Special Issue "Application of Signal Processing Methods for Systematic Analysis of Physiological Health"

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (20 January 2017).

Special Issue Editor

Prof. U Rajendra Acharya
E-Mail Website
Guest Editor
Ngee Ann Polytechnic, SIM University, Singapore Institute of Technology – Glasgow, Singapore
Tel. 96374029
Interests: biomedical signal processing; bioimaging; data mining; visualization; biophysics for better health care design; drug delivery and therapy
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Special Issue Information

Dear Colleagues,

Various physiological signals generated from different organs of our body are electroencephalogram (EEG), electrocardiogram (ECG), heart rate variability (HRV), magnetoencephalogram (MEG), electromyogram (EMG), phonocardiogram (PCG), blood pressure, blood glucose, speech, photoplethysmogram (PPG), respiration, etc. These bio-signals carry vital information about the health of corresponding organs. Similarly, any ailment in our organs can be visualized by using different modality images, such as X-ray, magnetic resonance imaging (MRI), computerized tomography (CT), Single photon emission computed tomography (SPECT), Positron emission tomography (PET), fundus and ultrasound images, etc., belonging to various body parts to obtain useful information. This demands advanced signal processing techniques to unearth the hidden information present in these signals, which are difficult to observe through the naked eye.

Application of such novel signal processing methods to the physiological signals and medical images will aid clinicians in the accurate fast diagnosis and help to improve patient quality of life. Thus, this Special Issue, entitled “Application of Signal Processing Methods for Systematic Analysis of Physiological Health”, focuses on new techniques that can be used to improve disease diagnosis and wellness.

U Rajendra Acharya
Guest Editor

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Published Papers (7 papers)

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Research

Open AccessArticle
Relationship between Amount of Daily Movement Measured by a Triaxial Accelerometer and Motor Symptoms in Patients with Parkinson’s Disease
Appl. Sci. 2017, 7(5), 486; https://doi.org/10.3390/app7050486 - 09 May 2017
Cited by 2
Abstract
The aim of this study was to analyze the association between the amount of daily movement measured with a triaxial accelerometer (MIMAMORI-Gait) and motor symptoms in patients with Parkinson’s disease (PD). The subjects were 50 consecutive patients with untreated PD free of dementia. [...] Read more.
The aim of this study was to analyze the association between the amount of daily movement measured with a triaxial accelerometer (MIMAMORI-Gait) and motor symptoms in patients with Parkinson’s disease (PD). The subjects were 50 consecutive patients with untreated PD free of dementia. The amount of overall movement over 24 h was measured with the portable MIMAMORI-Gait device and its association with the modified Hoehn and Yahr stage and UPDRS part II and III scores was analyzed. In patients with PD, the amount of overall movement measured with MIMAMORI-Gait was significantly associated with the UPDRS part II score (β = −0.506, p < 0.001) and part III score (β = −0.347, p = 0.010), but not with the modified Hoehn and Yahr stage. The amount of overall movement measured with MIMAMORI-Gait can potentially be used for evaluation of motor symptoms and ADL in PD patients. Full article
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Open AccessArticle
Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals
Appl. Sci. 2017, 7(4), 385; https://doi.org/10.3390/app7040385 - 12 Apr 2017
Cited by 73
Abstract
This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute [...] Read more.
This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of Q and the redundancy parameter (R) of TQWT showed better robustness for entropy computation in the presence of high- and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal formation of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier. We have also achieved classification accuracies of 99% and 98.6% in classifying seizure versus non-seizure EEG signals and the individual three classes, namely normal, seizure-free and seizure EEG signals, respectively. The performance measure of the proposed multi-scale entropy has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database. Full article
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Open AccessArticle
Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV
Appl. Sci. 2017, 7(4), 348; https://doi.org/10.3390/app7040348 - 31 Mar 2017
Cited by 5
Abstract
Gender recognition is trivial for a physiotherapist, but it is considered a challenge for computers. The electromyography (EMG) and heart rate variability (HRV) were utilized in this work for gender recognition during exercise using a stepper. The relevant features were extracted and selected. [...] Read more.
Gender recognition is trivial for a physiotherapist, but it is considered a challenge for computers. The electromyography (EMG) and heart rate variability (HRV) were utilized in this work for gender recognition during exercise using a stepper. The relevant features were extracted and selected. The selected features were then fused to automatically predict gender recognition. However, the feature selection for gender classification became a challenge to ensure better accuracy. Thus, in this paper, a feature selection approach based on both the performance and the diversity between the two features from the rank-score characteristic (RSC) function in a combinatorial fusion approach (CFA) (Hsu et al.) was employed. Then, the features from the selected feature sets were fused using a CFA. The results were then compared with other fusion techniques such as naive bayes (NB), decision tree (J48), k-nearest neighbor (KNN) and support vector machine (SVM). Besides, the results were also compared with previous researches in gender recognition. The experimental results showed that the CFA was efficient and effective for feature selection. The fusion method was also able to improve the accuracy of the gender recognition rate. The CFA provides much better gender classification results which is 94.51% compared to Barani’s work (90.34%), Nazarloo’s work (92.50%), and other classifiers. Full article
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Open AccessArticle
Global Synchronization of Multichannel EEG   Based on Rényi Entropy in Children with Autism  Spectrum Disorder
Appl. Sci. 2017, 7(3), 257; https://doi.org/10.3390/app7030257 - 06 Mar 2017
Cited by 7
Abstract
Autism spectrum disorder (ASD) has been defined as a pervasive neurodevelopmental disorder, involving communication, social interaction and repetitive behaviors. Currently, it is still challenging to understand the differences of brain activity between ASD and healthy children. In this study, we propose calculating the [...] Read more.
Autism spectrum disorder (ASD) has been defined as a pervasive neurodevelopmental disorder, involving communication, social interaction and repetitive behaviors. Currently, it is still challenging to understand the differences of brain activity between ASD and healthy children. In this study, we propose calculating the Rényi entropy of the eigenvalues derived from the signal correlation matrix to measure the global synchronization in multichannel electroencephalograph (EEG) from 16 children with ASD (aged 8–12 years) and 16 age‐ and sex‐matched healthy controls at the resting state. The results indicate that there is a significantly diminished global synchronization from ASD to healthy control. The proposed method can help to reveal the intrinsic characteristics of multichannel EEG signals in children with ASD and aspects that distinguish them from healthy children. Full article
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Open AccessArticle
Performance Comparison of Time-Frequency Distributions for Estimation of Instantaneous Frequency of Heart Rate Variability Signals
Appl. Sci. 2017, 7(3), 221; https://doi.org/10.3390/app7030221 - 27 Feb 2017
Cited by 9
Abstract
The instantaneous frequency (IF) of a non-stationary signal is usually estimated from a time-frequency distribution (TFD). The IF of heart rate variability (HRV) is an important parameter because the power in a frequency band around the IF can be used for the interpretation [...] Read more.
The instantaneous frequency (IF) of a non-stationary signal is usually estimated from a time-frequency distribution (TFD). The IF of heart rate variability (HRV) is an important parameter because the power in a frequency band around the IF can be used for the interpretation and analysis of the respiratory rate but also for a more accurate analysis of heart rate (HR) signals. In this study, we compare the performance of five states of the art kernel-based time-frequency distributions (TFDs) in terms of their ability to accurately estimate the IF of HR signals. The selected TFDs include three widely used fixed kernel methods: the modified B distribution, the S-method and the spectrogram; and two adaptive kernel methods: the adaptive optimal kernel TFD and the recently developed adaptive directional TFD. The IF of the respiratory signal, which is usually easier to estimate as the respiratory signal is a mono-component with small amplitude variations with time, is used as a reference to examine the accuracy of the HRV IF estimates. Experimental results indicate that the most reliable estimates are obtained using the adaptive directional TFD in comparison to other commonly used methods such as the adaptive optimal kernel TFD and the modified B distribution. Full article
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Open AccessCommunication
Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features
Appl. Sci. 2017, 7(2), 150; https://doi.org/10.3390/app7020150 - 06 Feb 2017
Cited by 27
Abstract
Driver fatigue has become one of the major causes of traffic accidents, and is a complicated physiological process. However, there is no effective method to detect driving fatigue. Electroencephalography (EEG) signals are complex, unstable, and non-linear; non-linear analysis methods, such as entropy, maybe [...] Read more.
Driver fatigue has become one of the major causes of traffic accidents, and is a complicated physiological process. However, there is no effective method to detect driving fatigue. Electroencephalography (EEG) signals are complex, unstable, and non-linear; non-linear analysis methods, such as entropy, maybe more appropriate. This study evaluates a combined entropy-based processing method of EEG data to detect driver fatigue. In this paper, 12 subjects were selected to take part in an experiment, obeying driving training in a virtual environment under the instruction of the operator. Four types of enthrones (spectrum entropy, approximate entropy, sample entropy and fuzzy entropy) were used to extract features for the purpose of driver fatigue detection. Electrode selection process and a support vector machine (SVM) classification algorithm were also proposed. The average recognition accuracy was 98.75%. Retrospective analysis of the EEG showed that the extracted features from electrodes T5, TP7, TP8 and FP1 may yield better performance. SVM classification algorithm using radial basis function as kernel function obtained better results. A combined entropy-based method demonstrates good classification performance for studying driver fatigue detection. Full article
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Open AccessArticle
Applying Improved Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG
Appl. Sci. 2017, 7(1), 92; https://doi.org/10.3390/app7010092 - 18 Jan 2017
Cited by 14
Abstract
Electroencephalography (EEG) is considered the output of a brain and it is a bioelectrical signal with multiscale and nonlinear properties. Motor Imagery EEG (MI-EEG) not only has a close correlation with the human imagination and movement intention but also contains a large amount [...] Read more.
Electroencephalography (EEG) is considered the output of a brain and it is a bioelectrical signal with multiscale and nonlinear properties. Motor Imagery EEG (MI-EEG) not only has a close correlation with the human imagination and movement intention but also contains a large amount of physiological or disease information. As a result, it has been fully studied in the field of rehabilitation. To correctly interpret and accurately extract the features of MI-EEG signals, many nonlinear dynamic methods based on entropy, such as Approximate Entropy (ApEn), Sample Entropy (SampEn), Fuzzy Entropy (FE), and Permutation Entropy (PE), have been proposed and exploited continuously in recent years. However, these entropy-based methods can only measure the complexity of MI-EEG based on a single scale and therefore fail to account for the multiscale property inherent in MI-EEG. To solve this problem, Multiscale Sample Entropy (MSE), Multiscale Permutation Entropy (MPE), and Multiscale Fuzzy Entropy (MFE) are developed by introducing scale factor. However, MFE has not been widely used in analysis of MI-EEG, and the same parameter values are employed when the MFE method is used to calculate the fuzzy entropy values on multiple scales. Actually, each coarse-grained MI-EEG carries the characteristic information of the original signal on different scale factors. It is necessary to optimize MFE parameters to discover more feature information. In this paper, the parameters of MFE are optimized independently for each scale factor, and the improved MFE (IMFE) is applied to the feature extraction of MI-EEG. Based on the event-related desynchronization (ERD)/event-related synchronization (ERS) phenomenon, IMFE features from multi channels are fused organically to construct the feature vector. Experiments are conducted on a public dataset by using Support Vector Machine (SVM) as a classifier. The experiment results of 10-fold cross-validation show that the proposed method yields relatively high classification accuracy compared with other entropy-based and classical time–frequency–space feature extraction methods. The t-test is used to prove the correctness of the improved MFE. Full article
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