Alzheimer’s Disease (AD) is a neurodegenaritive disorder characterized by a progressive dementia, for which actually no cure is known. An early detection of patients affected by AD can be obtained by analyzing their electroencephalography (EEG) signals, which show a reduction of the complexity, a perturbation of the synchrony, and a slowing down of the rhythms. In this work, we apply a procedure that exploits feature extraction and classification techniques to EEG signals, whose aim is to distinguish patient affected by AD from the ones affected by Mild Cognitive Impairment (MCI) and healthy control (HC) samples. Specifically, we perform a time-frequency analysis by applying both the Fourier and Wavelet Transforms on 109 samples belonging to AD, MCI, and HC classes. The classification procedure is designed with the following steps: (i) preprocessing of EEG signals; (ii) feature extraction by means of the Discrete Fourier and Wavelet Transforms; and (iii) classification with tree-based supervised methods. By applying our procedure, we are able to extract reliable human-interpretable classification models that allow to automatically assign the patients into their belonging class. In particular, by exploiting a Wavelet feature extraction we achieve 83%, 92%, and 79% of accuracy when dealing with HC vs AD, HC vs MCI, and MCI vs AD classification problems, respectively. Finally, by comparing the classification performances with both feature extraction methods, we find out that Wavelets analysis outperforms Fourier. Hence, we suggest it in combination with supervised methods for automatic patients classification based on their EEG signals for aiding the medical diagnosis of dementia.

Electroencephalography (EEG) signal analysis is a fast, inexpensive, and accessible technique to detect the early stages of dementia, such as Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD). In the last years, EEG signal analysis has become an important topic of research to extract suitable biomarkers to determine the subject’s cognitive impairment. In this work, we propose a novel simple and efficient method able to extract features with a finite response filter (FIR) in the double time domain in order to discriminate among patients affected by AD, MCI, and healthy controls (HC). Notably, we compute the power intensity for each high- and low-frequency band, using their absolute differences to distinguish among the three classes of subjects by means of different supervised machine learning methods. We use EEG recordings from a cohort of 105 subjects (48 AD, 37 MCI, and 20 HC) referred for dementia to the IRCCS Centro Neurolesi “Bonino-Pulejo” of Messina, Italy. The findings show that this method reaches 97%, 95%, and 83% accuracy when considering binary classifications (HC vs. AD, HC vs. MCI, and MCI vs. AD) and an accuracy of 75% when dealing with the three classes (HC vs. AD vs. MCI). These results improve upon those obtained in previous studies and demonstrate the validity of our approach. Finally, the efficiency of the proposed method might allow its future development on embedded devices for low-cost real-time diagnosis.

Background: The purpose of this study is to evaluate the effects of a virtual reality training with BTS Nirvana (BTS-N) system in the cognitive and behavioral recovery in patients with Parkinson disease (PD). The BTS-N is a semi-immersive therapy system used for motor and cognitive rehabilitation of patients with neurological diseases, by creating virtual scenarios with which the patient may interact. Methods: We enrolled 20 patients with PD undergoing neurorehabilitation. All the patients were randomized into 2 groups: experimental group performing semi-immersive virtual reality training with BTS-N and control group undergoing traditional cognitive training. Each participant was evaluated before and immediately after the end of the training. Each cognitive training consisted of 3 sessions a week, each lasting 60 minutes, for 8 weeks, for a total of 24 sessions in each group. Results: Experimental group showed a greater improvement in cognitive functioning, with regard to executive and visuospatial abilities, as compared with the control group. Conclusion: Our findings suggest that rehabilitation through a new virtual reality instrument could be a valuable tool in improving cognitive and behavioral outcomes of patients with PD.

Introduction: Cranioplasty is a surgical technique applied for the reconstruction of the skullcap removed during decompressive craniectomy (DC). Cranioplasty improves rehabilitation from a motor and cognitive perspective. However, it may increase the possibility of postoperative complications, such as seizures and infections. Timing of cranioplasty is therefore crucial even though literature is controversial. In this study, we compared motor and cognitive effects of early cranioplasty after DC and assess the optimal timing to perform it.Methods: A literature research was conducted in PubMed, Web of Science, and Cochrane Library databases. We selected studies including at least one of the following test: Mini‐Mental State Examination, Rey Auditory Verbal Learning Test immediate and 30‐min delayed recall, Digit Span Test, Glasgow Coma Scale, Glasgow Outcome Scale, Coma Recovery Scale‐Revised, Level of Cognitive Functioning Scale, Functional Independence Measure, and Barthel Index.Results: Six articles and two systematic reviews were included in the present study. Analysis of changes in pre‐ and postcranioplasty scores showed that an early procedure (within 90 days from decompressive craniectomy) is more effective in improving motor functions (standardized mean difference [SMD] = 0.51 [0.05; 0.97], p‐value = 0.03), whereas an early procedure did not significantly improve neither MMSE score (SMD = 0.06 [−0.49; 0.61], p‐value = 0.83) nor memory functions (SMD = −0.63 [−0.97; −0.28], p‐value < 0.001). No statistical significance emerged when we compared studies according to the timing from DC.Conclusions: It is believed that cranioplasty performed from 3 to 6 months after DC may significantly improve both motor and cognitive recovery.