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Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 51192

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Alfonso Mastropietro

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Guest Editor
Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato, Consiglio Nazionale delle Ricerche, Via Alfonso Corti 12, 20133 Milano, Italy
Interests: MRI; EEG; signal processing; medical image analysis; diffusion MRI; advanced MRI approaches; quantitative MRI; mental workload; central nervous system; stroke; skeletal muscle; rehabilitation
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandro Scano

E-Mail Website
Guest Editor
Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato, Consiglio Nazionale delle Ricerche, 20133 Milano, Italy
Interests: EMG; muscle synergies; motor control; neurological rehabilitation; signal analysis; kinematics; biomechanics; skeletal muscle; motion analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Massimo W. Rivolta

E-Mail Website
Guest Editor
Dipartimento di Informatica, Università degli Studi di Milano, 20133 Milan, Italy
Interests: signal processing; feature extraction; computational intelligence and computerized simulations for ECG signal processing and characterization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical sensors are key components in various medical instruments and equipment and are valuable tools that provide functional information to better understand the underlying mechanism of relevant biological processes and interventions. The recent development of high-density systems, characterized by a high number of sensors arranged in specific configurations (matrices, arrays, wearable, etc.) has boosted the use of biomedical sensors for functional evaluation, including electrophysiological activity, metabolic response of organs and tissues, motor control analysis, adding meaningful spatial information. Functional mapping is becoming a popular approach for most of the common biomedical techniques (EEG, EMG, ECG, NIRS, MEG, etc.), and it improves the comprehension of complex biological behaviors where the spatial localization of the sensing methodology is crucial. Functional mapping that uses biomedical sensors can be helpful in different fields (neuroscience, neuromuscular physiology, rehabilitation, cardiology, etc.) either for diagnostic purpose or for the assessment of therapeutic interventions effectiveness.

The aim of this Special Issue is to collect papers describing cutting-edge techniques, methods and applications of biomedical sensors, as well as specific algorithms for data processing, which are able to provide functional information associated with the underlying spatial localization.

Both review articles and original research papers are solicited. There is particular interest in papers concerning medical applications where different biomedical devices or systems are used in a complementary approach coupled with clinical assessments.

 

Manuscript Submission Information

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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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • High-density electroencephalography (EEG);
  • Multi-channel electromiography (EMG);
  • Kinematics and motion analysis;
  • Functional near infrared spectroscopy (fNIRS);
  • Electrocardiography (ECG);
  • Muscle synergies;
  • Wearable sensors;
  • Image and signal processing;
  • Functional and effective connectivity;
  • Applied machine learning.

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Published Papers (15 papers)

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Editorial

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4 pages, 179 KiB  
Editorial
Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications
by Alfonso Mastropietro, Massimo Walter Rivolta and Alessandro Scano
Sensors 2023, 23(16), 7063; https://doi.org/10.3390/s23167063 - 10 Aug 2023
Viewed by 1284
Abstract
The rapid advancement of biomedical sensor technology has revolutionized the field of functional mapping in medicine, offering novel and powerful tools for diagnosis, clinical assessment, and rehabilitation [...] Full article
(This article belongs to the Special Issue Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications)

Research

Jump to: Editorial, Other

13 pages, 582 KiB  
Article
COVID-19 Detection Using Photoplethysmography and Neural Networks
by Sara Lombardi, Piergiorgio Francia, Rossella Deodati, Italo Calamai, Marco Luchini, Rosario Spina and Leonardo Bocchi
Sensors 2023, 23(5), 2561; https://doi.org/10.3390/s23052561 - 25 Feb 2023
Cited by 3 | Viewed by 1626
Abstract
The early identification of microvascular changes in patients with Coronavirus Disease 2019 (COVID-19) may offer an important clinical opportunity. This study aimed to define a method, based on deep learning approaches, for the identification of COVID-19 patients from the analysis of the raw [...] Read more.
The early identification of microvascular changes in patients with Coronavirus Disease 2019 (COVID-19) may offer an important clinical opportunity. This study aimed to define a method, based on deep learning approaches, for the identification of COVID-19 patients from the analysis of the raw PPG signal, acquired with a pulse oximeter. To develop the method, we acquired the PPG signal of 93 COVID-19 patients and 90 healthy control subjects using a finger pulse oximeter. To select the good quality portions of the signal, we developed a template-matching method that excludes samples corrupted by noise or motion artefacts. These samples were subsequently used to develop a custom convolutional neural network model. The model accepts PPG signal segments as input and performs a binary classification between COVID-19 and control samples. The proposed model showed good performance in identifying COVID-19 patients, achieving 83.86% accuracy and 84.30% sensitivity (hold-out validation) on test data. The obtained results indicate that photoplethysmography may be a useful tool for microcirculation assessment and early recognition of SARS-CoV-2-induced microvascular changes. In addition, such a noninvasive and low-cost method is well suited for the development of a user-friendly system, potentially applicable even in resource-limited healthcare settings. Full article
(This article belongs to the Special Issue Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications)
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15 pages, 3510 KiB  
Article
Toward Early and Objective Hand Osteoarthritis Detection by Using EMG during Grasps
by Néstor J. Jarque-Bou, Verónica Gracia-Ibáñez, Alba Roda-Sales, Vicente Bayarri-Porcar, Joaquín L. Sancho-Bru and Margarita Vergara
Sensors 2023, 23(5), 2413; https://doi.org/10.3390/s23052413 - 22 Feb 2023
Cited by 5 | Viewed by 2340
Abstract
The early and objective detection of hand pathologies is a field that still requires more research. One of the main signs of hand osteoarthritis (HOA) is joint degeneration, which causes loss of strength, among other symptoms. HOA is usually diagnosed with imaging and [...] Read more.
The early and objective detection of hand pathologies is a field that still requires more research. One of the main signs of hand osteoarthritis (HOA) is joint degeneration, which causes loss of strength, among other symptoms. HOA is usually diagnosed with imaging and radiography, but the disease is in an advanced stage when HOA is observable by these methods. Some authors suggest that muscle tissue changes seem to occur before joint degeneration. We propose recording muscular activity to look for indicators of these changes that might help in early diagnosis. Muscular activity is often measured using electromyography (EMG), which consists of recording electrical muscle activity. The aim of this study is to study whether different EMG characteristics (zero crossing, wavelength, mean absolute value, muscle activity) via collection of forearm and hand EMG signals are feasible alternatives to the existing methods of detecting HOA patients’ hand function. We used surface EMG to measure the electrical activity of the dominant hand’s forearm muscles with 22 healthy subjects and 20 HOA patients performing maximum force during six representative grasp types (the most commonly used in ADLs). The EMG characteristics were used to identify discriminant functions to detect HOA. The results show that forearm muscles are significantly affected by HOA in EMG terms, with very high success rates (between 93.3% and 100%) in the discriminant analyses, which suggest that EMG can be used as a preliminary step towards confirmation with current HOA diagnostic techniques. Digit flexors during cylindrical grasp, thumb muscles during oblique palmar grasp, and wrist extensors and radial deviators during the intermediate power–precision grasp are good candidates to help detect HOA. Full article
(This article belongs to the Special Issue Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications)
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16 pages, 5686 KiB  
Article
Technology Acceptance Model for Exoskeletons for Rehabilitation of the Upper Limbs from Therapists’ Perspectives
by Beatrice Luciani, Francesco Braghin, Alessandra Laura Giulia Pedrocchi and Marta Gandolla
Sensors 2023, 23(3), 1721; https://doi.org/10.3390/s23031721 - 3 Feb 2023
Cited by 12 | Viewed by 3728
Abstract
Over the last few years, exoskeletons have been demonstrated to be useful tools for supporting the execution of neuromotor rehabilitation sessions. However, they are still not very present in hospitals. Therapists tend to be wary of this type of technology, thus reducing its [...] Read more.
Over the last few years, exoskeletons have been demonstrated to be useful tools for supporting the execution of neuromotor rehabilitation sessions. However, they are still not very present in hospitals. Therapists tend to be wary of this type of technology, thus reducing its acceptability and, therefore, its everyday use in clinical practice. The work presented in this paper investigates a novel point of view that is different from that of patients, which is normally what is considered for similar analyses. Through the realization of a technology acceptance model, we investigate the factors that influence the acceptability level of exoskeletons for rehabilitation of the upper limbs from therapists’ perspectives. We analyzed the data collected from a pool of 55 physiotherapists and physiatrists through the distribution of a questionnaire. Pearson’s correlation and multiple linear regression were used for the analysis. The relations between the variables of interest were also investigated depending on participants’ age and experience with technology. The model built from these data demonstrated that the perceived usefulness of a robotic system, in terms of time and effort savings, was the first factor influencing therapists’ willingness to use it. Physiotherapists’ perception of the importance of interacting with an exoskeleton when carrying out an enhanced therapy session increased if survey participants already had experience with this type of rehabilitation technology, while their distrust and the consideration of others’ opinions decreased. The conclusions drawn from our analyses show that we need to invest in making this technology better known to the public—in terms of education and training—if we aim to make exoskeletons genuinely accepted and usable by therapists. In addition, integrating exoskeletons with multi-sensor feedback systems would help provide comprehensive information about the patients’ condition and progress. This can help overcome the gap that a robot creates between a therapist and the patient’s human body, reducing the fear that specialists have of this technology, and this can demonstrate exoskeletons’ utility, thus increasing their perceived level of usefulness. Full article
(This article belongs to the Special Issue Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications)
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23 pages, 3561 KiB  
Article
Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors
by Pekka Siirtola, Satu Tamminen, Gunjan Chandra, Anusha Ihalapathirana and Juha Röning
Sensors 2023, 23(3), 1598; https://doi.org/10.3390/s23031598 - 1 Feb 2023
Cited by 10 | Viewed by 6373
Abstract
This study aims to predict emotions using biosignals collected via wrist-worn sensor and evaluate the performance of different prediction models. Two dimensions of emotions were considered: valence and arousal. The data collected by the sensor were used in conjunction with target values obtained [...] Read more.
This study aims to predict emotions using biosignals collected via wrist-worn sensor and evaluate the performance of different prediction models. Two dimensions of emotions were considered: valence and arousal. The data collected by the sensor were used in conjunction with target values obtained from questionnaires. A variety of classification and regression models were compared, including Long Short-Term Memory (LSTM) models. Additionally, the effects of different normalization methods and the impact of using different sensors were studied, and the way in which the results differed between the study subjects was analyzed. The results revealed that regression models generally performed better than classification models, with LSTM regression models achieving the best results. The normalization method called baseline reduction was found to be the most effective, and when used with an LSTM-based regression model it achieved high accuracy in detecting valence (mean square error = 0.43 and R2-score = 0.71) and arousal (mean square error = 0.59 and R2-score = 0.81). Moreover, it was found that even if all biosignals were not used in the training phase, reliable models could be obtained; in fact, for certain study subjects the best results were obtained using only a few of the sensors. Full article
(This article belongs to the Special Issue Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications)
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14 pages, 1873 KiB  
Article
Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines
by Alfonso Mastropietro, Ileana Pirovano, Alessio Marciano, Simone Porcelli and Giovanna Rizzo
Sensors 2023, 23(3), 1367; https://doi.org/10.3390/s23031367 - 26 Jan 2023
Cited by 6 | Viewed by 2455
Abstract
Background and Objective: Mental workload (MWL) is a relevant construct involved in all cognitively demanding activities, and its assessment is an important goal in many research fields. This paper aims at evaluating the reproducibility and sensitivity of MWL assessment from EEG signals considering [...] Read more.
Background and Objective: Mental workload (MWL) is a relevant construct involved in all cognitively demanding activities, and its assessment is an important goal in many research fields. This paper aims at evaluating the reproducibility and sensitivity of MWL assessment from EEG signals considering the effects of different electrode configurations and pre-processing pipelines (PPPs). Methods: Thirteen young healthy adults were enrolled and were asked to perform 45 min of Simon’s task to elicit a cognitive demand. EEG data were collected using a 32-channel system with different electrode configurations (fronto-parietal; Fz and Pz; Cz) and analyzed using different PPPs, from the simplest bandpass filtering to the combination of filtering, Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). The reproducibility of MWL indexes estimation and the sensitivity of their changes were assessed using Intraclass Correlation Coefficient and statistical analysis. Results: MWL assessed with different PPPs showed reliability ranging from good to very good in most of the electrode configurations (average consistency > 0.87 and average absolute agreement > 0.92). Larger fronto-parietal electrode configurations, albeit being more affected by the choice of PPPs, provide better sensitivity in the detection of MWL changes if compared to a single-electrode configuration (18 vs. 10 statistically significant differences detected, respectively). Conclusions: The most complex PPPs have been proven to ensure good reliability (>0.90) and sensitivity in all experimental conditions. In conclusion, we propose to use at least a two-electrode configuration (Fz and Pz) and complex PPPs including at least the ICA algorithm (even better including ASR) to mitigate artifacts and obtain reliable and sensitive MWL assessment during cognitive tasks. Full article
(This article belongs to the Special Issue Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications)
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13 pages, 3662 KiB  
Article
Reliable Fast (20 Hz) Acquisition Rate by a TD fNIRS Device: Brain Resting-State Oscillation Studies
by Rebecca Re, Ileana Pirovano, Davide Contini, Caterina Amendola, Letizia Contini, Lorenzo Frabasile, Pietro Levoni, Alessandro Torricelli and Lorenzo Spinelli
Sensors 2023, 23(1), 196; https://doi.org/10.3390/s23010196 - 24 Dec 2022
Cited by 10 | Viewed by 2622
Abstract
A high power setup for multichannel time-domain (TD) functional near infrared spectroscopy (fNIRS) measurements with high efficiency detection system was developed. It was fully characterized based on international performance assessment protocols for diffuse optics instruments, showing an improvement of the signal-to-noise ratio (SNR) [...] Read more.
A high power setup for multichannel time-domain (TD) functional near infrared spectroscopy (fNIRS) measurements with high efficiency detection system was developed. It was fully characterized based on international performance assessment protocols for diffuse optics instruments, showing an improvement of the signal-to-noise ratio (SNR) with respect to previous analogue devices, and allowing acquisition of signals with sampling rate up to 20 Hz and source-detector distance up to 5 cm. A resting-state measurement on the motor cortex of a healthy volunteer was performed with an acquisition rate of 20 Hz at a 4 cm source-detector distance. The power spectrum for the cortical oxy- and deoxyhemoglobin is also provided. Full article
(This article belongs to the Special Issue Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications)
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12 pages, 2884 KiB  
Article
Towards a Practical Implementation of a Single-Beam All-Optical Non-Zero-Field Magnetic Sensor for Magnetoencephalographic Complexes
by Mikhail Petrenko and Anton Vershovskii
Sensors 2022, 22(24), 9862; https://doi.org/10.3390/s22249862 - 15 Dec 2022
Cited by 4 | Viewed by 1401
Abstract
We present a single-beam all-optical two-channel magnetic sensor scheme developed for biological applications such as non-zero-field magnetoencephalography and magnetocardiography. The pumping, excitation and detection of magnetic resonance in two cells are performed using a single laser beam with time-modulated linear polarization: the linear [...] Read more.
We present a single-beam all-optical two-channel magnetic sensor scheme developed for biological applications such as non-zero-field magnetoencephalography and magnetocardiography. The pumping, excitation and detection of magnetic resonance in two cells are performed using a single laser beam with time-modulated linear polarization: the linear polarization of the beam switches to orthogonal every half-cycle of the Larmor frequency. Light with such characteristics can be transmitted over a single-mode polarization-maintaining fiber without any loss in the quality of the polarization characteristics. We also present an algorithm for calculating optical elements in a sensor scheme, the results of measuring the parametric dependences of magnetic resonance in cells, and the results of direct testing of a sensor in a magnetic shield. We demonstrate sensitivity at the level of 20 fT/√Hz in one sensor channel in the frequency range of 80–200 Hz. Full article
(This article belongs to the Special Issue Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications)
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21 pages, 19007 KiB  
Article
Multi-Scale Evaluation of Sleep Quality Based on Motion Signal from Unobtrusive Device
by Davide Coluzzi, Giuseppe Baselli, Anna Maria Bianchi, Guillermina Guerrero-Mora, Juha M. Kortelainen, Mirja L. Tenhunen and Martin O. Mendez
Sensors 2022, 22(14), 5295; https://doi.org/10.3390/s22145295 - 15 Jul 2022
Cited by 3 | Viewed by 3013
Abstract
Sleep disorders are a growing threat nowadays as they are linked to neurological, cardiovascular and metabolic diseases. The gold standard methodology for sleep study is polysomnography (PSG), an intrusive and onerous technique that can disrupt normal routines. In this perspective, m-Health technologies offer [...] Read more.
Sleep disorders are a growing threat nowadays as they are linked to neurological, cardiovascular and metabolic diseases. The gold standard methodology for sleep study is polysomnography (PSG), an intrusive and onerous technique that can disrupt normal routines. In this perspective, m-Health technologies offer an unobtrusive and rapid solution for home monitoring. We developed a multi-scale method based on motion signal extracted from an unobtrusive device to evaluate sleep behavior. Data used in this study were collected during two different acquisition campaigns by using a Pressure Bed Sensor (PBS). The first one was carried out with 22 subjects for sleep problems, and the second one comprises 11 healthy shift workers. All underwent full PSG and PBS recordings. The algorithm consists of extracting sleep quality and fragmentation indexes correlating to clinical metrics. In particular, the method classifies sleep windows of 1-s of the motion signal into: displacement (DI), quiet sleep (QS), disrupted sleep (DS) and absence from the bed (ABS). QS proved to be positively correlated (0.72±0.014) to Sleep Efficiency (SE) and DS/DI positively correlated (0.85±0.007) to the Apnea-Hypopnea Index (AHI). The work proved to be potentially helpful in the early investigation of sleep in the home environment. The minimized intrusiveness of the device together with a low complexity and good performance might provide valuable indications for the home monitoring of sleep disorders and for subjects’ awareness. Full article
(This article belongs to the Special Issue Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications)
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23 pages, 3036 KiB  
Article
A Novel Approach for Segment-Length Selection Based on Stationarity to Perform Effective Connectivity Analysis Applied to Resting-State EEG Signals
by Leonardo Góngora, Alessia Paglialonga, Alfonso Mastropietro, Giovanna Rizzo and Riccardo Barbieri
Sensors 2022, 22(13), 4747; https://doi.org/10.3390/s22134747 - 23 Jun 2022
Cited by 2 | Viewed by 1928
Abstract
Connectivity among different areas within the brain is a topic that has been notably studied in the last decade. In particular, EEG-derived measures of effective connectivity examine the directionalities and the exerted influences raised from the interactions among neural sources that are masked [...] Read more.
Connectivity among different areas within the brain is a topic that has been notably studied in the last decade. In particular, EEG-derived measures of effective connectivity examine the directionalities and the exerted influences raised from the interactions among neural sources that are masked out on EEG signals. This is usually performed by fitting multivariate autoregressive models that rely on the stationarity that is assumed to be maintained over shorter bits of the signals. However, despite being a central condition, the selection process of a segment length that guarantees stationary conditions has not been systematically addressed within the effective connectivity framework, and thus, plenty of works consider different window sizes and provide a diversity of connectivity results. In this study, a segment-size-selection procedure based on fourth-order statistics is proposed to make an informed decision on the appropriate window size that guarantees stationarity both in temporal and spatial terms. Specifically, kurtosis is estimated as a function of the window size and used to measure stationarity. A search algorithm is implemented to find the segments with similar stationary properties while maximizing the number of channels that exhibit the same properties and grouping them accordingly. This approach is tested on EEG signals recorded from six healthy subjects during resting-state conditions, and the results obtained from the proposed method are compared to those obtained using the classical approach for mapping effective connectivity. The results show that the proposed method highlights the influence that arises in the Default Mode Network circuit by selecting a window of 4 s, which provides, overall, the most uniform stationary properties across channels. Full article
(This article belongs to the Special Issue Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications)
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12 pages, 48960 KiB  
Article
Applications of Laser-Induced Fluorescence in Medicine
by Mirosław Kwaśny and Aneta Bombalska
Sensors 2022, 22(8), 2956; https://doi.org/10.3390/s22082956 - 12 Apr 2022
Cited by 21 | Viewed by 4569
Abstract
Fluorescence is the most sensitive spectroscopic method of analysis and fluorescence methods. However, classical analysis requires sampling. There are new needs for real-time analyses of biological materials, without the need for sampling. This article presents examples of proprietary applications of laser-induced fluorescence (LIF) [...] Read more.
Fluorescence is the most sensitive spectroscopic method of analysis and fluorescence methods. However, classical analysis requires sampling. There are new needs for real-time analyses of biological materials, without the need for sampling. This article presents examples of proprietary applications of laser-induced fluorescence (LIF) in medicine with such methods. A classic example is the analysis of photosensitizers using the photodynamic treatment method (PDT). The level and kinetics of accumulation and excretion of sensitizers in the body are examined, as well as the optimal exposure time after the application of compounds. The LIF method is also used to analyze endogenous fluorophores; it has been used to detect neoplasms, e.g., lung cancer or gynecological and dermatological diseases. Furthermore, it is used for the diagnosis of early stages of tooth decay or detection of fungi. The article will present the construction of sensors based on the LIF method—fiber laser spectrometers and investigated fluorescence spectra in individual applications. Examples of fluorescence imaging, e.g., dermatological, and dental diagnostics and measuring systems will be presented. The advantage of the method is it has greater sensitivity and easily detects lesions early compared to the methods used in observing the material in reflected light. Full article
(This article belongs to the Special Issue Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications)
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21 pages, 3409 KiB  
Article
Whole-Body Adaptive Functional Electrical Stimulation Kinesitherapy Can Promote the Restoring of Physiological Muscle Synergies for Neurological Patients
by Alessandro Scano, Robert Mihai Mira, Guido Gabbrielli, Franco Molteni and Viktor Terekhov
Sensors 2022, 22(4), 1443; https://doi.org/10.3390/s22041443 - 13 Feb 2022
Cited by 3 | Viewed by 3649
Abstract
Background: Neurological diseases and traumas are major factors that may reduce motor functionality. Functional electrical stimulation is a technique that helps regain motor function, assisting patients in daily life activities and in rehabilitation practices. In this study, we evaluated the efficacy of a [...] Read more.
Background: Neurological diseases and traumas are major factors that may reduce motor functionality. Functional electrical stimulation is a technique that helps regain motor function, assisting patients in daily life activities and in rehabilitation practices. In this study, we evaluated the efficacy of a treatment based on whole-body Adaptive Functional Electrical Stimulation Kinesitherapy (AFESK™) with the use of muscle synergies, a well-established method for evaluation of motor coordination. The evaluation is performed on retrospectively gathered data of neurological patients executing whole-body movements before and after AFESK-based treatments. Methods: Twenty-four chronic neurologic patients and 9 healthy subjects were recruited in this study. The patient group was further subdivided in 3 subgroups: hemiplegic, tetraplegic and paraplegic. All patients underwent two acquisition sessions: before treatment and after a FES based rehabilitation treatment at the VIKTOR Physio Lab. Patients followed whole-body exercise protocols tailored to their needs. The control group of healthy subjects performed all movements in a single session and provided reference data for evaluating patients’ performance. sEMG was recorded on relevant muscles and muscle synergies were extracted for each patient’s EMG data and then compared to the ones extracted from the healthy volunteers. To evaluate the effect of the treatment, the motricity index was measured and patients’ extracted synergies were compared to the control group before and after treatment. Results: After the treatment, patients’ motricity index increased for many of the screened body segments. Muscle synergies were more similar to those of healthy people. Globally, the normalized synergy similarity in respect to the control group was 0.50 before the treatment and 0.60 after (p < 0.001), with improvements for each subgroup of patients. Conclusions: AFESK treatment induced favorable changes in muscle activation patterns in chronic neurologic patients, partially restoring muscular patterns similar to healthy people. The evaluation of the synergic relationships of muscle activity when performing test exercises allows to assess the results of rehabilitation measures in patients with impaired locomotor functions. Full article
(This article belongs to the Special Issue Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications)
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11 pages, 2164 KiB  
Article
Experimental Assessment of Cuff Pressures on the Walls of a Trachea-Like Model Using Force Sensing Resistors: Insights for Patient Management in Intensive Care Unit Settings
by Antonino Crivello, Mario Milazzo, Davide La Rosa, Giacomo Fiacchini, Serena Danti, Fabio Guarracino, Stefano Berrettini and Luca Bruschini
Sensors 2022, 22(2), 697; https://doi.org/10.3390/s22020697 - 17 Jan 2022
Cited by 6 | Viewed by 2574
Abstract
The COVID-19 outbreak has increased the incidence of tracheal lesions in patients who underwent invasive mechanical ventilation. We measured the pressure exerted by the cuff on the walls of a test bench mimicking the laryngotracheal tract. The test bench was designed to acquire [...] Read more.
The COVID-19 outbreak has increased the incidence of tracheal lesions in patients who underwent invasive mechanical ventilation. We measured the pressure exerted by the cuff on the walls of a test bench mimicking the laryngotracheal tract. The test bench was designed to acquire the pressure exerted by endotracheal tube cuffs inflated inside an artificial model of a human trachea. The experimental protocol consisted of measuring pressure values before and after applying a maneuver on two types of endotracheal tubes placed in two mock-ups resembling two different sized tracheal tracts. Increasing pressure values were used to inflate the cuff and the pressures were recorded in two different body positions. The recorded pressure increased proportionally to the input pressure. Moreover, the pressure values measured when using the non-armored (NA) tube were usually higher than those recorded when using the armored (A) tube. A periodic check of the cuff pressure upon changing the body position and/or when performing maneuvers on the tube appears to be necessary to prevent a pressure increase on the tracheal wall. In addition, in our model, the cuff of the A tube gave a more stable output pressure on the tracheal wall than that of the NA tube. Full article
(This article belongs to the Special Issue Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications)
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13 pages, 1911 KiB  
Article
Questioning Domain Adaptation in Myoelectric Hand Prostheses Control: An Inter- and Intra-Subject Study
by Giulio Marano, Cristina Brambilla, Robert Mihai Mira, Alessandro Scano, Henning Müller and Manfredo Atzori
Sensors 2021, 21(22), 7500; https://doi.org/10.3390/s21227500 - 11 Nov 2021
Cited by 7 | Viewed by 3103
Abstract
One major challenge limiting the use of dexterous robotic hand prostheses controlled via electromyography and pattern recognition relates to the important efforts required to train complex models from scratch. To overcome this problem, several studies in recent years proposed to use transfer learning, [...] Read more.
One major challenge limiting the use of dexterous robotic hand prostheses controlled via electromyography and pattern recognition relates to the important efforts required to train complex models from scratch. To overcome this problem, several studies in recent years proposed to use transfer learning, combining pre-trained models (obtained from prior subjects) with training sessions performed on a specific user. Although a few promising results were reported in the past, it was recently shown that the use of conventional transfer learning algorithms does not increase performance if proper hyperparameter optimization is performed on the standard approach that does not exploit transfer learning. The objective of this paper is to introduce novel analyses on this topic by using a random forest classifier without hyperparameter optimization and to extend them with experiments performed on data recorded from the same patient, but in different data acquisition sessions. Two domain adaptation techniques were tested on the random forest classifier, allowing us to conduct experiments on healthy subjects and amputees. Differently from several previous papers, our results show that there are no appreciable improvements in terms of accuracy, regardless of the transfer learning techniques tested. The lack of adaptive learning is also demonstrated for the first time in an intra-subject experimental setting when using as a source ten data acquisitions recorded from the same subject but on five different days. Full article
(This article belongs to the Special Issue Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications)
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25 pages, 2704 KiB  
Systematic Review
Combined Use of EMG and EEG Techniques for Neuromotor Assessment in Rehabilitative Applications: A Systematic Review
by Cristina Brambilla, Ileana Pirovano, Robert Mihai Mira, Giovanna Rizzo, Alessandro Scano and Alfonso Mastropietro
Sensors 2021, 21(21), 7014; https://doi.org/10.3390/s21217014 - 22 Oct 2021
Cited by 36 | Viewed by 8451
Abstract
Electroencephalography (EEG) and electromyography (EMG) are widespread and well-known quantitative techniques used for gathering biological signals at cortical and muscular levels, respectively. Indeed, they provide relevant insights for increasing knowledge in different domains, such as physical and cognitive, and research fields, including neuromotor [...] Read more.
Electroencephalography (EEG) and electromyography (EMG) are widespread and well-known quantitative techniques used for gathering biological signals at cortical and muscular levels, respectively. Indeed, they provide relevant insights for increasing knowledge in different domains, such as physical and cognitive, and research fields, including neuromotor rehabilitation. So far, EEG and EMG techniques have been independently exploited to guide or assess the outcome of the rehabilitation, preferring one technique over the other according to the aim of the investigation. More recently, the combination of EEG and EMG started to be considered as a potential breakthrough approach to improve rehabilitation effectiveness. However, since it is a relatively recent research field, we observed that no comprehensive reviews available nor standard procedures and setups for simultaneous acquisitions and processing have been identified. Consequently, this paper presents a systematic review of EEG and EMG applications specifically aimed at evaluating and assessing neuromotor performance, focusing on cortico-muscular interactions in the rehabilitation field. A total of 213 articles were identified from scientific databases, and, following rigorous scrutiny, 55 were analyzed in detail in this review. Most of the applications are focused on the study of stroke patients, and the rehabilitation target is usually on the upper or lower limbs. Regarding the methodological approaches used to acquire and process data, our results show that a simultaneous EEG and EMG acquisition is quite common in the field, but it is mostly performed with EMG as a support technique for more specific EEG approaches. Non-specific processing methods such as EEG-EMG coherence are used to provide combined EEG/EMG signal analysis, but rarely both signals are analyzed using state-of-the-art techniques that are gold-standard in each of the two domains. Future directions may be oriented toward multi-domain approaches able to exploit the full potential of combined EEG and EMG, for example targeting a wider range of pathologies and implementing more structured clinical trials to confirm the results of the current pilot studies. Full article
(This article belongs to the Special Issue Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications)
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