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24 pages, 7091 KiB

Open AccessArticle

Utility of Cognitive Neural Features for Predicting Mental Health Behaviors

by Ryosuke Kato, Pragathi Priyadharsini Balasubramani, Dhakshin Ramanathan and Jyoti Mishra

Sensors 2022, 22(9), 3116; https://doi.org/10.3390/s22093116 - 19 Apr 2022

Cited by 5 | Viewed by 2570

Abstract

Cognitive dysfunction underlies common mental health behavioral symptoms including depression, anxiety, inattention, and hyperactivity. In this study of 97 healthy adults, we aimed to classify healthy vs. mild-to-moderate self-reported symptoms of each disorder using cognitive neural markers measured with an electroencephalography (EEG). We [...] Read more.

Cognitive dysfunction underlies common mental health behavioral symptoms including depression, anxiety, inattention, and hyperactivity. In this study of 97 healthy adults, we aimed to classify healthy vs. mild-to-moderate self-reported symptoms of each disorder using cognitive neural markers measured with an electroencephalography (EEG). We analyzed source-reconstructed EEG data for event-related spectral perturbations in the theta, alpha, and beta frequency bands in five tasks, a selective attention and response inhibition task, a visuospatial working memory task, a Flanker interference processing task, and an emotion interference task. From the cortical source activation features, we derived augmented features involving co-activations between any two sources. Logistic regression on the augmented feature set, but not the original feature set, predicted the presence of psychiatric symptoms, particularly for anxiety and inattention with >80% sensitivity and specificity. We also computed current flow closeness and betweenness centralities to identify the “hub” source signal predictors. We found that the Flanker interference processing task was the most useful for assessing the connectivity hubs in general, followed by the inhibitory control go-nogo paradigm. Overall, these interpretable machine learning analyses suggest that EEG biomarkers collected on a rapid suite of cognitive assessments may have utility in classifying diverse self-reported mental health symptoms. Full article

(This article belongs to the Special Issue Biomedical Signal Acquisition and Processing Using Sensors)

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15 pages, 950 KiB

Open AccessArticle

Modified Time-Frequency Marginal Features for Detection of Seizures in Newborns

by Nabeel Ali Khan, Sadiq Ali and Kwonhue Choi

Sensors 2022, 22(8), 3036; https://doi.org/10.3390/s22083036 - 15 Apr 2022

Cited by 3 | Viewed by 1649

Abstract

The timely detection of seizure activity in the case of newborns can help save lives. Clinical signs of seizures in newborns are difficult to observe, so, in this study, we propose an automated method of detecting seizures in newborns using multi-channel electroencephalogram (EEG) [...] Read more.

The timely detection of seizure activity in the case of newborns can help save lives. Clinical signs of seizures in newborns are difficult to observe, so, in this study, we propose an automated method of detecting seizures in newborns using multi-channel electroencephalogram (EEG) recording acquired from 36 newborns admitted to Royal Women’s Hospital, Brisbane, Australia. A novel set of time-frequency marginal features are defined to detect seizure activity in newborns. The proposed set is based on the observation that EEG seizure signals appear either as a train of spikes or as a summation of frequency-modulated chirps with slow variation in the instantaneous frequency curve. The proposed set of features is obtained by extracting the time-frequency (TF) signature of seizure spikes and frequency-modulated chirps by exploiting the direction of ridges in the TF plane. Based on extracted TF signature of spikes, the modified time-marginal is computed whereas based on the extracted TF signature of frequency-modulated chirps, the modified frequency-marginal is computed. It is demonstrated that features extracted from the modified time-domain marginal and frequency-domain marginal in combination with TF statistical and frequency-related features lead to better accuracy than the existing TF signal classification method, i.e., the proposed method achieves an F1 score of 70.93% which is 5% greater than the existing method. Full article

(This article belongs to the Special Issue Biomedical Signal Acquisition and Processing Using Sensors)

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23 pages, 1289 KiB

Open AccessArticle

Emotion Self-Regulation in Neurotic Students: A Pilot Mindfulness-Based Intervention to Assess Its Effectiveness through Brain Signals and Behavioral Data

by Lila Iznita Izhar, Areej Babiker, Edmi Edison Rizki, Cheng-Kai Lu and Mohammad Abdul Rahman

Sensors 2022, 22(7), 2703; https://doi.org/10.3390/s22072703 - 01 Apr 2022

Cited by 6 | Viewed by 3917

Abstract

Neuroticism has recently received increased attention in the psychology field due to the finding of high implications of neuroticism on an individual’s life and broader public health. This study aims to investigate the effect of a brief 6-week breathing-based mindfulness intervention (BMI) on [...] Read more.

Neuroticism has recently received increased attention in the psychology field due to the finding of high implications of neuroticism on an individual’s life and broader public health. This study aims to investigate the effect of a brief 6-week breathing-based mindfulness intervention (BMI) on undergraduate neurotic students’ emotion regulation. We acquired data of their psychological states, physiological changes, and electroencephalogram (EEG), before and after BMI, in resting states and tasks. Through behavioral analysis, we found the students’ anxiety and stress levels significantly reduced after BMI, with p-values of 0.013 and 0.027, respectively. Furthermore, a significant difference between students in emotion regulation strategy, that is, suppression, was also shown. The EEG analysis demonstrated significant differences between students before and after MI in resting states and tasks. Fp1 and O2 channels were identified as the most significant channels in evaluating the effect of BMI. The potential of these channels for classifying (single-channel-based) before and after BMI conditions during eyes-opened and eyes-closed baseline trials were displayed by a good performance in terms of accuracy (~77%), sensitivity (76–80%), specificity (73–77%), and area-under-the-curve (AUC) (0.66–0.8) obtained by k-nearest neighbor (KNN) and support vector machine (SVM) algorithms. Mindfulness can thus improve the self-regulation of the emotional state of neurotic students based on the psychometric and electrophysiological analyses conducted in this study. Full article

(This article belongs to the Special Issue Biomedical Signal Acquisition and Processing Using Sensors)

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25 pages, 13505 KiB

Open AccessArticle

Towards a Singing Voice Multi-Sensor Analysis Tool: System Design, and Assessment Based on Vocal Breathiness

by Evangelos Angelakis, Natalia Kotsani and Anastasia Georgaki

Sensors 2021, 21(23), 8006; https://doi.org/10.3390/s21238006 - 30 Nov 2021

Cited by 2 | Viewed by 2867

Abstract

Singing voice is a human quality that requires the precise coordination of numerous kinetic functions and results in a perceptually variable auditory outcome. The use of multi-sensor systems can facilitate the study of correlations between the vocal mechanism kinetic functions and the voice [...] Read more.

Singing voice is a human quality that requires the precise coordination of numerous kinetic functions and results in a perceptually variable auditory outcome. The use of multi-sensor systems can facilitate the study of correlations between the vocal mechanism kinetic functions and the voice output. This is directly relevant to vocal education, rehabilitation, and prevention of vocal health issues in educators; professionals; and students of singing, music, and acting. In this work, we present the initial design of a modular multi-sensor system for singing voice analysis, and describe its first assessment experiment on the ‘vocal breathiness’ qualitative characteristic. A system case study with two professional singers was conducted, utilizing signals from four sensors. Participants sung a protocol of vocal trials in various degrees of intended vocal breathiness. Their (i) vocal output, (ii) phonatory function, and (iii) respiratory behavior-per-condition were recorded through a condenser microphone (CM), an Electroglottograph (EGG), and thoracic and abdominal respiratory effort transducers (RET), respectively. Participants’ individual respiratory management strategies were studied through qualitative analysis of RET data. Microphone audio samples breathiness degree was rated perceptually, and correlation analysis was performed between sample ratings and parameters extracted from CM and EGG data. Smoothed Cepstral Peak Prominence (CPPS) and vocal folds’ Open Quotient (OQ), as computed with the Howard method (HOQ), demonstrated the higher correlation coefficients, when analyzed individually. DECOM method-computed OQ (DOQ) was also examined. Interestingly, the correlation coefficient of pitch difference between estimates from CM and EGG signals appeared to be (based on the Pearson correlation coefficient) statistically insignificant (a result that warrants investigation in larger populations). The study of multi-variate models revealed even higher correlation coefficients. Models studied were the Acoustic Breathiness Index (ABI) and the proposed multiple regression model CDH (CPPS, DOQ, and HOQ), which was attempted in order to combine analysis results from microphone and EGG signals. The model combination of ABI and the proposed CDH appeared to yield the highest correlation with perceptual breathiness ratings. Study results suggest potential for the use of a completed system version in vocal pedagogy and research, as the case study indicated system practicality, a number of pertinent correlations, and introduced topics with further research possibilities. Full article

(This article belongs to the Special Issue Biomedical Signal Acquisition and Processing Using Sensors)

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11 pages, 940 KiB

Open AccessArticle

Graph Theory on Brain Cortical Sources in Parkinson’s Disease: The Analysis of ‘Small World’ Organization from EEG

by Fabrizio Vecchio, Chiara Pappalettera, Francesca Miraglia, Francesca Alù, Alessandro Orticoni, Elda Judica, Maria Cotelli, Francesca Pistoia and Paolo Maria Rossini

Sensors 2021, 21(21), 7266; https://doi.org/10.3390/s21217266 - 31 Oct 2021

Cited by 11 | Viewed by 2725

Abstract

Parkinson’s disease (PD) is the second most common neurodegenerative disease in the elderly population. Similarly to other neurodegenerative diseases, the early diagnosis of PD is quite difficult. The current pilot study aimed to explore the differences in brain connectivity between PD and NOrmal [...] Read more.

Parkinson’s disease (PD) is the second most common neurodegenerative disease in the elderly population. Similarly to other neurodegenerative diseases, the early diagnosis of PD is quite difficult. The current pilot study aimed to explore the differences in brain connectivity between PD and NOrmal eLDerly (Nold) subjects to evaluate whether connectivity analysis may speed up and support early diagnosis. A total of 26 resting state EEGs were analyzed from 13 PD patients and 13 age-matched Nold subjects, applying to cortical reconstructions the graph theory analyses, a mathematical representation of brain architecture. Results showed that PD patients presented a more ordered structure at slow-frequency EEG rhythms (lower value of SW) than Nold subjects, particularly in the theta band, whereas in the high-frequency alpha, PD patients presented more random organization (higher SW) than Nold subjects. The current results suggest that PD could globally modulate the cortical connectivity of the brain, modifying the functional network organization and resulting in motor and non-motor signs. Future studies could validate whether such an approach, based on a low-cost and non-invasive technique, could be useful for early diagnosis, for the follow-up of PD progression, as well as for evaluating pharmacological and neurorehabilitation treatments. Full article

(This article belongs to the Special Issue Biomedical Signal Acquisition and Processing Using Sensors)

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22 pages, 4537 KiB

Open AccessArticle

Automated Detection and Removal of Cardiac and Pulse Interferences from Neonatal EEG Signals

by Gabriella Tamburro, Pierpaolo Croce, Filippo Zappasodi and Silvia Comani

Sensors 2021, 21(19), 6364; https://doi.org/10.3390/s21196364 - 23 Sep 2021

Cited by 2 | Viewed by 2060

Abstract

Electrical cardiac and pulsatile interference is very difficult to remove from electroencephalographic (EEG) signals, especially if recorded in neonates, for which a small number of EEG channels is used. Several methods were proposed, including Blind Source Separation (BSS) methods that required the use [...] Read more.

Electrical cardiac and pulsatile interference is very difficult to remove from electroencephalographic (EEG) signals, especially if recorded in neonates, for which a small number of EEG channels is used. Several methods were proposed, including Blind Source Separation (BSS) methods that required the use of artificial cardiac-related signals to improve the separation of artefactual components. To optimize the separation of cardiac-related artefactual components, we propose a method based on Independent Component Analysis (ICA) that exploits specific features of the real electrocardiographic (ECG) signals that were simultaneously recorded with the neonatal EEG. A total of forty EEG segments from 19-channel neonatal EEG recordings with and without seizures were used to test and validate the performance of our method. We observed a significant reduction in the number of independent components (ICs) containing cardiac-related interferences, with a consequent improvement in the automated classification of the separated ICs. The comparison with the expert labeling of the ICs separately containing electrical cardiac and pulsatile interference led to an accuracy = 0.99, a false omission rate = 0.01 and a sensitivity = 0.93, outperforming existing methods. Furthermore, we verified that true brain activity was preserved in neonatal EEG signals reconstructed after the removal of artefactual ICs, demonstrating the effectiveness of our method and its safe applicability in a clinical context. Full article

(This article belongs to the Special Issue Biomedical Signal Acquisition and Processing Using Sensors)

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12 pages, 432 KiB

Open AccessCommunication

Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

by Akira Ikeda and Yoshikazu Washizawa

Sensors 2021, 21(16), 5309; https://doi.org/10.3390/s21165309 - 06 Aug 2021

Cited by 5 | Viewed by 2350

Abstract

The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component [...] Read more.

The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods. Full article

(This article belongs to the Special Issue Biomedical Signal Acquisition and Processing Using Sensors)

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21 pages, 1404 KiB

Open AccessEditor’s ChoiceArticle

Predicting Exact Valence and Arousal Values from EEG

by Filipe Galvão, Soraia M. Alarcão and Manuel J. Fonseca

Sensors 2021, 21(10), 3414; https://doi.org/10.3390/s21103414 - 14 May 2021

Cited by 48 | Viewed by 5793

Abstract

Recognition of emotions from physiological signals, and in particular from electroencephalography (EEG), is a field within affective computing gaining increasing relevance. Although researchers have used these signals to recognize emotions, most of them only identify a limited set of emotional states (e.g., happiness, [...] Read more.

Recognition of emotions from physiological signals, and in particular from electroencephalography (EEG), is a field within affective computing gaining increasing relevance. Although researchers have used these signals to recognize emotions, most of them only identify a limited set of emotional states (e.g., happiness, sadness, anger, etc.) and have not attempted to predict exact values for valence and arousal, which would provide a wider range of emotional states. This paper describes our proposed model for predicting the exact values of valence and arousal in a subject-independent scenario. To create it, we studied the best features, brain waves, and machine learning models that are currently in use for emotion classification. This systematic analysis revealed that the best prediction model uses a KNN regressor (K = 1) with Manhattan distance, features from the alpha, beta and gamma bands, and the differential asymmetry from the alpha band. Results, using the DEAP, AMIGOS and DREAMER datasets, show that our model can predict valence and arousal values with a low error (MAE < 0.06, RMSE < 0.16) and a strong correlation between predicted and expected values (PCC > 0.80), and can identify four emotional classes with an accuracy of 84.4%. The findings of this work show that the features, brain waves and machine learning models, typically used in emotion classification tasks, can be used in more challenging situations, such as the prediction of exact values for valence and arousal. Full article

(This article belongs to the Special Issue Biomedical Signal Acquisition and Processing Using Sensors)

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17 pages, 851 KiB

Open AccessArticle

Common-Mode Noise Reduction in Noncontact Biopotential Acquisition Circuit Based on Imbalance Cancellation of Electrode-Body Impedance

by Minghui Chen, Jianqing Wang, Daisuke Anzai, Georg Fischer and Jens Kirchner

Sensors 2020, 20(24), 7140; https://doi.org/10.3390/s20247140 - 13 Dec 2020

Cited by 1 | Viewed by 3433

Abstract

Biopotential sensing technology with electrodes has a great future in medical treatment and human—machine interface, whereas comfort and longevity are two significant problems during usage. Noncontact electrode is a promising alternative to achieve more comfortable and long term biopotential signal recordings than contact [...] Read more.

Biopotential sensing technology with electrodes has a great future in medical treatment and human—machine interface, whereas comfort and longevity are two significant problems during usage. Noncontact electrode is a promising alternative to achieve more comfortable and long term biopotential signal recordings than contact electrode. However, it could pick up a significantly higher level of common-mode (CM) noise, which is hardly solved with passive filtering. The impedance imbalance at the electrode-body interface is a limiting factor of this problem, which reduces the common mode rejection ratio (CMRR) of the amplifier. In this work, we firstly present two novel CM noise reduction circuit designs. The circuit designs are based on electrode-body impedance imbalance cancellation. We perform circuit analysis and circuit simulations to explain the principles of the two circuits, both of which showed effectiveness in CM noise rejection. Secondly, we proposed a practical approach to detect and monitor the electrode-body impedance imbalance change. Compared with the conventional approach, it has certain advantages in interference immunity, and good linearity for capacitance. Lastly, we show experimental evaluation results on one of the designs we proposed. The results indicated the validity and feasibility of the approach. Full article

(This article belongs to the Special Issue Biomedical Signal Acquisition and Processing Using Sensors)

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36 pages, 15330 KiB

Open AccessArticle

Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing

by Jesús Jaime Moreno Escobar, Oswaldo Morales Matamoros, Ricardo Tejeida Padilla, Liliana Chanona Hernández, Juan Pablo Francisco Posadas Durán, Ana Karen Pérez Martínez, Ixchel Lina Reyes and Hugo Quintana Espinosa

Sensors 2020, 20(23), 6991; https://doi.org/10.3390/s20236991 - 07 Dec 2020

Cited by 7 | Viewed by 3633

Abstract

There are several pathologies attacking the central nervous system and diverse therapies for each specific disease. These therapies seek as far as possible to minimize or offset the consequences caused by these types of pathologies and disorders in the patient. Therefore, comprehensive neurological [...] Read more.

There are several pathologies attacking the central nervous system and diverse therapies for each specific disease. These therapies seek as far as possible to minimize or offset the consequences caused by these types of pathologies and disorders in the patient. Therefore, comprehensive neurological care has been performed by neurorehabilitation therapies, to improve the patients’ life quality and facilitating their performance in society. One way to know how the neurorehabilitation therapies contribute to help patients is by measuring changes in their brain activity by means of electroencephalograms (EEG). EEG data-processing applications have been used in neuroscience research to be highly computing- and data-intensive. Our proposal is an integrated system of Electroencephalographic, Electrocardiographic, Bioacoustic, and Digital Image Acquisition Analysis to provide neuroscience experts with tools to estimate the efficiency of a great variety of therapies. The three main axes of this proposal are: parallel or distributed capture, filtering and adaptation of biomedical signals, and synchronization in real epochs of sampling. Thus, the present proposal underlies a general system, whose main objective is to be a wireless benchmark in the field. In this way, this proposal could acquire and give some analysis tools for biomedical signals used for measuring brain interactions when it is stimulated by an external system during therapies, for example. Therefore, this system supports extreme environmental conditions, when necessary, which broadens the spectrum of its applications. In addition, in this proposal sensors could be added or eliminated depending on the needs of the research, generating a wide range of configuration limited by the number of CPU cores, i.e., the more biosensors, the more CPU cores will be required. To validate the proposed integrated system, it is used in a Dolphin-Assisted Therapy in patients with Infantile Cerebral Palsy and Obsessive–Compulsive Disorder, as well as with a neurotypical one. Event synchronization of sample periods helped isolate the same therapy stimulus and allowed it to be analyzed by tools such as the Power Spectrum or the Fractal Geometry. Full article

(This article belongs to the Special Issue Biomedical Signal Acquisition and Processing Using Sensors)

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19 pages, 11590 KiB

Open AccessTechnical Note

Requirements for Supporting Diagnostic Equipment of Respiration Process in Humans

by Szymon Nitkiewicz, Robert Barański, Marek Galewski, Hanna Zajączkiewicz, Andrzej Kukwa, Andrzej Zając, Stanisław Ejdys and Piotr Artiemjew

Sensors 2021, 21(10), 3479; https://doi.org/10.3390/s21103479 - 17 May 2021

Cited by 2 | Viewed by 2404

Abstract

There is abundant worldwide research conducted on the subject of the methods of human respiration process examination. However, many of these studies describe methods and present the results while often lacking insight into the hardware and software aspects of the devices used during [...] Read more.

There is abundant worldwide research conducted on the subject of the methods of human respiration process examination. However, many of these studies describe methods and present the results while often lacking insight into the hardware and software aspects of the devices used during the research. This paper’s goal is to present new equipment for assessing the parameters of human respiration, which can be easily adopted for daily diagnosis. This work deals with the issue of developing the correct method of obtaining measurement data. The requirements of the acquisition parameters are clearly pointed out and examples of the medical applications of the described device are shown. Statistical analysis of acquired signals proving its usability is also presented. In the examples of selected diseases of the Upper Respiratory Tract (URT), the advantages of the developed apparatus for supporting the diagnosis of URT patency have been proven. Full article

(This article belongs to the Special Issue Biomedical Signal Acquisition and Processing Using Sensors)

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12 pages, 3106 KiB

Open AccessLetter

A Time-Frequency Measurement and Evaluation Approach for Body Channel Characteristics in Galvanic Coupling Intrabody Communication

by Ziliang Wei, Yangrong Wen, Yueming Gao, Mingjing Yang, Jiejie Yang, Sio Hang Pun, Mang I Vai and Min Du

Sensors 2021, 21(2), 348; https://doi.org/10.3390/s21020348 - 06 Jan 2021

Cited by 1 | Viewed by 1985

Abstract

Intrabody communication (IBC) can achieve better power efficiency and higher levels of security than other traditional wireless communication technologies. Currently, the majority of research on the body channel characteristics of galvanic coupling IBC are motionless and have only been evaluated in the frequency [...] Read more.

Intrabody communication (IBC) can achieve better power efficiency and higher levels of security than other traditional wireless communication technologies. Currently, the majority of research on the body channel characteristics of galvanic coupling IBC are motionless and have only been evaluated in the frequency domain. Given the long measuring times of traditional methods, the access to dynamic variations and the simultaneous evaluation of the time-frequency domain remains a challenge for dynamic body channels such as the cardiac channel. To address this challenge, we proposed a parallel measurement methodology with a multi-tone strategy and a time-parameter processing approach to obtain a time-frequency evaluation for dynamic body channels. A group search algorithm has been performed to optimize the crest factor of multitone excitation in the time domain. To validate the proposed methods, in vivo experiments, with both dynamic and motionless conditions were measured using the traditional method and the proposed method. The results indicate that the proposed method is more time efficient (

T_{m e a s} = 1 ms

) with a consistent performance (

ρ_{c}

> 98%). Most importantly, it is capable of capturing dynamic variations in the body channel and provides a more comprehensive evaluation and richer information for the study of IBC. Full article

(This article belongs to the Special Issue Biomedical Signal Acquisition and Processing Using Sensors)

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