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Biomedical Signal Processing for the Diagnosis and Monitoring of Motor...
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Biomedical Signal Processing for the Diagnosis and Monitoring of Motor Disorders

A special issue of Biosensors (ISSN 2079-6374).

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 25634

Special Issue Editors

Prof. Dr. Dinesh K Kumar

E-Mail Website
Guest Editor
Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
Interests: biomedical signal processing; EMG; retina image analysis; thermal imaging; hyperspectral imaging
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marimuthu Palaniswami

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
Interests: sensors; biosensors; crystalline materials; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The successful management of neurological disorders, such as Parkinson’s disease (PD) or similar disorders, requires the early detection of the disease. There are no objective biomarkers and patients are evaluated by clinicians, which can lead to a delayed diagnosis, and the outcomes can be biased and dependent on the clinicians. This limits the objectivity of monitoring the progression of the disease as well as the effectiveness of treatment. There is an urgent need for quantifying human movement that can be suitable for the diagnosis and monitoring of such a disease.

Significant efforts are being made to develop devices that can sense human movement and muscle activity, which can be used for the diagnosis of a range of motor disorders. With the advancement of electronics and wireless technologies, a number of inexpensive devices, such as inertial movement units and accelerometers, are now available that can monitor human movement; digital tablets can record the fine motor control; while surface electromyogram devices can record the muscle activity.

The aim of this Special Issue is to bring together researchers working with the different modalities and looking at different aspects of the problem. We invite research papers that describe methods to process, analyze, and classify the recordings from these devices in order to diagnose and monitor neuro and motor diseases such as PD, post-stroke, and cerebral palsy, as well as similar disorders. We recognize that this research is multi-disciplinary and we invite researchers from clinical, engineering, scientific, and mathematical disciplines to submit their manuscripts. Regular length and short papers may be submitted, in accordance with the general guidelines of the journal. 

Prof. Dinesh K Kumar
Prof. Marimuthu Palaniswami
Guest Editors

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. Biosensors 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 2700 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

  • IMU
  • EMG
  • Parkinson’s disease
  • stroke
  • cerebral palsy
  • gait
  • falls
  • injury
  • arthritis
  • myopathy
  • neuropathy

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Published Papers (4 papers)

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Research

16 pages, 1339 KiB  
Article
Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson’s Disease
by Rekha Viswanathan, Sridhar P. Arjunan, Adrian Bingham, Beth Jelfs, Peter Kempster, Sanjay Raghav and Dinesh K. Kumar
Biosensors 2020, 10(1), 1; https://doi.org/10.3390/bios10010001 - 20 Dec 2019
Cited by 22 | Viewed by 6041
Abstract
In this paper, we have investigated the differences in the voices of Parkinson’s disease (PD) and age-matched control (CO) subjects when uttering three phonemes using two complexity measures: fractal dimension (FD) and normalised mutual information (NMI). Three sustained phonetic voice recordings, /a/, /u/ [...] Read more.
In this paper, we have investigated the differences in the voices of Parkinson’s disease (PD) and age-matched control (CO) subjects when uttering three phonemes using two complexity measures: fractal dimension (FD) and normalised mutual information (NMI). Three sustained phonetic voice recordings, /a/, /u/ and /m/, from 22 CO (mean age = 66.91) and 24 PD (mean age = 71.83) participants were analysed. FD was first computed for PD and CO voice recordings, followed by the computation of NMI between the test groups: PD–CO, PD–PD and CO–CO. Four features reported in the literature—normalised pitch period entropy (Norm. PPE), glottal-to-noise excitation ratio (GNE), detrended fluctuation analysis (DFA) and glottal closing quotient (ClQ)—were also computed for comparison with the proposed complexity measures. The statistical significance of the features was tested using a one-way ANOVA test. Support vector machine (SVM) with a linear kernel was used to classify the test groups, using a leave-one-out validation method. The results showed that PD voice recordings had lower FD compared to CO (p < 0.008). It was also observed that the average NMI between CO voice recordings was significantly lower compared with the CO–PD and PD–PD groups (p < 0.036) for the three phonetic sounds. The average NMI and FD demonstrated higher accuracy (>80%) in differentiating the test groups compared with other speech feature-based classifications. This study has demonstrated that the voices of PD patients has reduced FD, and NMI between voice recordings of PD–CO and PD–PD is higher compared with CO–CO. This suggests that the use of NMI obtained from the sample voice, when paired with known groups of CO and PD, can be used to identify PD voices. These findings could have applications for population screening. Full article
(This article belongs to the Special Issue Biomedical Signal Processing for the Diagnosis and Monitoring of Motor Disorders)
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17 pages, 2221 KiB  
Article
Label Self-Advised Support Vector Machine (LSA-SVM)—Automated Classification of Foot Drop Rehabilitation Case Study
by Sahar Adil Abboud, Saba Al-Wais, Salma Hameedi Abdullah, Fady Alnajjar and Adel Al-Jumaily
Biosensors 2019, 9(4), 114; https://doi.org/10.3390/bios9040114 - 27 Sep 2019
Cited by 10 | Viewed by 5333
Abstract
Stroke represents a major health problem in our society. One of the effects of stroke is foot drop. Foot drop (FD) is a weakness that occurs in specific muscles in the ankle and foot such as the anterior tibialis, gastrocnemius, plantaris and soleus [...] Read more.
Stroke represents a major health problem in our society. One of the effects of stroke is foot drop. Foot drop (FD) is a weakness that occurs in specific muscles in the ankle and foot such as the anterior tibialis, gastrocnemius, plantaris and soleus muscles. Foot flexion and extension are normally generated by lower motor neurons (LMN). The affected muscles impact the ankle and foot in both downward and upward motions. One possible solution for FD is to investigate the movement based on the bio signal (myoelectric signal) of the muscles. Bio signal control systems like electromyography (EMG) are used for rehabilitation devices that include foot drop. One of these systems is function electrical stimulation (FES). This paper proposes new methods and algorithms to develop the performance of myoelectric pattern recognition (M-PR), to improve automated rehabilitation devices, to test these methodologies in offline and real-time experimental datasets. Label classifying is a predictive data mining application with multiple applications in the world, including automatic labeling of resources such as videos, music, images and texts. We combine the label classification method with the self-advised support vector machine (SA-SVM) to create an adapted and altered label classification method, named the label self-advised support vector machine (LSA-SVM). For the experimental data, we collected data from foot drop patients using the sEMG device, in the Metro Rehabilitation Hospital in Sydney, Australia using Ethical Approval (UTS HREC NO. ETH15-0152). The experimental results for the EMG dataset and benchmark datasets exhibit its benefits. Furthermore, the experimental results on UCI datasets indicate that LSA-SVM achieves the best performance when working together with SA-SVM and SVM. This paper describes the state-of-the-art procedures for M-PR and studies all the conceivable structures. Full article
(This article belongs to the Special Issue Biomedical Signal Processing for the Diagnosis and Monitoring of Motor Disorders)
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11 pages, 2317 KiB  
Article
EMG-Based Characterization of Walking Asymmetry in Children with Mild Hemiplegic Cerebral Palsy
by Francesco Di Nardo, Annachiara Strazza, Alessandro Mengarelli, Stefano Cardarelli, Andrea Tigrini, Federica Verdini, Alberto Nascimbeni, Valentina Agostini, Marco Knaflitz and Sandro Fioretti
Biosensors 2019, 9(3), 82; https://doi.org/10.3390/bios9030082 - 27 Jun 2019
Cited by 24 | Viewed by 6554
Abstract
Hemiplegia is a neurological disorder that is often detected in children with cerebral palsy. Although many studies have investigated muscular activity in hemiplegic legs, few EMG-based findings focused on unaffected limb. This study aimed to quantify the asymmetric behavior of lower-limb-muscle recruitment during [...] Read more.
Hemiplegia is a neurological disorder that is often detected in children with cerebral palsy. Although many studies have investigated muscular activity in hemiplegic legs, few EMG-based findings focused on unaffected limb. This study aimed to quantify the asymmetric behavior of lower-limb-muscle recruitment during walking in mild-hemiplegic children from surface-EMG and foot-floor contact features. sEMG signals from tibialis anterior (TA) and gastrocnemius lateralis and foot-floor contact data during walking were analyzed in 16 hemiplegic children classified as W1 according to Winter’ scale, and in 100 control children. Statistical gait analysis, a methodology achieving a statistical characterization of gait by averaging surface-EMG-based features, was performed. Results, achieved in hundreds of strides for each child, indicated that in the hemiplegic side with respect to the non-hemiplegic side, W1 children showed a statistically significant: decreased number of strides with normal foot-floor contact; decreased stance-phase length and initial-contact sub-phase; curtailed, less frequent TA activity in terminal swing and a lack of TA activity at heel-strike. The acknowledged impairment of anti-phase eccentric control of dorsiflexors was confirmed in the hemiplegic side, but not in the contralateral side. However, a modified foot-floor contact pattern is evinced also in the contralateral side, probably to make up for balance requirements. Full article
(This article belongs to the Special Issue Biomedical Signal Processing for the Diagnosis and Monitoring of Motor Disorders)
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19 pages, 2923 KiB  
Article
Which Gait Parameters and Walking Patterns Show the Significant Differences Between Parkinson’s Disease and Healthy Participants?
by Sana M Keloth, Rekha Viswanathan, Beth Jelfs, Sridhar Arjunan, Sanjay Raghav and Dinesh Kumar
Biosensors 2019, 9(2), 59; https://doi.org/10.3390/bios9020059 - 25 Apr 2019
Cited by 13 | Viewed by 7013
Abstract
This study investigated the difference in the gait of patients with Parkinson’s disease (PD), age-matched controls and young controls during three walking patterns. Experiments were conducted with 24 PD, 24 age-matched controls and 24 young controls, and four gait intervals were measured using [...] Read more.
This study investigated the difference in the gait of patients with Parkinson’s disease (PD), age-matched controls and young controls during three walking patterns. Experiments were conducted with 24 PD, 24 age-matched controls and 24 young controls, and four gait intervals were measured using inertial measurement units (IMU). Group differences between the mean and variance of the gait parameters (stride interval, stance interval, swing interval and double support interval) for the three groups were calculated and statistical significance was tested. The results showed that the variance in each of the four gait parameters of PD patients was significantly higher compared with the controls, irrespective of the three walking patterns. This study showed that the variance of any of the gait interval parameters obtained using IMU during any of the walking patterns could be used to differentiate between the gait of PD and control people. Full article
(This article belongs to the Special Issue Biomedical Signal Processing for the Diagnosis and Monitoring of Motor Disorders)
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