Agreement study between color and IR retinal images based on retinal vasculature morphological parameters.

BACKGROUND: Color fundus photography have been extensively used to explore the link between retinal morphology changes associated with various disease i.e. Diabetic Retinopathy, Glaucoma. The development of multimodal imaging system that integrates Infrared Scanning Laser Ophthalmoscope (IR-SLO) and Optical Coherence Tomography (OCT) could help in studying these diseases at an early stage. The aim of this study was to test the agreement between the retinal vasculature parameters from the Infrared images obtained from optical coherence tomography and color fundus imaging. METHODS: The IR and Color retinal images were obtained from 16 volunteer participants and seven retinal vessel parameters, i.e. Fractal Dimension (FD), Average Angle (ABA), Total Angle Count (TAC), Tortuosity (ST), Vessel/Background ratio (VBR), Central Retinal Arteriolar Equivalent (CRAE) and Central Retinal Venular Equivalent (CRVE) were extracted from these retinal images using Retinal Image Vasculature Assessment software (RIVAS) and Integrative Vessel Analysis (IVAN). RESULTS: The Bland Altman plot was used to investigate the agreement between the two modalities. The paired sample t-test was used to assess the presence of fixed bias and the slope of Least Square Regression (LSR) line for the presence of proportional bias. The paired sample t-test showed that there was no statistically significant difference between Color and IR based on retinal vessel features (all p values > 0.05). LSR also revealed no statistically significant difference in the retinal vessel features between Color and IR. CONCLUSION: This study has revealed that there is a fair agreement between Color and IR images based on retinal vessel features. This research has shown that it is possible to use IR images of the retina to measure the retinal vasculature parameters which has the advantage of being flash-less, can be used even if there is opacity due to cataract, and can be performed along with OCT on the same device.

Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms.

BACKGROUND: Convolution neural networks have been considered for automatic analysis of fundus images to detect signs of diabetic retinopathy but suffer from low sensitivity. METHODS: This study has proposed an alternate method using probabilistic output from Convolution neural network to automatically and simultaneously detect exudates, hemorrhages and microaneurysms. The method was evaluated using two approaches: patch and image-based analysis of the fundus images on two public databases: DIARETDB1 and e-Ophtha. The novelty of the proposed method is that the images were analyzed using probability maps generated by score values of the softmax layer instead of the use of the binary output. RESULTS: The sensitivity of the proposed approach was 0.96, 0.84 and 0.85 for detection of exudates, hemorrhages and microaneurysms, respectively when considering patch-based analysis. The results show overall accuracy for DIARETDB1 was 97.3% and 86.6% for e-Ophtha. The error rate for image-based analysis was also significantly reduced when compared with other works. CONCLUSION: The proposed method provides the framework for convolution neural network-based analysis of fundus images to identify exudates, hemorrhages, and microaneurysms. It obtained accuracy and sensitivity which were significantly better than the reported studies and makes it suitable for automatic diabetic retinopathy signs detection.

Selection of suitable hand gestures for reliable myoelectric human computer interface.

BACKGROUND: Myoelectric controlled prosthetic hand requires machine based identification of hand gestures using surface electromyogram (sEMG) recorded from the forearm muscles. This study has observed that a sub-set of the hand gestures have to be selected for an accurate automated hand gesture recognition, and reports a method to select these gestures to maximize the sensitivity and specificity. METHODS: Experiments were conducted where sEMG was recorded from the muscles of the forearm while subjects performed hand gestures and then was classified off-line. The performances of ten gestures were ranked using the proposed Positive-Negative Performance Measurement Index (PNM), generated by a series of confusion matrices. RESULTS: When using all the ten gestures, the sensitivity and specificity was 80.0% and 97.8%. After ranking the gestures using the PNM, six gestures were selected and these gave sensitivity and specificity greater than 95% (96.5% and 99.3%); Hand open, Hand close, Little finger flexion, Ring finger flexion, Middle finger flexion and Thumb flexion. CONCLUSION: This work has shown that reliable myoelectric based human computer interface systems require careful selection of the gestures that have to be recognized and without such selection, the reliability is poor.

sEMG feature evaluation for identification of elbow angle resolution in graded arm movement.

Automatic and accurate identification of elbow angle from surface electromyogram (sEMG) is essential for myoelectric controlled upper limb exoskeleton systems. This requires appropriate selection of sEMG features, and identifying the limitations of such a system.This study has demonstrated that it is possible to identify three discrete positions of the elbow; full extension, right angle, and mid-way point, with window size of only 200 milliseconds. It was seen that while most features were suitable for this purpose, Power Spectral Density Averages (PSD-Av) performed best. The system correctly classified the sEMG against the elbow angle for 100% cases when only two discrete positions (full extension and elbow at right angle) were considered, while correct classification was 89% when there were three discrete positions. However, sEMG was unable to accurately determine the elbow position when five discrete angles were considered. It was also observed that there was no difference for extension or flexion phases.

Age-associated changes in muscle activity during isometric contraction.

INTRODUCTION: We investigated the effect of age on the complexity of muscle activity and the variance in the force of isometric contraction. METHODS: Surface electromyography (sEMG) from biceps brachii muscle and force of contraction were recorded from 96 subjects (20-70 years of age) during isometric contractions. RESULTS: There was a reduction in the complexity of sEMG associated with aging. The relationship of age and complexity was approximated using a bilinear fit, with the average knee point at 45 years. There was an age-associated increase in the coefficient of variation (CoV) of the force of muscle contraction, and this increase was correlated with the decrease in complexity of sEMG (r(2) = 0.76). CONCLUSIONS: There was an age-associated increase in CoV and also a reduction in the complexity of sEMG. The correlation between these 2 factors can be explained based on the age-associated increase in motor unit density.

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. However, previous studies have not examined the effects of levodopa on speech rigorously and have provided inconsistent results. In this study, three sustained phonemes of PD patients were investigated for the effect of medication. A set of features characterizing vocal fold dynamics as well as the vocal tract coordinators were extracted from the sustained phonemes /of 28 PD patients during levodopa medication off and on states. All the features were statistically investigated and classified using a linear discriminant analysis (LDA) classifier. LDA classifier identified medication on from medication off based on the combined features from phoneme /a/, /o/ and /m/ with the accuracy=82.75% and F1-score=82.18%. Voice recording of PD patients during sustained phonemes /a/, /o/ and /m/ has the potential for identifying whether the patients are in On state or Off state of medication.Clinical Relevance- The outcomes of this study have the potential to monitor the effect and progress of levodopa on PD patients.

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. Its focus was the development of software to provide the human user with tactile information that is usually acquired intuitively in face-to-face teaching. Though rigorous assessment of the glove's application showed errors at low levels of force in actual force measurement and interpretation by users, these errors were reduced at higher levels of force. Trainers of spinal manipulation reported the device to be very useful and suitable for the purpose. We conclude that this glove has the potential for being used for online training of students.Clinical Impact: The outcome of this study shows the feasibility of developing an inexpensive haptic glove using proprietary software for online training of students of manual therapy.

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. The aim of this study was to determine the difference in the apparent vocal tract length (AVTL) between people with PD and age-matched control healthy people. The phoneme, /a/ from the UCI Parkinson's Disease Classification Dataset and the Italian Parkinson's Voice and Speech Dataset were used and AVTL was calculated based on the first four formants of the sustained phoneme (F1-F4). The results show a correlation between Parkinson's disease and an increase in vocal tract length. The most sensitive feature was the AVTL calculated using the first formants of sustained phonemes (F1). The other significant finding reported in this article is that the difference is significant and only appeared in the male participants. However, the size of the database is not sufficiently large to identify the possible confounding factors such as the severity and duration of the disease, medication, age, and comorbidity factors.Clinical relevance-The outcomes of this research have the potential to improve the identification of early Parkinsonian dysarthria and monitor PD progression.

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. In this study, we have introduced translational research between the complexity analysis using software and verifying the same by deploying it in hardware using a controller board by investigating the error percentage in classifying normal (N) and early CAN (E). The study reveals that among the segments specific to CAN diagnosis, RR and ST show more error percentages (12±8 %). In contrast, PR and QT show a lesser error percentage (6±4 %) between software and hardware implementation of Fractal dimension (FD) values.

Impact of Biological Sex on Radar-Measured Heart Sound Quality.

Radar based contact-free technology has number of potential applications for monitoring the cardiopulmonary functions of patients. However, no study has evaluated the effect of gender on the quality of the recordings. This study makes an attempt to distinguish radar based recording of male and female subjects. The study analysed a publicly available dataset of radar-recorded heart sound signals from both male and female subjects. Here, we exploit the reference signal-to-noise ratio (RSNR) to quantify the signal's quality. The results indicate that there is a significant difference in the signal quality between males and females, with males having a higher RSNR value compared to females. This could be a limitation in the widespread use of the current radar based cardiopulmonary recording techniques and overcoming this should be considered for future research.Clinical relevance- This work has highlighted the gender based difference. By considering this, the radar based cardiopulmonary device has the potential for being used for patients requiring long-term monitoring.

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. This data can also be useful to train machine learning algorithms with a view to predicting healing. Unfortunately, a high volume of good-quality data is needed to create datasets of suitable volume from people with wounds. In order to improve the process of gathering venous leg ulcer (VLU) data we propose the generative adversarial network based on StyleGAN architecture to synthesize new images from original samples. We utilized a dataset that was manually collected as part of a longitudinal observational study of VLUs and successfully synthesized new samples. These synthesized samples were validated by two clinicians. In future work, we plan to further process these new samples to train a fully automated neural network for ulcer segmentation.

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. This study has investigated the effectiveness of three groups of phoneme parameters, i.e. voice intensity variation, perturbation of glottal vibration, and apparent vocal tract length (VTL) for differentiating people with PD from healthy subjects using two public databases. The parameters were extracted from five sustained phonemes; /a/, /e/, /i/, /o/, and /u/, recorded from 50 PD patients and 50 healthy subjects of PC-GITA dataset. The features were statistically investigated, and then classified using Support Vector Machine (SVM). This was repeated on Viswanathan dataset with smartphone-based recordings of /a/, /o/, and /m/ of 24 PD and 22 age-matched healthy people. VTL parameters gave the highest difference between voices of people with PD and healthy subjects; classification accuracy with the five vowels of PC-GITA dataset was 84.3% while the accuracy for other features was between 54% and 69.2%. The accuracy for Viswanathan's dataset was 96.0%. This study has demonstrated that VTL obtained from the recording of phonemes using smartphone can accurately identify people with PD. The analysis was fully computerized and automated, and this has the potential for telehealth diagnosis for PD.

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. Several COVID-19 variants cause significant respiratory symptoms, and thus a voice signal may be a potential biomarker for COVID-19 infection. AIM: This study investigated the effectiveness of different phonemes and a range of voice features in differentiating people infected by COVID-19 with respiratory tract symptoms. METHOD: This cross-sectional, longitudinal study recorded six phonemes (i.e., /a/, /e/, /i/, /o/, /u/, and /m/) from 40 COVID-19 patients and 48 healthy subjects for 22 days. The signal features were obtained for the recordings, which were statistically analyzed and classified using Support Vector Machine (SVM). RESULTS: The statistical analysis and SVM classification show that the voice features related to the vocal tract filtering (e.g., MFCC, VTL, and formants) and the stability of the respiratory muscles and lung volume (Intensity-SD) were the most sensitive to voice change due to COVID-19. The result also shows that the features extracted from the vowel /i/ during the first 3 days after admittance to the hospital were the most effective. The SVM classification accuracy with 18 ranked features extracted from /i/ was 93.5% (with F1 score of 94.3%). CONCLUSION: A measurable difference exists between the voices of people with COVID-19 and healthy people, and the phoneme /i/ shows the most pronounced difference. This supports the potential for using computerized voice analysis to detect the disease and consider it a biomarker.

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. However, findings from previous research on the effect of levodopa on speech have not shown a consistent picture. METHOD: This study has investigated the effect of medication on PD patients for three sustained phonemes; /a/, /o/, and /m/, which were recorded from 24 PD patients during medication off and on stages, and from 22 healthy participants. The differences were statistically investigated, and the features were classified using Support Vector Machine (SVM). RESULTS: The results show that medication has a significant effect on the change of time and amplitude perturbation (jitter and shimmer) and harmonics of /m/, which was the most sensitive individual phoneme to the levodopa response. /m/ and /o/ performed at a comparable level in discriminating PD-off from control recordings. However, SVM classifications based on the combined use of the three phonemes /a/, /o/, and /m/ showed the best classifications, both for medication effect and for separating PD from control voice. The SVM classification for PD-off versus PD-on achieved an AUC of 0.81. CONCLUSION: Studies of phonation by computerized voice analysis in PD should employ recordings of multiple phonemes. Our findings are potentially relevant in research to identify early parkinsonian dysarthria, and to tele-monitoring of the levodopa response in patients with established PD.

Pupillometric recordings to detect glaucoma.

Objective. 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.Approach. Pupillary data were recorded using a commercial eye-tracker device directly to the laptop. A total of 40 glaucoma patients and 30 age-matched controls were recruited for the study. The signals were de-noised, and the entropy of the steady-state oscillations was obtained for two light intensities, 34 and 100 cd m-2.Main results. The results show that at 100 cd m-2, there was significant difference (p < 0.05) between the sample entropy of the healthy eyes (0.55 ± 0.017) and glaucoma eyes (0.7 ± 0.034). The results at 34 cd m-2were also significantly different, though to a lesser extent.Significance. Entropy of the pupillary oscillations, or hippus, obtained using an eye-tracking device showed a significant difference between glaucoma and healthy eyes. The method used commercially available inexpensive hardware and thus has the potential for wide-scale deployment for computerized detection of glaucoma.

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. The diagnostic performance of the histogram and GLCM parameters was calculated in hindsight based on the known labels of each image. The results from the one-way ANOVA indicated there was a significant difference between ME eyes and the controls when using GLCM features, with the correlation feature having the highest area under the curve (AUC) (AZ) value. The performance of the proposed method was also evaluated using a support vector machine (SVM) classifier that gave sensitivity and specificity of 100%. This research shows that the texture of the IR images of the retina has a significant difference between ME eyes and the controls and that it can be considered for machine-based detection of ME without requiring flashes of light.

Implementation and experimental validation of surface electromyogram and force model of Tibialis Anterior muscle for examining muscular factors.

This study reports a surface electromyogram and force of contraction model. The objective was to investigate the effect of changes in the size, type and number of motor units in the Tibialis Anterior muscle to surface electromyogram and force of dorsiflexion. A computational model to simulate surface electromyogram and associated force of contraction by the Tibialis Anterior muscle was developed. This model was simulated for isometric dorsiflexion, and comparative experiments were conducted for validation. Repeated simulations were performed to investigate the different parameters and evaluate inter-experimental variability. An equivalence statistical test and the Bland-Altman method were used to observe the significance between the simulated and experimental data. Simulated and experimentally recorded data had high similarity for the three measures: maximal power of power spectral density (p < 0.0001), root mean square of surface electromyogram (p < 0.0001) and force recorded at the footplate (p < 0.03). Inter-subject variability in the experimental results was in-line with the variability in the repeated simulation results. This experimentally validated computational model for the surface electromyogram and force of the Tibialis Anterior muscle is significant as it allows the examination of three important muscular factors associated with ageing and disease: size, fibre type and number of motor units.

Association between Optical Coherence Tomography and Fluorescein Angiography based retinal features in the diagnosis of Macular Edema.

The association between optical coherence tomography (OCT) and the geometrical vascular parameters obtained from the fluorescein angiography (FA) of the eyes with macular edema (ME) was investigated. Data from 82 untreated eyes with ME were studied. Fractal dimension (FD), simple tortuosity, branching angle, total angle count and vessel to background ratio were the five vasculature parameters from FA that were studied. The four OCT features measured were central retinal/foveal thickness, average para-fovea thickness, average peri-fovea thickness and OCT volume. The four OCT parameters showed a significant difference between ME requiring treatment (MERT) and non-MERT eyes with the central retinal thickness (threshold at 300 µm) and average para-fovea thickness (threshold at 338.5 µm) as most significant. The results also indicate that FD from the FA of retinal vessels in the macular region was associated with the changes in retinal thickness and that OCT parameters can potentially be used for directly identifying ME.

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

Background: 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. Methods: The proposed framework was validated on two different datasets- our own dataset (DS1) and publicly available NinaPro dataset (DS2) for 16 movements and 50 movements respectively. Further, we have prototyped the proposed LoCoMo-Net on Virtex-7 Xilinx field-programmable gate array (FPGA) platform and validated for 15 movements from DS1 to demonstrate its feasibility for real-time execution. Results: The effectiveness of the proposed LoCoMo-Net was verified by a comparative analysis against the benchmarked models using the same datasets wherein our proposed model outperformed Twin- Support Vector Machine (SVM) and existing CNN based model by an average classification accuracy of 8.5 % and 16.0 % respectively. In addition, hardware complexity analysis is done to reveal the advantages of the two-stage pipeline where approximately 27 %, 49 %, 50 %, 23 %, and 43 % savings achieved in lookup tables (LUT's), registers, memory, power consumption and computational time respectively. Conclusion: The clinical significance of such sEMG based accurate and low-complex movement recognition system can be favorable for the potential improvement in quality of life of an amputated persons.

The association of temperature of Diabetic Foot Ulcers with Chronic Kidney Disorder.

The area of the diabetic foot ulcer (DFU) and its reduction over weeks is used for assessment in clinical practices; however, literature reports that this is not reliable parameter. This work has investigated the association of change in the mean temperature of the ulcers with three clinical conditions relevant to wound healing, i.e. peripheral vascular disease (PVD), chronic kidney disease (CKD) and ischemic heart disease (IHD). Thermal and RGB images of 23 DFUs of the first two weeks of ulceration were studied. One-way ANOVA was performed on the change in mean temperature of the ulcers and change in area and it was found that the weekly change in mean temperature was higher for patients with CKD (p-value=0.009). Also, change in area measured from RGB images did not show any association with the clinical conditions. The application of this work is that the temperature obtained from thermal image of the ulcer can be used as a prognostic parameter for its assessment.