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Brain Sciences
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Biomedical Signals Analysis for Neurorehabilitation and Clinical Decision Support in...
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Biomedical Signals Analysis for Neurorehabilitation and Clinical Decision Support in Neurological Disorders

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurotechnology and Neuroimaging".

Deadline for manuscript submissions: closed (25 November 2022) | Viewed by 6821

Special Issue Editor

Prof. Dr. Lorenzo Priano

E-Mail Website
Guest Editor
1. Department of Neurosciences "Rita Levi Montalcini", University of Turin, Turin, Italy
2. Istituto Auxologico Italiano, IRCCS, U.O. di Neurologia e Neuroriabilitazione, Ospedale S.Giuseppe, Piancavallo (VB), Italy
Interests: neurophysiology; electroencephalography; polysomnography; electrophysiological signal analysis; gait analysis; bioengineering techniques applied to automated movement analysis; sleep medicine; neurodegenerative diseases; neurorehabilitation

Special Issue Information

Dear Colleagues,

In recent years, advances in computer analysis, signal processing, machine learning, and big data analytics techniques have provided new opportunities to develop solutions for the automated analysis of biological signals. All these methodologies can be integrated into intelligent systems by fusing information from different devices, such as wireless sensors, eye-trackers, cameras, wearables, IoT devices, and brain–computer interfaces (BCI). Potential applications are relevant for neurological disorders with disabilities as it has become possible to achieve automated and quantitative evaluations in laboratory or at home, in unsupervised and ecological settings, both for diagnostic and rehabilitation purposes.

This Special Issue focuses on neurological disorders with motor or cognitive impairments and intends to collect contributions of the most recent and innovative research in the context of computer analysis of biosignals that are able to provide affordable information for diagnosis and clinical decision support or may permit the remote monitoring of patients’ conditions during neurorehabilitation protocols.

Potential areas of interest include but are not limited to:

  • Automatic processing, feature extraction, and classification of bioelectrical signals (ECG, EMG, EEG, etc.) for diagnostic, monitoring, or rehabilitation purposes;
  • Sensors or video-based movement analysis;
  • Assessment of motor impairments based on EEG/EMG/movement data;
  • Remote neurorehabilitation based on automated assessments of disabilities;
  • Quantitative evaluations on rehabilitation effectiveness;
  • Pattern recognition, machine learning, and artificial intelligence techniques for telemedicine;
  • Multi-parametric real-time signal processing;
  • Application of BCI systems in neurorehabilitation.

In particular, we encourage the submission of clinically significant studies that undertake a multidisciplinary approach to detect or predict parameters related to biosignals in neurological disorders.

Prof. Dr. Lorenzo Priano
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biosignal processing
  • feature extraction
  • multimodal BCI
  • machine learning
  • remote monitoring
  • wireless sensors
  • video-based movement analysis
  • neurorehabilitation
  • tele-medicine
  • neurological disorders

Published Papers (3 papers)

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Research

15 pages, 2191 KiB  
Article
Application of Soft-Clustering to Assess Consciousness in a CLIS Patient
by Sophie Adama and Martin Bogdan
Brain Sci. 2023, 13(1), 65; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci13010065 - 29 Dec 2022
Cited by 3 | Viewed by 1040
Abstract
Completely locked-in (CLIS) patients are characterized by sufficiently intact cognitive functions, but a complete paralysis that prevents them to interact with their surroundings. On one hand, studies have shown that the ability to communicate plays an important part in these patients’ quality of [...] Read more.
Completely locked-in (CLIS) patients are characterized by sufficiently intact cognitive functions, but a complete paralysis that prevents them to interact with their surroundings. On one hand, studies have shown that the ability to communicate plays an important part in these patients’ quality of life and prognosis. On the other hand, brain-computer interfaces (BCIs) provide a means for them to communicate using their brain signals. However, one major problem for such patients is the difficulty to determine if they are conscious or not at a specific time. This work aims to combine different sets of features consisting of spectral, complexity and connectivity measures, to increase the probability of correctly estimating CLIS patients’ consciousness levels. The proposed approach was tested on data from one CLIS patient, which is particular in the sense that the experimenter was able to point out one time frame Δt during which he was undoubtedly conscious. Results showed that the method presented in this paper was able to detect increases and decreases of the patient’s consciousness levels. More specifically, increases were observed during this Δt, corroborating the assertion of the experimenter reporting that the patient was definitely conscious then. Assessing the patients’ consciousness is intended as a step prior attempting to communicate with them, in order to maximize the efficiency of BCI-based communication systems. Full article
(This article belongs to the Special Issue Biomedical Signals Analysis for Neurorehabilitation and Clinical Decision Support in Neurological Disorders)
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15 pages, 1760 KiB  
Article
Assessment of Diaphragm in Hemiplegic Patients after Stroke with Ultrasound and Its Correlation of Extremity Motor and Balance Function
by Xiaoman Liu, Qingming Qu, Panmo Deng, Yuehua Zhao, Chenghong Liu, Conghui Fu and Jie Jia
Brain Sci. 2022, 12(7), 882; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12070882 - 04 Jul 2022
Cited by 4 | Viewed by 2584
Abstract
Background: A variety of functional disorders can be caused after stroke, among which impairment of respiratory function is a frequent and serious complication of stroke patients. The aim of this study was to examine diaphragmatic function after stroke by diaphragm ultrasonography and then [...] Read more.
Background: A variety of functional disorders can be caused after stroke, among which impairment of respiratory function is a frequent and serious complication of stroke patients. The aim of this study was to examine diaphragmatic function after stroke by diaphragm ultrasonography and then to apply to explore its correlation with extremity motor function and balance function of the hemiplegia patients. Methods: This cross-sectional observational study recruited 48 hemiplegic patients after stroke and 20 matched healthy participants. The data of demographic and ultrasonographic assessment of all healthy subjects were recorded, and 45 patients successfully underwent baseline data assessment in the first 48 h following admission, including post-stroke duration, stroke type, hemiplegia side, pipeline feeding, pulmonary infection, ultrasonographic assessment for diaphragm, Fugl–Meyer Motor Function Assessment Scale (FMA Scale), and Berg Balance Scale assessment. Ultrasonographic assessment parameters included diaphragm mobility under quiet and deep breathing, diaphragm thickness at end-inspiratory and end-expiratory, and calculated thickening fraction of the diaphragm. The aim was to analyze the diaphragm function of hemiplegic patients after stroke and to explore its correlation with extremity motor function and balance function. Results: The incidence of diaphragmatic dysfunction under deep breath was 46.67% in 45 hemiplegia patients after stroke at the convalescent phase. The paralyzed hemidiaphragm had major impairments, and the mobility of the hemiplegic diaphragm was significantly reduced during deep breathing (p < 0.05). Moreover, the thickness fraction of hemiplegic side was extremely diminished when contrasted with the healthy control and non-hemiplegic side (p < 0.05). We respectively compared the diaphragm mobility under deep breath on the hemiplegic and non-hemiplegic side of patients with left and right hemiplegia and found there was no significant difference between the hemiplegic side of right and left hemiplegia (p > 0.05), but the non-hemiplegic side of right hemiplegia was significantly weaker than that of left hemiplegia patients (p < 0.05). The diaphragm mobility of stroke patients under quiet breath was positively correlated with age and FMA Scale score (R2 = 0.296, p < 0.05), and significant positive correlations were found between the diaphragm mobility under deep breath and Berg Balance Scale score (R2 = 0.11, p < 0.05), diaphragm thickness at end-inspiratory and FMA Scale score (R2 = 0.152, p < 0.05), and end-expiratory thickness and FMA Scale score (R2 = 0.204, p < 0.05). Conclusions: The mobility and thickness fraction of the hemiplegic diaphragm after stroke by diaphragm ultrasonography were significantly reduced during deep breathing. Diaphragm mobility on bilateral sides of the right hemiplegia patients were reduced during deep breathing. Moreover, the hemiplegic diaphragmatic function was positively correlated with extremity motor and balance function of the hemiplegia patients. Full article
(This article belongs to the Special Issue Biomedical Signals Analysis for Neurorehabilitation and Clinical Decision Support in Neurological Disorders)
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9 pages, 285 KiB  
Article
Brain Asymmetry and Its Effects on Gait Strategies in Hemiplegic Patients: New Rehabilitative Conceptions
by Luca Vismara, Veronica Cimolin, Francesca Buffone, Matteo Bigoni, Daniela Clerici, Serena Cerfoglio, Manuela Galli and Alessandro Mauro
Brain Sci. 2022, 12(6), 798; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12060798 - 18 Jun 2022
Cited by 4 | Viewed by 2375
Abstract
Brain asymmetry is connected with motor performance, suggesting that hemiparetic patients have different gait patterns depending on the side of the lesion. This retrospective cohort study aims to further investigate the difference between right and left hemiplegia in order to assess whether the [...] Read more.
Brain asymmetry is connected with motor performance, suggesting that hemiparetic patients have different gait patterns depending on the side of the lesion. This retrospective cohort study aims to further investigate the difference between right and left hemiplegia in order to assess whether the injured side can influence the patient’s clinical characteristics concerning gait, thus providing insights for new personalized rehabilitation strategies. The data from 33 stroke patients (17 with left and 16 with right hemiplegia) were retrospectively compared with each other and with a control group composed of 20 unaffected age-matched individuals. The 3D gait analysis was used to assess kinematic data and spatio-temporal parameters. Compared to left hemiplegic patients, right hemiplegic patients showed worse spatio-temporal parameters (p < 0.05) and better kinematic parameters (p < 0.05). Both pathological groups were characterized by abnormal gait parameters in comparison with the control group (p < 0.05). These findings show an association between the side of the lesion—right or left—and the different stroke patients’ gait patterns: left hemiplegic patients show better spatio-temporal parameters, whereas right hemiplegic patients show better segmentary motor performances. Therefore, further studies may develop and assess new personalized rehabilitation strategies considering the injured hemisphere and brain asymmetry. Full article
(This article belongs to the Special Issue Biomedical Signals Analysis for Neurorehabilitation and Clinical Decision Support in Neurological Disorders)
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