Hierarchical deep learning for autonomous multi-label arrhythmia detection and classification on real-world wearable electrocardiogram data.

Objective: Arrhythmia detection and classification are challenging because of the imbalanced ratio of normal heartbeats to arrhythmia heartbeats and the complicated combinations of arrhythmia types. Arrhythmia classification on wearable electrocardiogram monitoring devices poses a further unique challenge: unlike clinically used electrocardiogram monitoring devices, the environments in which wearable devices are deployed are drastically different from the carefully controlled clinical environment, leading to significantly more noise, thus making arrhythmia classification more difficult.

Methods: We propose a novel hierarchical model based on CNN+BiLSTM with Attention to arrhythmia detection, consisting of a binary classification module between normal and arrhythmia heartbeats and a multi-label classification module for classifying arrhythmia events across combinations of beat and rhythm arrhythmia types.

GAIT-CKD (Gait Analysis using Artificial Intelligence for digital Therapeutics of patients with Chronic Kidney Disease): design and methods.

Background: Digital therapeutics are emerging as treatments for diseases and disabilities. In chronic kidney disease (CKD), gait is a potential biomarker for health status and intervention effectiveness. This study aims to analyze gait characteristics in CKD patients, providing baseline data for digital therapeutics development.

Exploring the Possibility of Photoplethysmography-Based Human Activity Recognition Using Convolutional Neural Networks.

Various sensing modalities, including external and internal sensors, have been employed in research on human activity recognition (HAR). Among these, internal sensors, particularly wearable technologies, hold significant promise due to their lightweight nature and simplicity. Recently, HAR techniques leveraging wearable biometric signals, such as electrocardiography (ECG) and photoplethysmography (PPG), have been proposed using publicly available datasets.

The effect of voluntary exercise on light cycle stress-induced metabolic resistance.

Purpose: Disruption of circadian genes affects metabolic homeostasis. Regular exercise programs prevent metabolic dysfunction and alter circadian gene expression In this study, we investigated whether exercise affects light stress-induced circadian rhythm derangement and metabolic resistance.

Methods: A circadian rhythm derangement mouse model was designed by extending the light exposure by two hours (14 L/10 D) for three weeks.

Distinct effect of exercise modes on mood-related behavior in mice.

Exercise can afford several benefits to combat mood disorders in both rodents and humans. Engagement in various physical activities upregulates levels of neurotrophic factors in several brain regions and improves mental health. However, the type of exercise that regulates mood and the underlying mechanisms in the brain remain elusive.

A Dual Level Analysis with Evolutionary Computing and Swarm Models for Classification of Leukemia.

One of the major reasons of mortality in human beings is cancer, and there is an absolute necessity for doctors to identify and treat a person suffering from it. Leukemia is a group of blood cancers that usually originates in the bone marrow and results in very high number of abnormal cells. For the diagnosis of cancer, microarray data serves as an important clinical application and serves as a great aid to the entire medical community.

A Holistic Strategy for Classification of Sleep Stages with EEG.

Manual sleep stage scoring is usually implemented with the help of sleep specialists by means of visual inspection of the neurophysiological signals of the patient. As it is a very hectic task to perform, automated sleep stage classification systems were developed in the past, and advancements are being made consistently by researchers. The various stages of sleep are identified by these automated sleep stage classification systems, and it is quite an important step to assist doctors for the diagnosis of sleep-related disorders.

iApp: An Autonomous Inspection, Auscultation, Percussion, and Palpation Platform.

Disease symptoms often contain features that are not routinely recognized by patients but can be identified through indirect inspection or diagnosis by medical professionals. Telemedicine requires sufficient information for aiding doctors' diagnosis, and it has been primarily achieved by clinical decision support systems (CDSSs) utilizing visual information. However, additional medical diagnostic tools are needed for improving CDSSs.

Physical Telerehabilitation Improves Quality of Life in Patients with Multiple Sclerosis.

The purpose of this study was to investigate the effect of physical telerehabilitation on the quality of life (QOL) in patients with multiple sclerosis (PwMS) in a randomized controlled trial. PwMS in both groups received home-based individualized exercise plan based on their physical therapy exam. PwMS in the intervention group were guided by a telerehabilitation system in following their exercise program on a daily basis whereas PwMS in the control group received periodic newsletters.

Usability of Remote Assessment of Exercise Capacity for Pulmonary Telerehabilitation Program.

Pulmonary rehabilitation [PR] has been successfully carried out via telemedicine however initial patient assessment has been traditionally conducted in PR centers. The first step in PR is assessment of patient's exercise capacity which allows individualized prescription of safe and effective exercise program. With COVID-19 pandemics assessment of patients in PR centers has been limited resulting in significant reduction of patients undergoing life-saving PR.

Association Between System Usage Pattern and Impact of Web-Based Telerehabilitation in Patients with Multiple Sclerosis.

Patients with multiple sclerosis (PwMS) increasingly use online services for managing their healthcare. The objective of this study was to investigate web log data (weblogs) generated by PwMS in the process of web-based telerehabilitation and correlate them with rehabilitation progress. The weblogs from 17 patients (female: 15, male 2; mean age: 60.

Towards a Highly Usable, Mobile Electronic Platform for Patient Recruitment and Consent Management.

This study seeks to assess usability and acceptance of E-Consent on mobile devices such as tablet computers for collecting universal biobank consents. Usability inspection occurred via cognitive walkthroughs and heuristics evaluations, supplemented by surveys to capture health literacy, patient engagement, and other metrics. 17 patients of varied ages, backgrounds, and occupations participated in the study.

Relationship Between Exercise Duration in Multimodal Telerehabilitation and Quality of Sleep in Patients with Multiple Sclerosis.

The purpose of this study was to investigate the effect of a telerehabilitation system on the quality of sleep in patients with multiple sclerosis (PwMS). Fifteen females and two males (60.1 ± 11.

Assessment of Patient Ambulation Profiles to Predict Hospital Readmission, Discharge Location, and Length of Stay in a Cardiac Surgery Progressive Care Unit.

Importance: Promoting patient mobility during hospitalization is associated with improved outcomes and reduced risk of hospitalization-associated functional decline. Therefore, accurate measurement of mobility with high-information content data may be key to improved risk prediction models, identification of at-risk patients, and the development of interventions to improve outcomes. Remote monitoring enables measurement of multiple ambulation metrics incorporating both distance and speed.

Implant Failure Prediction Using Discriminant Analysis.

Electronic dental records (EDR) provide access to a vast repository of clinical information which may be used for analyzing dental care delivery. The goal of this study was identification of determinants of implant survival and development of implant failure prediction model using large data set of intact and failed implant parameters extracted from EDR. A retrospective analysis of 19 variables reflecting patient, surgeon and dental treatment characteristics of 800 dental implants was performed using discriminant analysis to develop a predictive model identifying potential implant failure based on characteristics routinely available in a clinical care setting.

Two Distinct Types of Sweat Profile in Healthy Subjects While Exercising at Constant Power Output Measured by a Wearable Sweat Sensor.

Wearable sweat sensors have enabled real-time monitoring of sweat profiles (sweat concentration versus time) and could enable monitoring of electrolyte loss during exercise or for individuals working in extreme environments. To assess the feasibility of using a wearable sweat chloride sensor for real-time monitoring of individuals during exercise, we recorded and analyzed the sweat profiles of 50 healthy subjects while spinning at 75 Watts for 1 hour. The measured sweat chloride concentrations were in the range from 2.

Laparoendoscopic Single Site Surgery for the Treatment of Huge Ovarian Cysts Using an Angiocatheter Needle.

Purpose: The aim of this study was to evaluate the feasibility and safety of laparoendoscopic single site (LESS) surgery using an angiocatheter needle in patients with huge ovarian cysts (diameter ≥15 cm).

Materials And Methods: Thirty-one patients with huge ovarian cysts underwent LESS surgery using an angiocatheter needle between March 2011 and August 2016. An intra-umbilical vertical incision (1.

Mining Electronic Dental Records to Identify Dry Socket Risk Factors.

The goal of this study was to investigate risk factors for developing dry sockets in patients after dental extractions. Data were collected directly from electronic dental records (EDR) and were utilized for selecting dry socket cases and controls to conduct a nested case-control study. Case-control matching was based on sex, age range, maxilla-mandible location, and anterior-posterior location.

Using Big Data to Uncover Patient Determinants of Care Utilization Compliance in a Student Dental Clinic.

This study aims at assessing the relationship between social determinants of health (SDH) and dental care utilization compliance in a student dental clinic. Electronic dental records (EDR) were queried based on visit codes and evaluated using descriptive and inferential statistics, and binary logistic regression. Overall, characteristics of 16,474 visits were analyzed to identify potential predictors of appointment compliance.

Wearable Devices for Precision Medicine and Health State Monitoring.

Wearable technologies will play an important role in advancing precision medicine by enabling measurement of clinically-relevant parameters describing an individual's health state. The lifestyle and fitness markets have provided the driving force for the development of a broad range of wearable technologies that can be adapted for use in healthcare. Here we review existing technologies currently used for measurement of the four primary vital signs: temperature, heart rate, respiration rate, and blood pressure, along with physical activity, sweat, and emotion.

Factors Affecting Adherence with Telerehabilitation in Patients with Multiple Sclerosis.

The goal of this study was to identify predictors of telerehabilitation adherence in patients with multiple sclerosis (MS). An adherence prediction model was based on baseline patient characteristics. Such a model may be useful for identifying patients who require higher levels of engagements in the early stages of home telerehabilitation programs.

Distinguishing positions and movements in bed from load cell signals.

Objective: To characterize and classify six positions and movements for individuals in a bed using the output signals of four load cell sensors.

Approach: A bed equipped with four load cell sensors and synchronized video was used to assess the load cell response of 54 healthy individuals in prescribed positions and as they moved between positions. Stationary positions were characterized by the signals from the four load cells and the coordinates of the center of mass (CoM).

Sweat test for cystic fibrosis: Wearable sweat sensor vs. standard laboratory test.

Background: Sweat chloride testing for diagnosis of cystic fibrosis (CF) involves sweat induction, collection and handling, and measurement in an analytical lab. We have developed a wearable sensor with an integrated salt bridge for real-time measurement of sweat chloride concentration. Here, in a proof-of-concept study, we compare the performance of the sensor to current clinical practice in CF patients and healthy subjects.

Using a Real-Time Location System for Assessment of Patient Ambulation in a Hospital Setting.

Objective: To assess the feasibility of using an infrared-based Real-Time Location System (RTLS) for measuring patient ambulation in a 2-minute walk test (2MWT) by comparing the distance walked and the Johns Hopkins Highest Level of Mobility (JH-HLM) score to clinician observation as a criterion standard.

Design: Criterion standard validation study.

Setting: Inpatient, university hospital.

Machine learning approaches to personalize early prediction of asthma exacerbations.

Patient telemonitoring results in an aggregation of significant amounts of information about patient disease trajectory. However, the potential use of this information for early prediction of exacerbations in adult asthma patients has not been systematically evaluated. The aim of this study was to explore the utility of telemonitoring data for building machine learning algorithms that predict asthma exacerbations before they occur.