Self-Attention LSTM-FCN model for arrhythmia classification and uncertainty assessment.

This paper presents ArrhyMon, a self-attention-based LSTM-FCN model for arrhythmia classification from ECG signal inputs. ArrhyMon targets to detect and classify six different types of arrhythmia apart from normal ECG patterns. To the best of our knowledge, ArrhyMon is the first end-to-end classification model that successfully targets the classification of six detailed arrhythmia types and compared to previous work does not require additional preprocessing and/or feature extraction operations separate from the classification model.

Deep reinforcement learning-based propofol infusion control for anesthesia: A feasibility study with a 3000-subject dataset.

In this work, we present a deep reinforcement learning-based approach as a baseline system for autonomous propofol infusion control. Specifically, design an environment for simulating the possible conditions of a target patient based on input demographic data and design our reinforcement learning model-based system so that it effectively makes predictions on the proper level of propofol infusion to maintain stable anesthesia even under dynamic conditions that can affect the decision-making process, such as the manual control of remifentanil by anesthesiologists and the varying patient conditions under anesthesia. Through an extensive set of evaluations using patient data from 3000 subjects, we show that the proposed method results in stabilization in the anesthesia state, by managing the bispectral index (BIS) and effect-site concentration for a patient showing varying conditions.

Predicting speech discrimination scores from pure-tone thresholds-A machine learning-based approach using data from 12,697 subjects.

Diagnostic tests for hearing impairment not only determines the presence (or absence) of hearing loss, but also evaluates its degree and type, and provides physicians with essential data for future treatment and rehabilitation. Therefore, accurately measuring hearing loss conditions is very important for proper patient understanding and treatment. In current-day practice, to quantify the level of hearing loss, physicians exploit specialized test scores such as the pure-tone audiometry (PTA) thresholds and speech discrimination scores (SDS) as quantitative metrics in examining a patient's auditory function.

Disentangling the clinical data chaos: User-centered interface system design for trauma centers.

This paper presents a year-long study of our project, aiming at (1) understanding the work practices of clinical staff in trauma intensive care units (TICUs) at a trauma center, with respect to their usage of clinical data interface systems, and (2) developing and evaluating an intuitive and user-centered clinical data interface system for their TICU environments. Based on a long-term field study in an urban trauma center that involved observation-, interview-, and survey-based studies to understand our target users and their working environment, we designed and implemented MediSenseView as a working prototype. MediSenseView is a clinical-data interface system, which was developed through the identification of three core challenges of existing interface system use in a trauma care unit-device separation, usage inefficiency, and system immobility-from the perspectives of three staff groups in our target environment (i.

Combining hepatic surface nodularity and serum tests better predicts hepatic fibrosis stages in chronic liver disease.

Purpose: Hepatic surface nodularity quantified on CT images has shown promising results in staging hepatic fibrosis in chronic hepatitis C. The aim of this study was to evaluate hepatic surface nodularity, serum fibrosis indices, and a linear combination of them for staging fibrosis in chronic liver disease, mainly chronic hepatitis B.

Methods: We developed a semiautomated software quantifying hepatic surface nodularity on CT images.

Kaleidoscopic fluorescent arrays for machine-learning-based point-of-care chemical sensing.

Multiplexed analysis allows simultaneous measurements of multiple targets, improving the detection sensitivity and accuracy. However, highly multiplexed analysis has been challenging for point-of-care (POC) sensing, which requires a simple, portable, robust, and affordable detection system. In this work, we developed paper-based POC sensing arrays consisting of kaleidoscopic fluorescent compounds.

Block-building performance test using a virtual reality head-mounted display in children with intermittent exotropia.

Purpose: To determine whether childhood intermittent exotropia (IXT) affects distance divergence and performance in block-building tasks within a virtual reality (VR) environment.

Methods: Thirty-nine children with IXT, aged 6-12 years, who underwent muscle surgery and 37 normal controls were enrolled. Children were instructed to watch the target moving away and perform a block-building task while fitted with a VR head-mounted display equipped with eye- and hand-movement tracking systems.

IB-MAC: Transmission Latency-Aware MAC for Electro-Magnetic Intra-Body Communications.

Intra-body Communication (IBC) is a communication method using the human body as a communication medium, in which body-attached devices exchange electro-magnetic (EM) wave signals with each other. The fact that our human body consists of water and electrolytes allows such communication methods to be possible. Such a communication technology can be used to design novel body area networks that are secure and resilient towards external radio interference.

Convolutional neural network-based classification system design with compressed wireless sensor network images.

With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools.

Constructing an Open-Access Bio-Signal Repository from Intensive Care Units.

Bio-signals can be crucial evidence in detecting urgent clinical events. However, until now, access to this data was limited. We aim to construct and provide a new open bio-signal repository with data gathered from more than 40 intensive care unit (ICU) beds.

System for Collecting Biosignal Data from Multiple Patient Monitoring Systems.

Objectives: Biosignal data include important physiological information. For that reason, many devices and systems have been developed, but there has not been enough consideration of how to collect and integrate raw data from multiple systems. To overcome this limitation, we have developed a system for collecting and integrating biosignal data from two patient monitoring systems.

Motion-oriented noisy physiological signal refining using embedded sensing platforms.

Recent improvements in data learning techniques have catalyzed the development of various clinical learning systems. However, for clinical applications, training from noisy data can cause significant misleading results, directly leading to potentially dangerous clinical decisions. Given its importance, this work targets to present a preliminary effort to identify corrupted vital sign data by analyzing the patient motions on hospital beds.

Wireless sensing systems in clinical environments: improving the efficiency of the patient monitoring process.

Multiple studies suggest that the level of patient care may decline in the future because of a larger aging population and medical staff shortages. Wireless sensing systems that automate some of the patient monitoring tasks can potentially improve the efficiency of patient workflows, but their efficacy in clinical settings is an open question. This article examines the potential of wireless sensor network (WSN) technologies to improve the efficiency of the patient-monitoring process in clinical environments.