Entropy and fractal analysis of brain-related neurophysiological signals in Alzheimer's and Parkinson's disease.

Brain-related neuronal recordings, such as local field potential, electroencephalogram and magnetoencephalogram, offer the opportunity to study the complexity of the human brain at different spatial and temporal scales. The complex properties of neuronal signals are intrinsically related to the concept of 'scale-free' behavior and irregular dynamic, which cannot be fully described through standard linear methods, but can be measured by nonlinear indexes. A remarkable application of these analysis methods on electrophysiological recordings is the deep comprehension of the pathophysiology of neurodegenerative diseases, that has been shown to be associated to changes in brain activity complexity. In particular, a decrease of global complexity has been associated to Alzheimer's disease, while a local increase of brain signals complexity characterizes Parkinson's disease. Despite the recent proliferation of studies using fractal and entropy-based analysis, the application of these techniques is still far from clinical practice, due to the lack of an agreement about their correct estimation and a conclusive and shared interpretation. Along with the aim of helping towards the realization of a multidisciplinary audience to approach nonlinear methods based on the concepts of fractality and irregularity, this survey describes the implementation and proper employment of the mostly known and applied indexes in the context of Alzheimer's and Parkinson's diseases

A framework for the comparative assessment of neuronal spike sorting algorithms towards more accurate off-line and on-line microelectrode arrays data analysis

Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting "building blocks" into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis

A telerehabilitation platform for cognitive, physical and behavioural rehabilitation in elderly patients affected by dementia

Dementia is one of the main causes of disability in elderly people and its treatment becomes, year after year, an increasingly compelling priority for the public health system. In the last years, home assistance and telemedicine have paved the way to decrease the treatments’ costs and to improve the patients and caregivers quality of life quality. In this framework, the aim of ABILITY project is to design, develop and validate an integrated platform of services aimed at supporting and enhancing the rehabilitation process for patients with dementia at their homes. ABILITY platform allows the clinician to assign rehabilitation plans with a strong compliance monitoring, enabled by the technological solutions integrated, and the holistic approach to rehabilitation, as the plan includes physical, cognitive and behavioral therapies/exercises. The ABILITY platform will be assessed through a set of validation activities, involving a small group of pilot patients, and a Randomized Control Trial. In conclusion, the ABILITY project generates a series of assistive services inside a modular and flexible platform, adaptable to the single patient and his/her needs, increasing the treatment efficiency and efficacy with respect to the state of the art

EEG indices correlate with sustained attention performance in patients affected by diffuse axonal injury

The aim of this study is to assess the ability of EEG-based indices in providing relevant information about cognitive engagement level during the execution of a clinical sustained attention (SA) test in healthy volunteers and DAI (diffused axonal injury)-affected patients. We computed three continuous power-based engagement indices (Pβ/Pα, 1/Pα, and Pβ/ (Pα+ Pθ)) from EEG recordings in a control group (n = 7) and seven DAI-affected patients executing a 10-min Connersâ\u80\u99 â\u80\u9cnot-Xâ\u80\u9d continuous performance test (CPT). A correlation analysis was performed in order to investigate the existence of relations between the EEG metrics and behavioral parameters in both the populations. Pβ/Pαand 1/Pαindices were found to be correlated with reaction times in both groups while Pβ/ (Pα+ Pθ) and Pβ/Pαalso correlated with the errors rate for DAI patients. In line with previous studies, time course fluctuations revealed a first strong decrease of attention after 2 min from the beginning of the test and a final fading at the end. Our results provide evidence that EEG-derived indices extraction and evaluation during SA tasks are helpful in the assessment of attention level in healthy subjects and DAI patients, offering motivations for including EEG monitoring in cognitive rehabilitation practice. [Figure not available: see fulltext.

Bicoherence Interpretation in EEG Requires Signal to Noise Ratio Quantification: An Application to Sensorimotor Rhythms

In the electroencephalogram (EEG) the quadratic phase coupling (QPC) phenomenon indicates the presence of non-linear interactions among brain rhythms that could affect the interpretation of their physiological meaning. We propose the use of the bicoherence as a QPC quantification method to understand the nature of brain rhythm interplay

Information Retrieval from Photoplethysmographic Sensors: A Comprehensive Comparison of Practical Interpolation and Breath-Extraction Techniques at Different Sampling Rates

The increasingly widespread diffusion of wearable devices makes possible the continuous monitoring of vital signs, such as heart rate (HR), heart rate variability (HRV), and breath signal. However, these devices usually do not record the “gold-standard” signals, namely the electrocardiography (ECG) and respiratory activity, but a single photoplethysmographic (PPG) signal, which can be exploited to estimate HR and respiratory activity. In addition, these devices employ low sampling rates to limit power consumption. Hence, proper methods should be adopted to compensate for the resulting increased discretization error, while diverse breath-extraction algorithms may be differently sensitive to PPG sampling rate. Here, we assessed the efficacy of parabola interpolation, cubic-spline, and linear regression methods to improve the accuracy of the inter-beat intervals (IBIs) extracted from PPG sampled at decreasing rates from 64 to 8 Hz. PPG-derived IBIs and HRV indices were compared with those extracted from a standard ECG. In addition, breath signals extracted from PPG using three different techniques were compared with the gold-standard signal from a thoracic belt. Signals were recorded from eight healthy volunteers during an experimental protocol comprising sitting and standing postures and a controlled respiration task. Parabola and cubic-spline interpolation significantly increased IBIs accuracy at 32, 16, and 8 Hz sampling rates. Concerning breath signal extraction, the method holding higher accuracy was based on PPG bandpass filtering. Our results support the efficacy of parabola and spline interpolations to improve the accuracy of the IBIs obtained from low-sampling rate PPG signals, and also indicate a robust method for breath signal extraction

EEG-based index for engagement level monitoring during sustained attention

This paper investigates the relation between mental engagement level and sustained attention in 9 healthy adults performing a Conners' "not-X" continuous performance test (CPT), while their electroencephalographic (EEG) activity was simultaneously acquired. Spectral powers were estimated and extracted in the classical EEG frequency bands. The engagement index (β/α) was calculated employing four different cortical montages suggested by the literature. Results show the efficacy of the estimated measures in detecting changes in mental state and its correlation with subject reaction times throughout the test. Moreover, the influence of the recording sites was proved underling the role of frontal cortex in maintaining a constant sustained attention level

Higher order spectral analysis of scalp EEG activity reveals non-linear behavior during rhythmic visual stimulation

Objective. Flickering visual stimulation is known to evoke rhythmic oscillations in the electroencephalographic (EEG) activity, called steady-state visually evoked potentials (SSVEP). The presence of harmonic components in the EEG signals during SSVEP suggests the non-linearity of the visual-system response to rhythmic stimulation, but the nature of this behavior has not been deeply understood. The aim of this study is the quantitative evaluation and characterization of this non-linear phenomenon and its interference with the physiological alpha rhythm by means of spectral and higher order spectral analysis. Approach. EEG signals were acquired in a group of 12 healthy subjects during a pattern-reversal stimulation protocol at three different driving frequencies (7.5 Hz, 15 Hz and 24 Hz). Spectral power values were estimated, after Laplacian spatial filtering, to quantitatively evaluate the changes in the power of the individual alpha and stimulation frequencies related harmonic components. Bicoherence measure were employed to assess the presence of quadratic phase coupling (QPC) at each channel location. Main results. Our analysis confirmed a strong non-linear response to the rhythmic stimulus principally over the parieto-occipital channel locations and a simultaneous significant alpha power suppression during 7.5 Hz and 15 Hz stimulation. A prominent sub-harmonic component characterized the resonance behavior of the 24 Hz stimulation. Significance. The findings presented suggest that bicoherence is a useful tool for the identification of QPC interactions between stimulus-related frequency components within the same signal and the characterization of the non-linearity of SSVEP-induced harmonics generation. In addition, the applied methodology demonstrates the presence of coupled EEG rhythms (harmonics of the main oscillation) both in resting condition and during stimulation, with different characteristics in the distinct brain areas

Multiscale Functional Clustering Reveals Frequency Dependent Brain Organization in Type II Focal Cortical Dysplasia with Sleep Hypermotor Epilepsy

A multiscale functional clustering approach is proposed to investigate the organization of the epileptic networks (EN) during different sleep stages and in relation with the occurrence of seizures