Sensor-Based Locomotion Data Mining for Supporting the Diagnosis of Neurodegenerative Disorders: A Survey

Locomotion characteristics and movement patterns are reliable indicators of neurodegenerative diseases (NDDs). This survey provides a systematic literature review of locomotion data mining systems for supporting NDD diagnosis. We discuss techniques for discovering low-level locomotion indicators, sensor data acquisition and processing methods, and NDD detection algorithms. The survey presents a comprehensive discussion on the main challenges for this active area, including the addressed diseases, locomotion data types, duration of monitoring, employed algorithms, and experimental validation strategies. We also identify prominent open challenges and research directions regarding ethics and privacy issues, technological and usability aspects, and availability of public benchmarks

Annual Report 2014

This document is a compendium of the research activity carried out by the IR3C during the year 2014, and describes its eleven Research Groups of Excellence, the two lines of research, the publications, funded projects, patents, and other activities within this period

Uncovering the Correlation between COVID-19 and Neurodegenerative Processes: Toward a New Approach Based on EEG Entropic Analysis

COVID-19 is an ongoing global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Although it primarily attacks the respiratory tract, inflammation can also affect the central nervous system (CNS), leading to chemo-sensory deficits such as anosmia and serious cognitive problems. Recent studies have shown a connection between COVID-19 and neurodegenerative diseases, particularly Alzheimer’s disease (AD). In fact, AD appears to exhibit neurological mechanisms of protein interactions similar to those that occur during COVID-19. Starting from these considerations, this perspective paper outlines a new approach based on the analysis of the complexity of brain signals to identify and quantify common features between COVID-19 and neurodegenerative disorders. Considering the relation between olfactory deficits, AD, and COVID-19, we present an experimental design involving olfactory tasks using multiscale fuzzy entropy (MFE) for electroencephalographic (EEG) signal analysis. Additionally, we present the open challenges and future perspectives. More specifically, the challenges are related to the lack of clinical standards regarding EEG signal entropy and public data that can be exploited in the experimental phase. Furthermore, the integration of EEG analysis with machine learning still requires further investigatio

The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control

Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions—including Parkinson’s disease, ataxia, and dementia— we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel ‘big data’ approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction

Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis : Exploratory Study

Background: Depression is a prevalent mental health challenge. Current depression assessment methods using self-reported and clinician-administered questionnaires have limitations. Instrumenting smartphones to passively and continuously collect moment-by-moment data sets to quantify human behaviors has the potential to augment current depression assessment methods for early diagnosis, scalable, and longitudinal monitoring of depression. Objective: The objective of this study was to investigate the feasibility of predicting depression with human behaviors quantified from smartphone data sets, and to identify behaviors that can influence depression. Methods: Smartphone data sets and self-reported 8-item Patient Health Questionnaire (PHQ-8) depression assessments were collected from 629 participants in an exploratory longitudinal study over an average of 22.1 days (SD 17.90; range 8-86). We quantified 22 regularity, entropy, and SD behavioral markers from the smartphone data. We explored the relationship between the behavioral features and depression using correlation and bivariate linear mixed models (LMMs). We leveraged 5 supervised machine learning (ML) algorithms with hyperparameter optimization, nested cross-validation, and imbalanced data handling to predict depression. Finally, with the permutation importance method, we identified influential behavioral markers in predicting depression. Results: Of the 629 participants from at least 56 countries, 69 (10.97%) were females, 546 (86.8%) were males, and 14 (2.2%) were nonbinary. Participants' age distribution is as follows: 73/629 (11.6%) were aged between 18 and 24, 204/629 (32.4%) were aged between 25 and 34, 156/629 (24.8%) were aged between 35 and 44, 166/629 (26.4%) were aged between 45 and 64, and 30/629 (4.8%) were aged 65 years and over. Of the 1374 PHQ-8 assessments, 1143 (83.19%) responses were nondepressed scores (PHQ-8 score = 10), as identified based on PHQ-8 cut-off. A significant positive Pearson correlation was found between screen status-normalized entropy and depression (r=0.14, P Conclusions: Our findings demonstrate that behavioral markers indicative of depression can be unobtrusively identified from smartphone sensors' data. Traditional assessment of depression can be augmented with behavioral markers from smartphones for depression diagnosis and monitoring.Peer reviewe

Sensor-based evaluation of Circadian motor behavior in people with Dementia. Development and validation of analysis strategies

[ITA]La demenza consiste nel deterioramento, spesso progressivo, dello stato cognitivo di un individuo. Chi è affetto da demenza, presenta alterazioni a livello cognitivo, comportamentale e motorio, ad esempio compiendo gesti ossessivi, ripetitivi, senza uno scopo preciso. La condizione dei pazienti affetti da demenza è valutata clinicamente tramite apposite scale e le informazioni relative al comportamento vengono raccolte intervistando chi se ne occupa, come familiari, il personale infermieristico o il medico curante. Spesso queste valutazioni si rivelano inaccurate, possono essere fortemente influenzate da considerazioni soggettive, e sono dispendiose in termini di tempo. Si ha quindi l'esigenza di disporre di metodiche oggettive per valutare il comportamento motorio dei pazienti e le sue alterazioni patologiche; i sensori inerziali indossabili potrebbero costituire una valida soluzione, per questo scopo. L'obiettivo principale della presente attività di tesi è stato definire e implementare un software per una valutazione oggettiva, basata su sensori, del pattern motorio circadiano, in pazienti affetti da demenza ricoverati in un'unità di terapia a lungo termine, che potrebbe evidenziare differenze nei sintomi della malattia che interessano il comportamento motorio, come descritto in ambito clinico. Lo scopo secondario è stato quello di verificare i cambiamenti motori pre- e post-intervento in un sottogruppo di pazienti, a seguito della somministrazione di un programma sperimentale di intervento basato su esercizi fisici. --------------- [ENG]Dementia involves deterioration, often progressive, of a person's cognitive status. Those who suffer from dementia, present alterations in cognitive and motor behavior, for example performing obsessive and repetitive gestures, without a purpose. The condition of patients suffering from dementia is clinically assessed by means of specific scales and information relating to the behavior are collected by interviewing caregivers, such as the family, nurses, or the doctor. Often it turns out that these are inaccurate assessments that may be heavily influenced by subjective evaluations and are costly in terms of time. Therefore, there is the need for objective methods to assess the patients' motor behavior and the pathological changes; wearable inertial sensors may represent a viable option, so this aim. The main objective of this thesis project was to define and implement a software for a sensor-based assessment of the circadian motor pattern in patients suffering from dementia, hospitalized in a long-term care unit, which could highlight differences in the disease symptoms affecting the motor behavior, as described in the clinical setting. The secondary objective was to verify pre- and post-intervention changes in the motor patterns of a subgroup of patients, following the administration of an experimental program of intervention based on physical exercises

Investigating the Differences between Neuropsychological and Physiological Markers in a PTSD-only Group Among Vietnam Veterans

Individuals diagnosed with posttraumatic stress disorder (PTSD) have demonstrated extinction-resistant symptoms and long-lasting impacts on the brain’s functional activity (Krystal et al., 2017; Stark et al., 2015). PTSD illustrates a strong relationship with cognitive functioning, and research has found lower parietal and hippocampal volume and deterioration in brain function and structure, resulting in executive dysfunction, attentional deficits, mood dysregulation, and memory difficulties (Krystal et al., 2017; Stark et al., 2015; Weiner et al. 2017). Sleep has been investigated to better understand its contribution to AD pathologies as well as its impact on PTSD treatment and prognosis. Although sleep’s functions are not widely understood, past studies have found that sleep aids in clearing waste, reconsolidating memories, and promoting neuronal connection (National Institute of Neurological Disorders and Stroke, 2006; Pace-Schott et al., 2015). Preliminary research by Weiner et al. (2017) has demonstrated worse global cognitive functioning, lower superior parietal volume, and lower amyloid positivity in people with PTSD, although preliminary results have not indicated PTSD as an increased risk factor for AD (Weiner et al., 2017). Therefore, further assessment of the differences between CSF amyloid, CSF ptau, and PTSD symptoms, specifically sleep, may expand current research on AD risk factors and biomarkers. A total of 179 participants were included in this study and were split into two groups: a PTSD group (n = 96) and a control group (n = 83). A MANCOVA analysis was conducted to assess if one or more mean differences between sleep quality index scores and AD pathology (i.e., CSF tau, CSF p-tau, CSF amyloid beta, CSF amyloid beta 40, and CSF amyloid beta 42/40 ratio) as well as in neuropsychological assessment results were present in a PTSD group compared to the control group (i.e., no PTSD or TBI group) among Vietnam veterans. A statistically significant MANCOVA effect was obtained, Pillai’s Trace = 0.28, ii F(6,61) = 3.89, p \u3c 0.002, when assessing differences between sleep quality and AD pathology. However, a statistically significant MANCOVA effect was not obtained, Pillai’s Trace = 0.16, F(11,38) = 0.68, p = 0.75, when assessing differences on neuropsychological assessments. Findings from this study suggest that sleep disturbance is prominent in individuals with PTSD and could be a significant contributor to AD pathologies. However, no difference was found between groups on neuropsychological assessments, illustrating that individuals with PTSD do not differ from those without PTSD in areas assessed by these measures

Characterization of the spontaneous EEG activity in the Alzheimer's disease continuum: from local activation to network organization

La presente Tesis Doctoral se presenta como un compendio de cuatro publicaciones indexadas en el Journal Citation Reports. El objetivo de estas publicaciones es la caracterización de los cambios neuronales subyacentes en las diferentes etapas de la enfermedad de Alzheimer (EA) y su etapa prodrómica, el deterioro cognitivo leve (DCL), siguiendo tres niveles de análisis: activación local, interacción entre pares de sensores, y organización de red. Los principales cambios encontrados a medida que progresa la enfermedad son: (i) una lentificación, y una pérdida de complejidad e irregularidad de la actividad EEG espontánea; (ii) una disminución significativa de la conectividad en bandas altas de frecuencia y un aumento en las bandas bajas; y (iii) una pérdida en la integración y la segregación de las redes neuronales. Estos hallazgos han proporcionado información adicional sobre las alteraciones cerebrales de la EA en sus diferentes etapas, útiles para comprender mejor sus mecanismos fisiopatológicos.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione