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. RESULTS: There was a significant increase in the co-activation, a reduction in the TA modulation and an increase in the TA-MG lateral asymmetry among the people with PD during a level, straight-line walking. For people with PD, the gait impairment scale was low with an average postural instability and gait disturbance (PIGD) score = 5.29 out of a maximum score of 20. Investigating the single and double support phases of the gait revealed that while the muscle activity and co-activation index (CI) of controls modulated over the gait cycle, this was highly diminished for people with PD. The biggest difference between CI of controls and people with PD was during the double support phase of gait. DISCUSSION: The study has shown that people with early-stage PD have high asymmetry, reduced modulation, and higher co-activation. They have reduced muscle activity, ability to inhibit antagonist, and modulate their muscle activities. This has the potential for diagnosis and regular assessment of people with PD to detect gait impairments using wearable sensors.

A Non-Invasive IR Sensor Technique to Differentiate Parkinson's Disease from Other Neurological Disorders Using Autonomic Dysfunction as Diagnostic Criterion.

Early diagnosis of Parkinson's disease (PD) plays a critical role in effective disease management and delayed disease progression. This study reports a technique that could diagnose and differentiate PD from essential tremor (ET) in its earlier stage using a non-motor phenotype. Autonomic dysfunction, an early symptom in PD patients, is caused by α-synuclein pathogenesis in the central nervous system and can be diagnosed using skin vasomotor response to cold stimuli. In this study, the investigations were performed using data collected from 20 PD, 20 ET and 20 healthy subjects. Infrared thermography was used for the cold stress test to observe subjects' hand temperature before and after cold stimuli. The results show that the recovery rate of hand temperature was significantly different between the groups. The data obtained in the cold stress test were verified using Pearson's cross-correlation technique, which showed that few disease parameters like medication and motor rating score had an impact on the recovery rate of hand temperature in PD subjects. The characteristics of the three groups were compared and classified using the k-means clustering algorithm. The sensitivity and specificity of these techniques were analyzed using an Receiver Operating Characteristic (ROC) curve analyzer. These results show that this non-invasive technique can be used as an effective tool in the diagnosis and differentiation of PD in its early stage.

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.

A Review of Spectroscopic and Non-Spectroscopic Techniques for Diagnosing Breast Cancer.

Malignancy, one of the leading causes of death worldwide, accounts for 9.6 million deaths in 2018. Around 1 out of 6 deaths are the direct result of the malignancy. Clinicians claim that age and breast density are two preliminary factors increasing the risk of cancer. The mortality rate brought about by malignant growth in low and high income countries is, for the most part, around 70%. Imaging techniques play a vital role in the detection, and staging, thereby helping in treatment decision making. This review paper presents a comprehensive survey involving a literature study about the evolution and efficacy of various breast cancer detection techniques. This work studies various procedures of imaging techniques such as mammograms, ultrasound, MRI, PET, CT, Terahertz Spectroscopy, Raman Spectroscopy, Optical coherence Tomography, Mass spectroscopy, diffuse reflectance spectroscopy, and Infrared Thermography. Since cancer is a complicated illness with diverse pathophysiologies, numerous modifications of the fundamental detection approach employed in each of these modalities have been performed throughout the years to increase the detection efficiency. This paper covers basic preliminary results with FFPE breast cancer blocks of malignant and normal subjects using THz Techniques that are presented as proof of concept to carry out further research.

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.

Spatial Intensity Map of HDEMG Based Classification of Muscle Fatigue.

In this, study, we have investigated to identify the muscle fatigue using spatial maps of High-Density Electromyography (HDEMG). The experiment involves subjects performing plantar flexion at 40% maximum voluntary contraction until fatigue. During the experiment, HDEMG signal was recorded from the tibialis anterior muscle. The monopolar and bipolar spatial intensity maps were extracted from the HDEMG signal. The random forest classifier with different tree configurations was tested to distinguish nonfatigue and fatigue condition. The results indicate that selected electrodes from the differential intensity map results in an accuracy of 83.3% with the number of trees set at 17. This method of spatial analysis of HDEMG signals may be extended to assess fatigue in real life scenarios.

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.

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.

Differentiating between Parkinson's disease patients and controls using variability in muscle activation during walking.

Surface electromyography (sEMG) of the lower limb muscles has been proposed to evaluate motor dysfunctions in Parkinson's disease (PD) patients. Variability in the sEMG could be used as an indicator of poor muscle coordination, but previous studies have reported conflicting results. This study has examined the variability of muscle using the coefficients of variance of Tibialis anterior (TA) and Medial gastrocnemius (MG) lower limb muscles for 24 PD, 24 age matched controls (CO), and 24 young controls (YC), during different phases of the gait cycle. The gait intervals were measured using the inertial measurement unit (IMU). We observed a statistically significant difference between PD and control for the variability of lower limb muscle when comparing the sub-phases of the gait. It was also found that the difference was more pronounced for the TA muscle.

Estimation of Parkinson's disease severity from voice features of vowels and consonant.

This study has investigated the efficiency of voice features in estimating the motor Unified Parkinson's Disease Rating Scale (UPDRS) score in Parkinson's disease (PD) patients. A total of 26 PD patients (mean age = 72) and 22 control subjects (mean age = 66.91) were recruited for the study. The sustained phonation /a/, /u/ and /m/ were collected in both off-state and on-state of Levodopa medication. The average motor UPDRS for PD off-state patients was 27.31, on-state was 20.42 and that of controls was 2.63. Voice features were extracted from the phonation tasks and were reduced to the most relevant 6 features for each phonation task using the Least Absolute Shrinkage and Selection Operator (LASSO) feature ranking method. The correlation between the reduced features and motor UPDRS was tested using the Spearman correlation coefficient test. AdaBoost regression learner was trained and used for automatically estimating the motor UPDRS score using the voice features. The results show that the vocal features for /m/ performed best by estimating the motor UPDRS score for PD off-state with the mean absolute error (MAE) of 3.52 and 5.90 for PD on-state. This study shows that assessment of voice can be used for day to day remote monitoring of PD patients.

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.

Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson's Disease.

In this paper, we have investigated the differences in the voices of Parkinson's disease (PD) and age-matched control (CO) subjects when uttering three phonemes using two complexity measures: fractal dimension (FD) and normalised mutual information (NMI). Three sustained phonetic voice recordings, /a/, /u/ and /m/, from 22 CO (mean age = 66.91) and 24 PD (mean age = 71.83) participants were analysed. FD was first computed for PD and CO voice recordings, followed by the computation of NMI between the test groups: PD-CO, PD-PD and CO-CO. Four features reported in the literature-normalised pitch period entropy (Norm. PPE), glottal-to-noise excitation ratio (GNE), detrended fluctuation analysis (DFA) and glottal closing quotient (ClQ)-were also computed for comparison with the proposed complexity measures. The statistical significance of the features was tested using a one-way ANOVA test. Support vector machine (SVM) with a linear kernel was used to classify the test groups, using a leave-one-out validation method. The results showed that PD voice recordings had lower FD compared to CO (p < 0.008). It was also observed that the average NMI between CO voice recordings was significantly lower compared with the CO-PD and PD-PD groups (p < 0.036) for the three phonetic sounds. The average NMI and FD demonstrated higher accuracy (>80%) in differentiating the test groups compared with other speech feature-based classifications. This study has demonstrated that the voices of PD patients has reduced FD, and NMI between voice recordings of PD-CO and PD-PD is higher compared with CO-CO. This suggests that the use of NMI obtained from the sample voice, when paired with known groups of CO and PD, can be used to identify PD voices. These findings could have applications for population screening.

Normalized Mutual Information of phonetic sound to distinguish the speech of Parkinson's disease.

This study has investigated the use of inter-personnel mutual information computed from the phonetic sound recordings to differentiate between Parkinson's disease (PD) and control subjects. The normalized mutual information (NMI) denotes the amount of information shared between the voice recordings of people within the same group: PD and Control. The hypothesis of this study was that within group NMI will be significantly different when compared with inter- group NMI. For each phonetic sound, the NMI was computed for every pairing of recordings for both the PD and control groups. Pearson correlation coefficient analysis was used to determine the association of NMI with clinical parameters including Unified Parkinson's Disease Rating Scale (UPDRS), Montreal cognitive assessment (MoCA) and disease duration. ANOVA test for the three phonetic sounds of control and PD subjects showed that there is significant difference between the intra-group mean NMI for the two groups (p <; 0.003) and also showed significant association with the UPDRS motor examination score, MoCA and disease duration.

Measuring complexity in different muscles during sustained contraction using fractal properties of SEMG signal.

Modelling and analysis of surface Electromyogram (sEMG) signal has gained increasing attention in bio-signal processing for medical and healthcare applications. This research reports the study to examine the complexity in surface electromyogram signal measured from different muscles to identify the properties of muscles. Experiments were conducted to study the properties of the four muscle groups representing four sizes in length and complexities: Zygomaticus (facial), biceps, quadriceps and flexor digitorum superficialis (FDS). Complexity of the sEMG signal was computed using Higuchi's Fractal dimension. The relationship between FD and the muscle properties was investigated. Experimental results demonstrate that for a small variation in muscle contraction, there is very small change in the value of complexity (measured using Fractal dimension $\sim 0.1$%) and indicates that the larger and more complex muscles having a higher complexity at MVC. It is observed that the change in FD with muscle contraction is a result of changes in the properties of the particular muscle and its associated movement or change in length.

Introducing a Novel Layer in Convolutional Neural Network for Automatic Identification of Diabetic Retinopathy.

Convolutional neural networks have been widely used for identifying diabetic retinopathy on color fundus images. For such application, we proposed a novel framework for the convolutional neural network architecture by embedding a preprocessing layer followed by the first convolutional layer to increase the performance of the convolutional neural network classifier. Two image enhancement techniques i.e. 1- Contrast Enhancement 2- Contrast-limited adaptive histogram equalization were separately embedded in the proposed layer and the results were compared. For identification of exudates, hemorrhages and microaneurysms, the proposed framework achieved the total accuracy of 87.6%, and 83.9% for the contrast enhancement and contrast-limited adaptive histogram equalization layers, respectively. However, the total accuracy of the convolutional neural network alone without the prreprocessing layer was found to be 81.4%. Consequently, the new convolutional neural network architecture with the proposed preprocessing layer improved the performance of convolutional neural network.

Identifying Noisy Electrodes in High Density Surface Electromyography Recordings Through Analysis of Spatial Similarities.

In this study we developed a technique for identifying noisy electrodes in high density surface electromyography (HD-sEMG). The technique finds the spatial similarity of each electrode in the electrode array by counting the number of interactions the electrode has. Using this information the technique identifies noisy electrodes by finding electrodes that are significantly dissimilar to the other electrodes. The HD-sEMG recordings used in this study were taken from three participants who performed two isometric contractions of their biceps at 40% and 80% of their maximum voluntary contraction (MVC) force. White Gaussian noisy was added to a varying number of recorded signals before being digital filtering to generate a variety of recordings to test the technique with. In the recordings, groups of 2, 4, 8, and 16 electrodes had noise added such that the signal to noise ratios (SNR) were 0, 5, 10, 15, and 20dB. The results show that the technique can reliably identify groups of 2, 4, and 8 noisy electrodes with SNRs of 0, 5, and 10dB.

Efficacy of Guided Spiral Drawing in the Classification of Parkinson's Disease.

BACKGROUND: Change of handwriting can be an early marker for severity of Parkinson's disease but suffers from poor sensitivity and specificity due to inter-subject variations. AIM: This study has investigated the group-difference in the dynamic features during sketching of spiral between PD and control subjects with the aim of developing an accurate method for diagnosing PD patients. METHOD: Dynamic handwriting features were computed for 206 specimens collected from 62 Subjects (31 Parkinson's and 31 Controls). These were analyzed based on the severity of the disease to determine group-difference. Spearman rank correlation coefficient was computed to evaluate the strength of association for the different features. RESULTS: Maximum area under ROC curve (AUC) using the dynamic features during different writing and spiral sketching tasks were in the range of 0.67 to 0.79. However, when angular features ($\boldsymbol{\varphi }$ and ${\boldsymbol{p}_{\boldsymbol{n}}}$) and count of direction inversion during sketching of the spiral were used, AUC improved to 0.933. Spearman correlation coefficient was highest for ϕ and ${\boldsymbol{p}_{\boldsymbol{n}}}$. CONCLUSION: The angular features and count of direction inversion which can be obtained in real-time while sketching the Archimedean guided spiral on a digital tablet can be used for differentiating between Parkinson's and healthy cohort.

An ICA-EBM-Based sEMG Classifier for Recognizing Lower Limb Movements in Individuals With and Without Knee Pathology.

Surface electromyography (sEMG) data acquired during lower limb movements has the potential for investigating knee pathology. Nevertheless, a major challenge encountered with sEMG signals generated by lower limb movements is the intersubject variability, because the signals recorded from the leg or thigh muscles are contingent on the characteristics of a subject such as gait activity and muscle structure. In order to cope with this difficulty, we have designed a three-step classification scheme. First, the multichannel sEMG is decomposed into activities of the underlying sources by means of independent component analysis via entropy bound minimization. Next, a set of time-domain features, which would best discriminate various movements, are extracted from the source estimates. Finally, the feature selection is performed with the help of the Fisher score and a scree-plot-based statistical technique, prior to feeding the dimension-reduced features to the linear discriminant analysis. The investigation involves 11 healthy subjects and 11 individuals with knee pathology performing three different lower limb movements, namely, walking, sitting, and standing, which yielded an average classification accuracy of 96.1% and 86.2%, respectively. While the outcome of this study per se is very encouraging, with suitable improvement, the clinical application of such an sEMG-based pattern recognition system that distinguishes healthy and knee pathological subjects would be an attractive consequence.