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The progressive loss of motor function in the brain is a hallmark of Parkinson's disease (PD). Electroencephalogram (EEG) signals are commonly used for early diagnosis since they are associated with a brain disorder. This work aims to find a better way to represent electroencephalography (EEG) signals and enhance the classification accuracy of individuals with Parkinson's disease using EEG signals. In this paper, we present two hybrid deep neural networks (DNN) that combine convolutional neural networks with long short-term memory to diagnose Parkinson's disease using EEG signals, that is, through the establishment of parallel and series combined models. The deep CNN network is utilized to acquire the structural features of ECG signals and extract meaningful information from them, after which the signals are sent via a long short-term memory network to extract the features' context dependency. The proposed architecture was able to achieve 97.6% specificity, 97.1% sensitivity, and 98.6% accuracy for a parallel model and 99.1% specificity, 98.5% sensitivity, and 99.7% accuracy for a series model, both in 3-class classification (PD patients with medication, PD patients without medication and healthy).
RESUMO
Background and aims Peripheral neuropathy is a frequent complication of long-standing diabetes mellitus that adversely affects the quality of life. Pregabalin (anticonvulsant) and duloxetine (antidepressant) are often prescribed for diabetic peripheral neuropathic pain. This study aimed to determine and compare the efficacy and safety of pregabalin and duloxetine in patients with diabetic peripheral neuropathic pain. Materials and methods This prospective observational study was conducted at District Headquarter (DHQ) Hospital, Daggar, Buner district, Pakistan, from February 15 to July 15, 2022, after approval from the Institutional Research and Ethical Review Board. Confirmation of diabetic peripheral neuropathy was based on the history of diabetes mellitus and vibration perception threshold (VPT) using a biothesiometer. The cut-off was set at 15 volts. VPT of more than 15 volts was considered confirmatory for peripheral neuropathy. Patients were divided equally into two groups. Baseline visual analog scale (VAS) score was recorded for all patients. Tablet pregabalin 300 mg daily was administered for four weeks to one group, while tablet duloxetine in 60 mg strength daily was administered to the other group. VAS score after four-week treatment was recorded and compared. Adverse events experienced by the patient were also noted. Results A total of 86 patients were enrolled. The patient ages ranged from 30 to 80 years. Baseline characteristics, including mean age, mean BMI, and mean disease duration of duloxetine versus pregabalin group, were 50.30 ± 8.55 versus 48.20 ± 8.99 years, 23.47 ± 1.23 versus 23.10 ± 1.59 kg/m2 and 21.64 ±7.41 versus 20.04±6.37 months respectively. Duloxetine effectively controlled peripheral neuropathic pain in 81.4% of patients compared to pregabalin in 74.4% of patients. Severe drug-related adverse reactions were observed in 4.6% of patients with duloxetine compared to 0% with pregabalin. Conclusion Duloxetine and pregabalin effectively reduce diabetes-related peripheral neuropathic pain. However, duloxetine has slightly better outcomes than pregabalin. The safety profile of pregabalin is better than duloxetine.
