A novel approach for analysis of altered gait variability in amyotrophic lateral sclerosis

A new parallel-path ConvMixer neural network for predicting neurodegenerative diseases from gait analysis

Neurodegenerative disorders (NDD) represent a broad spectrum of diseases that progressively impact neurological function, yet available therapeutics remain conspicuously limited. They lead to altered rhythms and dynamics of walking, which are evident in the sequential footfall contact times measured from one stride to the next. Early detection of aberrant walking patterns can prevent the progression of risks associated with neurodegenerative diseases, enabling timely intervention and management. In this study, we propose a new methodology based on a parallel-path ConvMixer neural network for neurodegenerative disease classification from gait analysis. Earlier research in this field depended on either gait parameter-derived features or the ground reaction force signal. This study has emerged to combine both ground reaction force signals and extracted features to improve gait pattern analysis. The study is being carried out on the gait dynamics in the NDD database, i.e., on the benchmark dataset Physionet gaitndd. Leave one out cross-validation is carried out. The proposed model achieved the best average rates of accuracy, precision, recall, and an F1-score of 97.77 % , 96.37 % , 96.5 % , and 96.25 % , respectively. The experimental findings demonstrate that our approach outperforms the best results achieved by other state-of-the-art methods.

Automatic selection model to identify neurodegenerative diseases

Objective

This study evaluates machine learning algorithms' effectiveness in classifying Parkinson's disease and Huntington's disease based on biomarker data obtained non-invasively from patients and healthy controls.

Methods

Datasets containing biomarker data (x, y, and z values of accelerometers) from sensors were collected from Parkinson's disease, Huntington's disease patients, and healthy controls. An automatic selection model method was implemented for disease classification, using a unique Mexican database of human gait biomarkers, which we consider the only one of its kind. Random forest, random subspace method, and K-star algorithms were employed, with parameters optimized through an automated model selection.

Results

The study achieved a 0.893 precision rate for Parkinson's disease and Huntington's disease using the random subspace method. The findings underscore the potential of machine learning techniques in medical diagnosis, particularly in neurological disorders.

Conclusion

The automatic selection model method demonstrated efficacy in classifying Parkinson's disease and Huntington's disease based on non-invasive biomarker data. This research contributes to advancing non-invasive diagnostic approaches in neurological disorders, highlighting the significance of machine learning in healthcare.

Blast Overpressures as a Military and Occupational Health Concern

PURPOSE

This tutorial reviews effects of environmental stressors like blast overpressures and other well-known acoustic contaminants (continuous, intermittent, and impulsive noise) on hearing, tinnitus, vestibular, and balance-related functions. Based on the overall outcome of these effects, detailed consideration is given to the health and well-being of individuals.

METHOD

Because hearing loss and tinnitus are consequential in affecting quality of life, novel neuromodulation paradigms are reviewed for their positive abatement and treatment-related effects. Examples of clinical data, research strategies, and methodological approaches focus on repetitive transcranial magnetic stimulation (rTMS) and electrical stimulation of the vagus nerve paired with tones (VNSt) for their unique contributions to this area.

RESULTS

Acoustic toxicants transmitted through the atmosphere are noteworthy for their propensity to induce hearing loss and tinnitus. Mounting evidence also indicates that high-level rapid onset changes in atmospheric sound pressure can significantly impact vestibular and balance function. Indeed, the risk of falling secondary to loss of, or damage to, sensory receptor cells in otolith organs (utricle and saccule) is a primary reason for this concern. As part of the complexities involved in VNSt treatment strategies, vocal dysfunction may also manifest. In addition, evaluation of temporospatial gait parameters is worthy of consideration based on their ability to detect and monitor incipient neurological disease, cognitive decline, and mortality.

CONCLUSION

Highlighting these respective areas underscores the need to enhance information exchange among scientists, clinicians, and caregivers on the benefits and complications of these outcomes.

Altered gait parameters in distracted walking: a bio-evolutionary and prognostic health perspective on passive listening and active responding during cell phone use

The underpinnings of bipedal gait are reviewed from an evolutionary biology and prognostic health perspective to better understand issues and concerns related to cell phone use during ambulation and under conditions of distraction and interference. We also consider gait-related health issues associated with the fear of or risk of falling and include prognostic dimensions associated with cognitive decline, dementia, and mortality. Data were acquired on 21 healthy young adults without hearing loss, vestibular, balance, otological or neurological dysfunction using a computerized walkway (GAITRite® Walkway System) combined with specialized software algorithms to extract gait parameters. Four experimental conditions and seven temporo-spatial gait parameters were studied: gait velocity, cadence, stride length, ambulatory time, single-support time, double-support time, and step count. Significant main effects were observed for ambulation time, velocity, stride velocity, and double-support time. The greatest impact of distraction and interference occurred during the texting condition, although other significant effects occurred when participants were verbally responding to queries and passively listening to a story. These experimental observations show that relatively simple distraction and interference tasks implemented through the auditory sensory modality can induce significant perturbations in gait while individuals were ambulating and using a cell phone. Herein, emphasis is placed on the use of quantifiable gait parameters in medical, psychological, and audiological examinations to serve as a foundation for identifying and potentially averting gait-related disturbances.

Multimodal Gait Recognition for Neurodegenerative Diseases

In recent years, single modality-based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognized that each of the established approaches has different strengths and weaknesses. As an important motor symptom, gait disturbance is usually used for diagnosis and evaluation of diseases; moreover, the use of multimodality analysis of the patient's walking pattern compensates for the one-sidedness of single modality gait recognition methods that only learn gait changes in a single measurement dimension. The fusion of multiple measurement resources has demonstrated promising performance in the identification of gait patterns associated with individual diseases. In this article, as a useful tool, we propose a novel hybrid model to learn the gait differences between three neurodegenerative diseases, between patients with different severity levels of Parkinson's disease, and between healthy individuals and patients, by fusing and aggregating data from multiple sensors. A spatial feature extractor (SFE) is applied to generating representative features of images or signals. In order to capture temporal information from the two modality data, a new correlative memory neural network (CorrMNN) architecture is designed for extracting temporal features. Afterward, we embed a multiswitch discriminator to associate the observations with individual state estimations. Compared with several state-of-the-art techniques, our proposed framework shows more accurate classification results.

Petri net transition times as training features for multiclass models to support the detection of neurodegenerative diseases

This paper proposes the transition times of Petri net models of human gait as training features for multiclass random forests (RFs) and classification trees (CTs). These models are designed to support screening for neurodegenerative diseases. The proposed Petri net describes gait in terms of nine cyclic phases and the timing of the nine events that mark the transition between phases. Since the transition times between strides vary, each is represented as a random variable characterized by its mean and standard deviation. These transition times are calculated using the PhysioNet database of vertical ground reaction forces (VGRFs) generated by feet-ground contact. This database comprises the VGRFs of four groups: amyotrophic lateral sclerosis, the control group, Huntington's disease, and Parkinson disease. The RF produced an overall classification accuracy of 91%, and the specificities and sensitivities for each class were between 80% and 100%. However, despite this high performance, the RF-generated models demonstrated lack of interpretability prompted the training of a CT using identical features. The obtained tree comprised only four features and required a maximum of three comparisons. However, this simplification dramatically reduced the overall accuracy from 90.6% to 62.3%. The proposed set features were compared with those included in PhysioNet database of VGRFs. In terms of both the RF and CT, more accurate models were established using our features than those of the PhysioNet.

Functional ambulation decline and factors associated in amyotrophic lateral sclerosis

Abstract Introduction: Amyotrophic lateral sclerosis (ALS) is a disabling neurodegenerative disease, which compromises locomotion and functional independence. As the goal of physical therapy is to maintain the individual's locomotion capacity and independence as long as possible, it is necessary to gain a better understanding of the possible factors associated with the loss of this capacity. Objective: To evaluate functional ambulation in patients with ALS and possible factors associated with its decline. Methods: A cross-sectional study was conducted with sporadic ALS patients. Demographic and clinical/functional aspects were evaluated. ALS Functional Rating Scale-Revised (ALSFRS-R), Functional Ambulation Category, Medical Research Council scale and Fatigue Severity Scale were used. Descriptive and comparative analyses were conducted of the groups capable and incapable of functional ambulation. Binary logistic regression (stepwise forward method) was performed to determine potential factors associated with the loss of functional ambulation. Results: Among the 55 patients (mean age: 56.9 ± 11.2 years), 74.5% were able to walk functionally. Differences were found between groups regarding time of diagnosis, number of falls, pain, use of noninvasive ventilation, gastrostomy, ability to turn in bed, mobility aids, home adaptations, functional performance, muscle strength and fatigue. The possible predictors of walking disability were overall muscle strength (OR = 0.837; p = 0.003) and fatigue (OR =1.653; p = 0.034). Conclusion: Muscle strength and fatigue are associated with the decline in ambulation capacity in patients with ALS. In view of the complexity of elements involved in walking, further studies are needed to investigate the influence of these aspects in this population.

Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques

Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.

Multiscale Approximate Entropy for Gait Analysis in Patients with Neurodegenerative Diseases

Neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), Parkinson’s diseases (PD), and Huntington’s disease (HD) are not rare neurological diseases. They affect different neurological systems and present various characteristic gait abnormalities. We retrieved gait signals of the right and left feet from a public domain on the Physionet. There were 13 patients with ALS, 15 patients with PD, 20 patients with HD and 16 healthy controls (HC). We used multiscale approximate entropy (MAE) to analyze ground reaction force on both feet. Our study shows that MAE increases with scales in all tested subjects. The group HD has the highest MAE and group ALS has the lowest MAE. We can differentiate ALS from HC by MAE, while scale factors >10 in the left foot. There are few significant differences of MAE between the HC and HD. We found a good correlation of MAE between both feet in group ALS. In conclusion, our results indicate that MAE analysis of gait signals can be used for diagnosis and long-term assessment for ALS and probably HD. Similarity of MAE between both feet can also be a diagnostic marker for ALS.

Buprenorphine Depresses Respiratory Variability in Obese Mice with Altered Leptin Signaling

BACKGROUND

Opiate-induced respiratory depression is sexually dimorphic and associated with increased risk among the obese. The mechanisms underlying these associations are unknown. The present study evaluated the two-tailed hypothesis that sex, leptin status, and obesity modulate buprenorphine-induced changes in breathing.

METHODS

Mice (n = 40 male and 40 female) comprising four congenic lines that differ in leptin signaling and body weight were injected with saline and buprenorphine (0.3 mg/kg). Whole-body plethysmography was used to quantify the effects on minute ventilation. The data were evaluated using three-way analysis of variance, regression, and Poincaré analyses.

RESULTS

Relative to B6 mice with normal leptin, buprenorphine decreased minute ventilation in mice with diet-induced obesity (37.2%; P < 0.0001), ob/ob mice that lack leptin (62.6%; P < 0.0001), and db/db mice with dysfunctional leptin receptors (65.9%; P < 0.0001). Poincaré analyses showed that buprenorphine caused a significant (P < 0.0001) collapse in minute ventilation variability that was greatest in mice with leptin dysfunction. There was no significant effect of sex or body weight on minute ventilation.

CONCLUSIONS

The results support the interpretation that leptin status but not body weight or sex contributed to the buprenorphine-induced decrease in minute ventilation. Poincaré plots illustrate that the buprenorphine-induced decrease in minute ventilation variability was greatest in mice with impaired leptin signaling. This is relevant because normal respiratory variability is essential for martialing a compensatory response to ventilatory challenges imposed by disease, obesity, and surgical stress.

Selection of clinical features for pattern recognition applied to gait analysis

This paper deals with the opportunity of extracting useful information from medical data retrieved directly from a stereophotogrammetric system applied to gait analysis. A feature selection method to exhaustively evaluate all the possible combinations of the gait parameters is presented, in order to find the best subset able to classify among diseased and healthy subjects. This procedure will be used for estimating the performance of widely used classification algorithms, whose performance has been ascertained in many real-world problems with respect to well-known classification benchmarks, both in terms of number of selected features and classification accuracy. Precisely, support vector machine, Naive Bayes and K nearest neighbor classifiers can obtain the lowest classification error, with an accuracy greater than 97 %. For the considered classification problem, the whole set of features will be proved to be redundant and it can be significantly pruned. Namely, groups of 3 or 5 features only are able to preserve high accuracy when the aim is to check the anomaly of a gait. The step length and the swing speed are the most informative features for the gait analysis, but also cadence and stride may add useful information for the movement evaluation.

Symmetry Analysis of Gait between Left and Right Limb Using Cross-Fuzzy Entropy

The purpose of this paper is the investigation of gait symmetry problem by using cross-fuzzy entropy (C-FuzzyEn), which is a recently proposed cross entropy that has many merits as compared to the frequently used cross sample entropy (C-SampleEn). First, we used several simulation signals to test its performance regarding the relative consistency and dependence on data length. Second, the gait time series of the left and right stride interval were used to calculate the C-FuzzyEn values for gait symmetry analysis. Besides the statistical analysis, we also realized a support vector machine (SVM) classifier to perform the classification of normal and abnormal gaits. The gait dataset consists of 15 patients with Parkinson's disease (PD) and 16 control (CO) subjects. The results show that the C-FuzzyEn values of the PD patients' gait are significantly higher than that of the CO subjects with a p value of less than 10⁻⁵, and the best classification performance evaluated by a leave-one-out (LOO) cross-validation method is an accuracy of 96.77%. Such encouraging results imply that the C-FuzzyEn-based gait symmetry measure appears as a suitable tool for analyzing abnormal gaits.