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An intelligent insole system may monitor the individual’s foot pressure and temperature in real-time from the comfort of their home, which can help capture foot problems in their earliest stages. Constant monitoring for foot complications is essential to avoid potentially devastating outcomes from common diseases such as diabetes mellitus. Inspired by those goals, the authors of this work propose a full design for a wearable insole that can detect both plantar pressure and temperature using off-the-shelf sensors. The design provides details of specific temperature and pressure sensors, circuit configuration for characterizing the sensors, and design considerations for creating a small system with suitable electronics. The procedure also details how, using a low-power communication protocol, data about the individuals’ foot pressure and temperatures may be sent wirelessly to a centralized device for storage. This research may aid in the creation of an affordable, practical, and portable foot monitoring system for patients. The solution can be used for continuous, at-home monitoring of foot problems through pressure patterns and temperature differences between the two feet. The generated maps can be used for early detection of diabetic foot complication with the help of artificial intelligence.

Diabetic neuropathy (DN) is one of the prevalent forms of neuropathy that involves alterations in biomechanical changes in the human gait. Diabetic foot ulceration (DFU) is one of the pervasive types of complications that arise due to DN. In the literature, for the last 50 years, researchers have been trying to observe the biomechanical changes due to DN and DFU by studying muscle electromyography (EMG) and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we propose using Machine learning techniques to identify DN and DFU patients by using EMG and GRF data. We collected a dataset from the literature which involves three patient groups: Control (n = 6), DN (n = 6), and previous history of DFU (n = 9) and collected three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius lateralis (GL)), and three GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and different feature extraction techniques were applied to extract the best features from the signals. The extracted feature list was ranked using four different feature ranking techniques, and highly correlated features were removed. In this study, we considered different combinations of muscles and GRF components to find the best performing feature list for the identification of DN and DFU. We trained eight different conventional ML models: Discriminant analysis classifier (DAC), Ensemble classification model (ECM), Kernel classification model (KCM), k-nearest neighbor model (KNN), Linear classification model (LCM), Naive Bayes classifier (NBC), Support vector machine classifier (SVM), and Binary decision classification tree (BDC), to find the best-performing algorithm and optimized that model. We trained the optimized the ML algorithm for different combinations of muscles and GRF component features, and the performance matrix was evaluated. Our study found the KNN algorithm performed well in identifying DN and DFU, and we optimized it before training. We found the best accuracy of 96.18% for EMG analysis using the top 22 features from the chi-square feature ranking technique for features from GL and VL muscles combined. In the GRF analysis, the model showed 98.68% accuracy using the top 7 features from the Feature selection using neighborhood component analysis for the feature combinations from the GRFx-GRFz signal. In conclusion, our study has shown a potential solution for ML application in DN and DFU patient identification using EMG and GRF parameters. With careful signal preprocessing with strategic feature extraction from the biomechanical parameters, optimization of the ML model can provide a potential solution in the diagnosis and stratification of DN and DFU patients from the EMG and GRF signals.

The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average  (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average  (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique, i.e., the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average  and  values of 30.76 dB and 59.51%, respectively, for all the EEG recordings. On the other hand, for the available 16 fNIRS recordings, the two-stage motion artifacts removal technique, i.e., WPD-CCA has produced the best average  (16.55 dB, utilizing db1 wavelet packet) and largest average  (41.40%, using fk8 wavelet packet). The highest average  and  using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively, for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed in comparison with the single-stage WPD method. In addition, the average  also increases when WPD-CCA techniques are used instead of single-stage WPD for both EEG and fNIRS signals. The increment in both  and  values is a clear indication that two-stage WPD-CCA performs relatively better compared to single-stage WPD. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.

Patients with neurological symptoms should be enquired about recent vaccination history. It is important after the COVID‐19 mRNA vaccine, which is newly introduced as it might link to the development of a wider variety of neurological diseases. Patients with neurological symptoms should be enquired about recent vaccination history. It is important after the COVID‐19 mRNA vaccine, which is newly introduced as it might link to the development of a wider variety of neurological diseases.

Introduction Migraine is a debilitating neurological disorder characterized by recurrent throbbing, moderate-to-severe headaches that disrupt daily chores, leisure, and social activities of patients, impacting their overall quality of life (QoL). Despite the high disease burden, there is a scarcity of data on migraines within the Middle East (ME) region. Thus, a systematic literature review (SLR) was conducted to examine epidemiological data, treatment patterns, QoL, and unmet needs regarding migraines in the ME region. Methods Electronic searches were carried out using the MEDLINE® and Embase® databases via the OvidSP® platform for articles published prior to April 2024. The inclusion and exclusion criteria for the selection of studies were based on the Patients, Intervention, Comparator, Outcomes, and Study design framework, which identified 42 studies. Results The prevalence of migraines reported from the region ranged between 2.6 and 32%, and the average age of patients with migraines reported in these studies ranged from 27 to 37.5 years. The data indicated a gender disparity in migraine prevalence, with women exhibiting a 2- to 2.5-fold higher prevalence. Common comorbidities reported were depression, anxiety, and irritable bowel disease. Migraines significantly impact patients' physical and emotional well-being, leading to disabilities and loss of productivity. The most common triggers of migraines were sleep disorders, dietary habits, and stress. The current treatment landscape for acute migraines encompasses anti-inflammatory agents, analgesics, triptans, ditans, calcitonin-gene-related peptides, and antiemetics. However, migraines in the region are often underestimated, underreported, and undertreated. Several unmet needs persist in the region, including delayed referral along with delayed diagnosis, misdiagnosis, poor treatment adherence, limited accessibility to treatments, and a lack of awareness among health care providers and patients. Conclusions The SLR highlights knowledge gaps in clinical aspects and the treatment of migraines and enables clinicians to make informed decisions to ensure optimal patient outcomes in diverse clinical settings.

This study investigated the risk factors for COVID-19 and its impact on diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes (T2D). Patients with T2D underwent assessments with the NICE post-COVID questionnaire, DN4 questionnaire, vibration perception threshold (VPT), and corneal confocal microscopy (CCM) before and 11.0 ± 8.9 months after developing COVID-19. Of 76 participants with T2D, 35 (46.1%) developed COVID-19, of whom 8 (22.9%) developed severe COVID-19 and 9 (25.7%) developed long-COVID. The development of COVID-19 was associated with lower systolic blood pressure ( P  < 0.05). The presence and severity of DPN were not associated with developing COVID-19, severe COVID-19, or long-COVID ( P  = 0.42–0.94). Women were eight times more likely to develop long-COVID ( P  < 0.05) and elevated body weight, LDL, and VPT were associated with the development of long-COVID ( P  < 0.05 − 0.01). The long-COVID group exhibited significant changes in triglycerides and LDL ( P  < 0.05 for both) and body weight ( P  < 0.01) at follow-up. Their impact on clinical and neuropathy measures was comparable in patients with and without COVID-19 ( P  = 0.08–0.99). There was a significant reduction in corneal nerve measures ( P  < 0.05-0.0001) in patients with and without COVID-19. A low systolic blood pressure, altered lipids, body weight, higher VPT, and gender may determine the impact of COVID-19 in patients with T2D, but there was no evidence of an impact of COVID-19 on the development or progression of DPN.

Guillain–Barré syndrome is an acute immune‐mediated demyelinating disease. Typical features include progressive ascending lower extremity weakness and areflexia. Several variants have been described that can make the diagnosis challenging. Here, we report a case of GBS presenting with progressive lower limb weakness and T6 sensory level. To bring attention to the wide spectrum of presentation of Guillain–Barre syndrome by presenting a rare, atypical variant with a sensory level which was successfully managed by our team so that clinicians keep this deferential in mind when they face patients with such neurological manifestation.

Diabetic sensorimotor polyneuropathy (DSPN) is a serious long-term complication of diabetes, which may lead to foot ulceration and amputation. Among the screening tools for DSPN, the Michigan neuropathy screening instrument (MNSI) is frequently deployed, but it lacks a straightforward rating of severity. A DSPN severity grading system has been built and simulated for the MNSI, utilizing longitudinal data captured over 19 years from the Epidemiology of Diabetes Interventions and Complications (EDIC) trial. Machine learning algorithms were used to establish the MNSI factors and patient outcomes to characterise the features with the best ability to detect DSPN severity. A nomogram based on multivariable logistic regression was designed, developed and validated. The extra tree model was applied to identify the top seven ranked MNSI features that identified DSPN, namely vibration perception (R), 10-gm filament, previous diabetic neuropathy, vibration perception (L), presence of callus, deformities and fissure. The nomogram’s area under the curve (AUC) was 0.9421 and 0.946 for the internal and external datasets, respectively. The probability of DSPN was predicted from the nomogram and a DSPN severity grading system for MNSI was created using the probability score. An independent dataset was used to validate the model’s performance. The patients were divided into four different severity levels, i.e., absent, mild, moderate, and severe, with cut-off values of 10.50, 12.70 and 15.00 for a DSPN probability of less than 50, 75 and 100%, respectively. We provide an easy-to-use, straightforward and reproducible approach to determine prognosis in patients with DSPN.

BackgroundThe etiology of facial nerve palsy is diverse and includes herpes zoster virus, Guillain-Barre syndrome (GBS), otitis media, Lyme disease, sarcoidosis, human immunodeficiency virus, etc. The lower motor neuron type facial nerve palsy is usually caused by an ipsilateral facial nerve lesion; however, it may be caused by a central lesion of the facial nerve nucleus and tract in the pons. Facial diplegia is an extremely rare condition that occurs in approximately 0.3% to 2.0% of all facial palsies. Electrodiagnostic studies including direct facial nerve conduction, facial electromyography (EMG), and blink reflex studies are useful for the prognosis and lesion localization in facial nerve palsy.MethodologyThis retrospective, observational study was conducted at the Neurophysiology Unit, Hamad General Hospital, Doha, Qatar. This study included 11 patients with bilateral facial weakness who visited for electrodiagnostic studies in the neurophysiology laboratory.ResultsIn total, eight (72.7%) patients had facial diplegia, eight (72.7%) had hypo/areflexia, seven (63.6%) had facial numbness, and five (45.5%) had cerebrospinal fluid albuminocytological dissociation. The most frequent cause of facial diplegia in this study was GBS (81.9%). Direct facial nerve conduction stimulation showed that nine (81.8%) patients had bilateral facial nerve low compound muscle action potential amplitudes. The bilateral blink reflex study showed that eight (88.8%) patients had absent bilateral evoked responses. Finally, the EMG study showed that five (55.5%) patients had active denervation in bilateral sample facial muscles.ConclusionsBilateral facial nerve palsy is an extremely rare condition with a varied etiology. Electrodiagnostic studies are useful in detecting the underlying pathophysiologic processes, prognosis, and central or peripheral lesion localization in patients with facial diplegia.

The introduction of numerous therapeutic advancements in the management of myasthenia gravis (MG) may add difficulties in clinical decision-making, especially when no recommendations tailored to the local context are available. For this reason, the Saudi Arabia Neuromuscular and Electrodiagnostic Medicine (SANEM) chapter of the Saudi Neurology Society launched an initiative to discuss and agree on issues related to the management of MG in the Gulf Cooperation Council (GCC) region. An expert panel from all GCC countries (Saudi Arabia, United Arab Emirates, Bahrain, Kuwait, Qatar, and Oman) was formed to develop practical recommendations using the Delphi method to facilitate the management approach of MG and enhance patient outcomes. Plain language summary The diagnosis and treatment of myasthenia gravis by experts in Saudi Arabia and the gulf region Myasthenia Gravis (MG) is a condition that affects how muscles work. With so many new treatments arising, the condition is becoming more complex to manage. This makes it challenging for doctors to choose the best care. To address this, the Saudi Neurology Society’s SANEM (Saudi Arabia Neuromuscular and Electrodiagnostic Medicine) chapter gathered experts from all GCC countries to create practical recommendations for MG using a structured method called the Delphi method. These recommendations make it easier for doctors to choose the best treatment for patients with MG.