Reflexive eye saccadic parameters in Parkinson's disease.

Introduction: Abnormal reactive saccade, with reduced saccadic gain, impaired smooth pursuit, and unwarranted reactions are clinically used to assess people with Parkinson's disease (PwPD). However, there are inconsistent findings related to other saccade parameters such as latency and transition times. This study aimed to identify differences in the reflexive saccade parameters of early stage PwPD and aged-matched control (AMC).

Screening major depressive disorder in patients with obstructive sleep apnea using single-lead ECG recording during sleep.

A large number of people with obstructive sleep apnea (OSA) also suffer from major depressive disorder (MDD), leading to underdiagnosis due to overlapping symptoms. Polysomnography has been considered to identify MDD. However, limited access to sleep clinics makes this challenging.

Enhanced Domain Adaptation for Foot Ulcer Segmentation Through Mixing Self-Trained Weak Labels.

Wound management requires the measurement of the wound parameters such as its shape and area. However, computerized analysis of the wound suffers the challenge of inexact segmentation of the wound images due to limited or inaccurate labels. It is a common scenario that the source domain provides an abundance of labeled data, while the target domain provides only limited labels.

NeuroDiag: Software for Automated Diagnosis of Parkinson's Disease Using Handwriting.

Objective: A change in handwriting is an early sign of Parkinson's disease (PD). However, significant inter-person differences in handwriting make it difficult to identify pathological handwriting, especially in the early stages. This paper reports the testing of NeuroDiag, a software-based medical device, for the automated detection of PD using handwriting patterns.

Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor.

Background: Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems.

Method: We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on the Fahn-Tolosa-Marin tremor rating scale (FTM) and determine the classification accuracy within the tremor type.

Monitoring the Effect of Levodopa Using Sustained Phonemes in Parkinson's Disease Patients.

Parkinson's disease (PD) is a neurological disease identified by multiple symptoms, and levodopa is one of the most effective medications for treating the disease. To determine the dosage of levodopa, it is necessary to meet on a regular basis and observe motor function. The early detection and progression of the disease have been proposed using hypokinetic dysarthria.

A translational study for detection of cardiac autonomic neuropathy using fractal features: A bench to bedside approach.

Cardiac autonomic Neuropathy (CAN) is an acute complication of Diabetes mellitus (DM) that does not exhibit overt symptoms in the subclinical stage. Researchers have developed several techniques that have proved to give higher efficiency in classification using software tools. The challenge in implementing the same using hardware for diagnosis fails when classification boundaries are mismatched, as there are more chances of misinterpreting the classes.

Application of StyleGAN Architecture for Generating Venous Leg Ulcer Images.

Leg ulcers caused by impaired venous blood return are the most typical chronic wound form and have a significant negative impact on the lives of people living with these wounds. Thus, it is important to provide early assessment and appropriate treatment of the wounds to promote their healing in the normal trajectory. Gathering quality wound data is an important component of good clinical care, enabling monitoring of healing progress.

The Change of Vocal Tract Length in People with Parkinson's Disease.

Hypokinetic dysarthria is one of the early symptoms of Parkinson's disease (PD) and has been proposed for early detection and also for monitoring of the progression of the disease. PD reduces the control of vocal tract muscles such as the tongue and lips and, therefore the length of the active vocal tract is altered. However, the change in the vocal tract length due to the disease has not been investigated.

Human Factors in Sensory Feedback for Haptic Gloves.

Manual therapy training requires close proximity between the clinical teacher and students, which limits the training of people in remote and rural regions. Video-based online training can provide visual but not tactile information, which is also essential for manual therapies. This project describes the development and testing of an inexpensive sensor glove developed using commercially available sensors, suitable for monitoring the shape and force applied by the hand of a person delivering a spinal manipulation.

On the inter-dataset generalization of machine learning approaches to Parkinson's disease detection from voice.

Background And Objective: Parkinson's disease is the second-most-common neurodegenerative disorder that affects motor skills, cognitive processes, mood, and everyday tasks such as speaking and walking. The voices of people with Parkinson's disease may become weak, breathy, or hoarse and may sound emotionless, with slurred words and mumbling. Algorithms for computerized voice analysis have been proposed and have shown highly accurate results.

Voice Features of Sustained Phoneme as COVID-19 Biomarker.

Background: The COVID-19 pandemic has resulted in enormous costs to our society. Besides finding medicines to treat those infected by the virus, it is important to find effective and efficient strategies to prevent the spreading of the disease. One key factor to prevent transmission is to identify COVID-19 biomarkers that can be used to develop an efficient, accurate, noninvasive, and self-administered screening procedure.

Computerised prediction of healing for venous leg ulcers.

Early prediction of delayed healing for venous leg ulcers could improve management outcomes by enabling earlier initiation of adjuvant therapies. In this paper, we propose a framework for computerised prediction of healing for venous leg ulcers assessed in home settings using thermal images of the 0 week. Wound data of 56 older participants over 12 weeks were used for the study.

Phonemes based detection of parkinson's disease for telehealth applications.

Dysarthria is an early symptom of Parkinson's disease (PD) which has been proposed for detection and monitoring of the disease with potential for telehealth. However, with inherent differences between voices of different people, computerized analysis have not demonstrated high performance that is consistent for different datasets. The aim of this study was to improve the performance in detecting PD voices and test this with different datasets.

Wearable sensors during drawing tasks to measure the severity of essential tremor.

Commonly used methods to assess the severity of essential tremor (ET) are based on clinical observation and lack objectivity. This study proposes the use of wearable accelerometer sensors for the quantitative assessment of ET. Acceleration data was recorded by inertial measurement unit (IMU) sensors during sketching of Archimedes spirals in 17 ET participants and 18 healthy controls.

Computerised analysis of writing and drawing by essential tremor phenotype.

We investigated whether computerised analysis of writing and drawing could discriminate essential tremor (ET) phenotypes according to the 2018 Consensus Statement on the Classification of Tremors. The Consensus scheme emphasises soft additional findings, mainly motor, that do not suffice to diagnose another tremor syndrome. Ten men and nine women were classified by blinded assessors according to Consensus Axis 1 definitions of ET and ET plus.

Convolutional neural network ensemble for Parkinson's disease detection from voice recordings.

The computerized detection of Parkinson's disease (PD) will facilitate population screening and frequent monitoring and provide a more objective measure of symptoms, benefiting both patients and healthcare providers. Dysarthria is an early symptom of the disease and examining it for computerized diagnosis and monitoring has been proposed. Deep learning-based approaches have advantages for such applications because they do not require manual feature extraction, and while this approach has achieved excellent results in speech recognition, its utilization in the detection of pathological voices is limited.

Muscle activation strategies of people with early-stage Parkinson's during walking.

Introduction: Some people with Parkinson's disease (PD) frequently have an unsteady gait with shuffling, reduced strength, and increased rigidity. This study has investigated the difference in the neuromuscular strategies of people with early-stage PD, healthy older adults (HOA) and healthy young adult (HYA) during short-distance walking.

Method: Surface electromyogram (sEMG) was recorded from tibialis anterior (TA) and medial gastrocnemius (MG) muscles along with the acceleration data from the lower leg from 72 subjects-24 people with early-stage PD, 24 HOA and 24 HYA during short-distance walking on a level surface using wearable sensors.

Detecting Effect of Levodopa in Parkinson's Disease Patients Using Sustained Phonemes.

Background: Parkinson's disease (PD) is a multi-symptom neurodegenerative disease generally managed with medications, of which levodopa is the most effective. Determining the dosage of levodopa requires regular meetings where motor function can be observed. Speech impairment is an early symptom in PD and has been proposed for early detection and monitoring of the disease.

Pupillometric recordings to detect glaucoma.

. Glaucoma is the second cause of vision loss with early diagnosis having significantly better prognosis. We propose the use of hippus, the steady-state pupil oscillations, obtained from an eye-tracker for computerised detection of glaucoma.

PPG Derived Respiratory Rate Estimation in Daily living Conditions.

Respiratory rate (RR) derived from photoplethysmogram (PPG) during daily activities can be corrupted due to movement and other artefacts. We have investigated the use of ensemble empirical mode decomposition (EEMD) based smart fusion approach for improving the RR extraction from PPG. PPG was recorded while subjects performed five different activities: sitting, standing, climbing and descending stairs, walking, and running.

Locomo-Net: A Low -Complex Deep Learning Framework for sEMG-Based Hand Movement Recognition for Prosthetic Control.

The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we report a deep learning framework named 'Low-Complex Movement recognition-Net' (LoCoMo-Net) built with convolution neural network (CNN) for recognition of wrist and finger flexion movements; grasping and functional movements; and force pattern from single channel surface electromyography (sEMG) recording. The network consists of a two-stage pipeline: 1) input data compression; 2) data-driven weight sharing.

Infrared retinal images for flashless detection of macular edema.

This study evaluates the use of infrared (IR) images of the retina, obtained without flashes of light, for machine-based detection of macular oedema (ME). A total of 41 images of 21 subjects, here with 23 cases and 18 controls, were studied. Histogram and gray-level co-occurrence matrix (GLCM) parameters were extracted from the IR retinal images.

Relation Between Retinal Vasculature and Retinal Thickness in Macular Edema.

This study has investigated the relationship of the retinal vasculature and the retinal thickness for Macular Edema (ME) subjects. Ninety sets of Fluorescein Angiograph (FA) and Optical Coherence Tomography (OCT) of 54 participants were analyzed. Multivariate analysis using the binary logistic regression model was used to study the association between vessel parameters and retinal thickness.