 male) were asked to walk 10 steps in a straight line, turn, and walk 10 steps back before drinking and each hour, for up to 7 hours after drinking a weight-based dose of alcohol to reach a BrAC of .20%. Smartphones were placed on the lumbar region and 3-axis accelerometer data was recorded at a rate of 100 Hz. Accelerometer data were segmented into task segments (i.e., walk forward, walk backward). Features were generated for each overlapping 1-second windows, and the data set was split into training and testing data sets. Logistic regression models were used to estimate accuracy for classifying BrAC ≤ .08% from BrAC > .08% for each subject. RESULTS Across participants, BrAC > .08% was predicted with a mean accuracy of 92.5% using logistic regression, an improvement from a naive model accuracy of 88.2% (mean sensitivity = .89; specificity = .92; positive predictive value = .77; and negative predictive value = .97). The two most informative accelerometer features were mean signal amplitude and variance of the signal in the x-axis (i.e., gait sway). CONCLUSIONS We found preliminary evidence supporting use of gait-related features measured by smartphone accelerometer sensors to detect alcohol intoxication. Future research should determine whether these findings replicate in situ.

Tracking a liquid or food bolus in videofluoroscopic images during X-ray based diagnostic swallowing examinations is a dominant clinical approach to assess human swallowing function during oral, pharyngeal and esophageal stages of swallowing. This tracking represents a highly challenging problem for clinicians as swallowing is a rapid action. Therefore, we developed a computer-aided method to automate bolus detection and tracking in order to alleviate issues associated with human factors. Specifically, we applied a stateof-the-art deep learning model called Mask-RCNN to detect and segment the bolus in videofluoroscopic image sequences. We trained the algorithm with 450 swallow videos and evaluated with an independent dataset of 50 videos. The algorithm was able to detect and segment the bolus with a mean average precision of 0.49 and an intersection of union of 0.71. The proposed method indicated robust detection results that can help to improve the speed and accuracy of a clinical decisionmaking process.

Upper esophageal sphincter is an important anatomical landmark of the swallowing process commonly observed through the kinematic analysis of radiographic examinations that are vulnerable to subjectivity and clinical feasibility issues. Acting as the doorway of esophagus, upper esophageal sphincter allows the transition of ingested materials from pharyngeal into esophageal stages of swallowing and a reduced duration of opening can lead to penetration/aspiration and/or pharyngeal residue. Therefore, in this study we consider a non-invasive high resolution cervical auscultation-based screening tool to approximate the human ratings of upper esophageal sphincter opening and closure. Swallows were collected from 116 patients and a deep neural network was trained to produce a mask that demarcates the duration of upper esophageal sphincter opening. The proposed method achieved more than 90% accuracy and similar values of sensitivity and specificity when compared to human ratings even when tested over swallows from an independent clinical experiment. Moreover, the predicted opening and closure moments surprisingly fell within an inter-human comparable error of their human rated counterparts which demonstrates the clinical significance of high resolution cervical auscultation in replacing ionizing radiation-based evaluation of swallowing kinematics.

Emergency department (ED) nurses need to identify patients with potential acute coronary syndrome (ACS) rapidly because treatment delay could impact patient outcomes. Aims of this secondary analysis were to identify key patient factors that could be available at initial ED nurse triage that predict ACS. Consecutive patients with chest pain who called 9-1-1, received a 12-lead electrocardiogram in the prehospital setting, and were transported via emergency medical service were included in the study. A total of 750 patients were recruited. The sample had an average age of 59 years old, was 57% male, and 40% Black. One hundred and fifteen patients were diagnosed with ACS. Older age, non-Caucasian race, and faster respiratory rate were independent predictors of ACS. There was an interaction between heart rate by Type II diabetes receiving insulin in the context of ACS. Type II diabetics requiring insulin for better glycemic control manifested a faster heart rate. By identifying patient factors at ED nurse triage that could be predictive of ACS, accuracy rates of triage may improve, thus impacting patient outcomes.

BACKGROUND The Emergency Severity Index (ESI) is a widely used tool to triage patients in emergency departments. The ESI tool is used to assess all complaints and has significant limitation for accurately triaging patients with suspected acute coronary syndrome (ACS). OBJECTIVE We evaluated the accuracy of ESI in predicting serious outcomes in suspected ACS and aimed to assess the incremental reclassification performance if ESI is supplemented with a clinically validated tool used to risk-stratify suspected ACS. METHODS We used existing data from an observational cohort study of patients with chest pain. We extracted ESI scores documented by triage nurses during routine medical care. Two independent reviewers adjudicated the primary outcome, incidence of 30-day major adverse cardiac events. We compared ESI with the well-established modified HEAR/T (patient History, Electrocardiogram, Age, Risk factors, but without Troponin) score. RESULTS Our sample included 750 patients (age, 59 ± 17 years; 43% female; 40% black). A total of 145 patients (19%) experienced major adverse cardiac event. The area under the receiver operating characteristic curve for ESI score for predicting major adverse cardiac event was 0.656, compared with 0.796 for the modified HEAR/T score. Using the modified HEAR/T score, 181 of the 391 false positives (46%) and 16 of the 19 false negatives (84%) assigned by ESI could be reclassified correctly. CONCLUSION The ESI score is poorly associated with serious outcomes in patients with suspected ACS. Supplementing the ESI tool with input from other validated clinical tools can greatly improve the accuracy of triage in patients with suspected ACS.