A Comparative Study of the Validity and Reliability of Two Wireless Telemetry Electrocardiogram Devices in the Emergency Medicine Department

Analysing the effect of robotic gait on lower extremity muscles and classification by using deep learning

Robotic gait training helps the nervous system recover and strengthen weak muscle groups. Many studies in the literature show that applying robotic gait rehabilitation to patients with neurological disorders such as Multiple Sclerosis (MS), Stroke and Spinal Cord Injection (SCI) effectively restores gait ability. In contrast to the studies in the literature that included only healthy individuals, both the control and patient groups were formed and detailed analyses were carried out for both groups. In this study, EMG signals in GMA, GME, ILP, BF, VM, MG, TA muscles were recorded simultaneously with a different electrode placement during robotic gait for the first time in literature and then a location that prevents a phase shift was presented. The classification performance has also been increased by removing 26 different attribute parameters like time, frequency and statistics from the signals instead of gait studies with a maximum of 12-16 traits extraction. The extracted features were classified with the approaches Multilayer Perceptron Neural Networks (MLP), Support Vector Machines (SVM), K-Nearest Neighbourhood algorithm (KNN), Random Forest Classification Algorithm (RF) and Deep Learning and then a detailed performance comparison have been realized. Among the approaches compared the Stochastic Gradient Optimization Algorithm-based deep learning structure produced the best performance with 98.5714% accuracy. It was also seen that it is essential to plan the exoskeleton and the robotic gait pattern suitable for patients' disease state and muscle activation.

Psychotic disorders and electrocardiogram abnormalities in the acute psychiatric setting: more than the metabolic syndrome alone

Medical comorbidity in severe mental illness (SMI) is high and often severe, accounting for reduced life expectancy in this group. We assessed the rate and degree of electrocardiogram (ECG) abnormalities in acutely unwell SMI male patients by reviewing the case notes of 50 consecutive admissions to an inpatient psychiatric unit. 82% were admitted with a psychotic disorder with a median age of 35 (range 19 - 72 years). Of the 29 patients who had an ECG during admission or recently (<90 days) prior to admission, 21% had a clinically relevant abnormality. 42% of patients did not have an ECG during admission or recently. Results indicate that SMI patients requiring acute care, in addition to metabolic disorders, may also have significant ECG abnormalities from a young age. Comprehensive medical monitoring, including regular ECGs and use of preventive strategies, should be an integral part of management of SMI from the outset.KEY POINTSThis small study found that out of 50 patients admitted to an acute psychiatric unit, 29 (58%) had a recent (<90 days) electrocardiogram (ECG); 6 of them (21%) had clinically relevant ECG abnormalities.ECG abnormalities were found in 24% (n = 10) of patients who had at least 1 documented ECG ever performed (n = 41, 82%).42% of patients did not have an ECG performed in the 90 days prior to or during admission.Results indicate that patients with Severe Mental Illness (SMI) requiring acute care are at risk of metabolic disorders and ECG abnormalities from a young age.Comprehensive medical monitoring including regular ECGs and early preventive strategies should be part of SMI patients' management plans from the outset. Larger scale studies are needed to assess the impact of early intervention on cardiovascular risk in SMI.