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1 pages, 143 KiB

Open AccessEditorial

Statement of Peer Review

by Ioannis Kakkos and Dimitrios Glotsos

Eng. Proc. 2023, 50(1), 15; https://doi.org/10.3390/engproc2023050015 - 03 Jan 2024

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Abstract

In submitting conference proceedings to Engineering Proceedings, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors [...] Full article

(This article belongs to the Proceedings of Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference)

2 pages, 151 KiB

Open AccessEditorial

Preface: Proceedings of the Advances in Biomedical Sciences, Engineering and Technology Conference—ABSET 2023

by Ioannis Kakkos and Dimitrios Glotsos

Eng. Proc. 2023, 50(1), 16; https://doi.org/10.3390/engproc2023050016 - 03 Jan 2024

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Abstract

The Advances in Biomedical Sciences, Engineering and Technology (ABSET) 2023 international conference was organized by the MSc Program “Biomedical Engineering” and hosted by the Department of Biomedical Engineering of the University of West Attica, Greece [...] Full article

(This article belongs to the Proceedings of Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference)

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8 pages, 6557 KiB

Open AccessProceeding Paper

Bimodal Active Shape Models for Cervical Vertebrae and Spinal Canal Boundary Extraction

by Meletios Liaskos, Michalis A. Savelonas, Pantelis A. Asvestas and George K. Matsopoulos

Eng. Proc. 2023, 50(1), 1; https://doi.org/10.3390/engproc2023050001 - 26 Oct 2023

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Abstract

Cervical spine pathologies often stem from deformations of the intervertebral discs and spinal canal. This work introduces a computational method for boundary extraction of these structures. The proposed method employs an active shape model (ASM) and is bimodal, in the sense that computed [...] Read more.

Cervical spine pathologies often stem from deformations of the intervertebral discs and spinal canal. This work introduces a computational method for boundary extraction of these structures. The proposed method employs an active shape model (ASM) and is bimodal, in the sense that computed tomography (CT) images are used for ASM training and magnetic resonance (MR) images are used for ASM testing. The proposed method is less dependent on large amounts of training samples than deep learning methods, whereas it involves limited user intervention. Still, it is comparable to state-of-the-art methods in terms of segmentation quality, as demonstrated in our experimental comparisons. Full article

(This article belongs to the Proceedings of Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference)

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9 pages, 1795 KiB

Open AccessProceeding Paper

Rehabotics: A Comprehensive Rehabilitation Platform for Post-Stroke Spasticity, Incorporating a Soft Glove, a Robotic Exoskeleton Hand and Augmented Reality Serious Games

by Pantelis Syringas, Theodore Economopoulos, Ioannis Kouris, Ioannis Kakkos, Georgios Papagiannis, Athanasios Triantafyllou, Nikolaos Tselikas, George K. Matsopoulos and Dimitrios I. Fotiadis

Eng. Proc. 2023, 50(1), 2; https://doi.org/10.3390/engproc2023050002 - 27 Oct 2023

Cited by 1 | Viewed by 904

Abstract

Spasticity following a stroke often leads to severe motor impairments, necessitating comprehensive and personalized rehabilitation protocols. This paper presents Rehabotics, an innovative rehabilitation platform incorporating a multi-component design for the rehabilitation of patients with post-stroke spasticity in the upper limbs. This system incorporates [...] Read more.

Spasticity following a stroke often leads to severe motor impairments, necessitating comprehensive and personalized rehabilitation protocols. This paper presents Rehabotics, an innovative rehabilitation platform incorporating a multi-component design for the rehabilitation of patients with post-stroke spasticity in the upper limbs. This system incorporates a sensor-equipped soft glove, a robotic exoskeleton hand, and an augmented reality (AR) platform with serious games of varying difficulties for adaptive therapy personalization. The soft glove collects data regarding hand movements and force exertion levels when the patient touches an object. In conjunction with a web camera, this enables real-time physical therapy using AR serious games, thus targeting specific motor skills. The exoskeleton hand, facilitated by servomotors, assists patients in hand movements, specifically aiding in overcoming the challenge of hand opening. The proposed system utilizes the data collected and (in combination with the clinical measurements) provides personalized and refined rehabilitation plans and targeted therapy to the affected hand. A pilot study of Rehabotics was conducted with a sample of 14 stroke patients. This novel system promises to enhance patient engagement and outcomes in post-stroke spasticity rehabilitation by providing a personalized, adaptive, and engaging therapy experience. Full article

(This article belongs to the Proceedings of Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference)

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9 pages, 4447 KiB

Open AccessProceeding Paper

Benchmarking Computer-Vision-Based Facial Emotion Classification Algorithms While Wearing Surgical Masks

by Luis Coelho, Sara Reis, Cristina Moreira, Helena Cardoso, Miguela Sequeira and Raquel Coelho

Eng. Proc. 2023, 50(1), 3; https://doi.org/10.3390/engproc2023050003 - 27 Oct 2023

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Abstract

Effective human communication relies heavily on emotions, making them a crucial aspect of interaction. As technology progresses, the desire for machines to exhibit more human-like characteristics, including emotion recognition, grows. DeepFace has emerged as a widely adopted library for facial emotion recognition. However, [...] Read more.

Effective human communication relies heavily on emotions, making them a crucial aspect of interaction. As technology progresses, the desire for machines to exhibit more human-like characteristics, including emotion recognition, grows. DeepFace has emerged as a widely adopted library for facial emotion recognition. However, the widespread use of surgical masks after the COVID-19 pandemic presents a considerable obstacle to its performance. To assess this issue, we conducted a benchmark using the FER2013 dataset. The results revealed a substantial performance decline when individuals wore surgical masks. “Disgust” suffers a 22.6% F1-score reduction, while “Surprise” is least affected with a 48.7% reduction. Addressing these issues improves human–machine interfaces and paves the way for more natural machine communication. Full article

(This article belongs to the Proceedings of Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference)

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8 pages, 1002 KiB

Open AccessProceeding Paper

The Effect of an Interventional Movement Program on the Mechanical Gait Characteristics of a Patient with Dementia

by Pinelopi Vlotinou, Anna Tsiakiri, Christos A. Frantzidis, Ioanna-Giannoula Katsouri and Nikolaos Aggelousis

Eng. Proc. 2023, 50(1), 4; https://doi.org/10.3390/engproc2023050004 - 27 Oct 2023

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Abstract

We investigated the effect of an occupational therapy movement program (OTMP) on the specific mechanical characteristics of walking in a person with dementia. The hip joint of the patient’s dominant limb was examined for flexion, extension, adduction, abduction, and internal or external rotation [...] Read more.

We investigated the effect of an occupational therapy movement program (OTMP) on the specific mechanical characteristics of walking in a person with dementia. The hip joint of the patient’s dominant limb was examined for flexion, extension, adduction, abduction, and internal or external rotation movements. This study included simple gait and dual-task gait conditions, with motor and cognitive tasks performed simultaneously. Neuropsychological scales and the gait analysis system (Vicon) were used to assess the patient pre- and post-intervention. Following the OTMP, statistically significant improvements were observed in hip movements, including flexion/extension, adduction/abduction, and internal/external rotation. These findings suggest that the OTMP can enhance hip mechanical gait characteristics and potentially contribute to functional independence in individuals with dementia. Full article

(This article belongs to the Proceedings of Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference)

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8 pages, 844 KiB

Open AccessProceeding Paper

Prediction of Heart Wellness Based on the Analysis of Skin Color

by Kavya Sree Kammari, Neetu Srivastava and Ioannis Sarris

Eng. Proc. 2023, 50(1), 5; https://doi.org/10.3390/engproc2023050005 - 31 Oct 2023

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Abstract

Heart rate monitoring is crucial in scientific and technical fields as it provides essential information about cardiovascular health, exercise performance, and stress levels, enabling early detection of and intervention for potential cardiac abnormalities or risks. Traditional methods for measuring heart rate often require [...] Read more.

Heart rate monitoring is crucial in scientific and technical fields as it provides essential information about cardiovascular health, exercise performance, and stress levels, enabling early detection of and intervention for potential cardiac abnormalities or risks. Traditional methods for measuring heart rate often require direct contact with the body, which can be invasive and inconvenient. In this analysis, we have studied the remote photoplethysmography (rPPG) techniques for predicting heart wellness using different machine algorithms. To evaluate the effectiveness of different rPPG methods, we conducted a study with a diverse sample of 20 participants. We considered factors such as gender, skin texture (based on participants from India and Sierra Leone), and age group. By collecting data from various PPG and rPPG methods, we aimed to determine the most accurate technique for heart rate prediction. To accomplish this, we employed two machine learning algorithms: Lasso Regression and Random Forest Regression. These algorithms were trained on the collected heart rate data to predict and compare the performance of different rPPG methods. Our research findings indicate that both Random Forest Regression and Lasso Regression models exhibit promising results in predicting heart rate non-invasively and accurately. The Random Forest Regression model achieved an average mean square error of 3.193 and a coefficient of determination value of 0.885, while the Lasso Regression model achieved an average mean square error of 33.336 and a coefficient of determination, R2, value of 0.086. The relatively low Mean Squared Error (MSE) and high (R-squared) R2 values obtained from the Random Forest Regression model demonstrate its superior predictive performance compared to the Lasso Regression model. This suggests that the Random Forest algorithm is better suited for analyzing the collected heart rate prediction dataset using rPPG features. Our research findings underscore the potential of remote photoplethysmography (rPPG) and machine learning algorithms in predicting heart rate non-invasively. We have successfully analyzed the study method across different genders, regions, and skin colors. Moreover, our study emphasizes the significance of considering factors such as skin color pigments and their impact on the accuracy of heart rate predictions. By recognizing the influence of these factors, we can further refine and improve the performance of rPPG-based heart rate monitoring systems. Full article

(This article belongs to the Proceedings of Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference)

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7 pages, 2843 KiB

Open AccessProceeding Paper

Time-Dependent Adaptations of Brain Networks in Driving Fatigue

by Olympia Giannakopoulou, Ioannis Kakkos, Georgios N. Dimitrakopoulos, Yu Sun, George K. Matsopoulos and Dimitrios D. Koutsouris

Eng. Proc. 2023, 50(1), 6; https://doi.org/10.3390/engproc2023050006 - 31 Oct 2023

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Abstract

Driving with fatigue is a major contributor to traffic accidents and is closely linked to central nervous system functions. To investigate the evolution of brain dynamics during simulated driving under different EEG rhythms, we conducted an experiment in which participants performed a 1 [...] Read more.

Driving with fatigue is a major contributor to traffic accidents and is closely linked to central nervous system functions. To investigate the evolution of brain dynamics during simulated driving under different EEG rhythms, we conducted an experiment in which participants performed a 1 h driving task while their EEG signals were recorded. We used the complex network theory to analyze data derived from the driving stimulation and found that as fatigue deepened, small-world metrics, namely the path lengths, clustering coefficients, and measures of efficiency (global, local, nodal), showed alterations against the driving time. Additionally, a major correlation (corr = 0.98) was observed between the cluster coefficient with local efficiency in all frequency bands (theta, alpha, beta). Our findings suggest that driving fatigue can cause significant trends in brain network characteristics, such as path length (m = −103 to −93), (m = 98) for specific rhythms (beta, alpha, theta band, respectively) and their related brain functions, which could serve as objective indicators when evaluating the fatigue level and in the future, preventing driving fatigue and its consequences. Full article

(This article belongs to the Proceedings of Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference)

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10 pages, 2050 KiB

Open AccessProceeding Paper

Brain Signals to Actions Using Machine Learning

by Dimitris Angelakis, Errikos Ventouras and Pantelis Asvestas

Eng. Proc. 2023, 50(1), 7; https://doi.org/10.3390/engproc2023050007 - 31 Oct 2023

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Abstract

This research presents a machine learning model that predicts left, right, or no action using electroencephalography (EEG) signals extracted from two different wearable EEG headsets. The research aims to develop an accurate and efficient model by following a rigorous and effective process divided [...] Read more.

This research presents a machine learning model that predicts left, right, or no action using electroencephalography (EEG) signals extracted from two different wearable EEG headsets. The research aims to develop an accurate and efficient model by following a rigorous and effective process divided into two parts. In Part I, the constant features approach is employed, which involves data loading, feature extraction, preprocessing, model selection, and tuning the best model for optimal performance. The performance of classification algorithms (support vector machine (SVM), decision tree classifier, and random forest classifier) is evaluated using root-mean-squared error metrics. In Part II, the multivariate time series approach is utilized to improve the accuracy and robustness of the model. The approach involves data loading, preprocessing (such as normalizing the data), modeling, results analysis, and deployment preparation. A neural network architecture consisting of convolutional filters followed by a long short-term memory neural network (LSTM) is used in the proposed approach. The convolutional layer performs a convolution of an input series of feature maps with a filter matrix to extract high-level features. The LSTM network is specifically designed to capture long-term dependencies and overcome the issue of vanishing gradients. The proposed approach achieves an accuracy of 98% and can be used for real-time testing. The model can be utilized in various fields where accurate and real-time prediction of brain–computer interfaces (BCI) actions is crucial. Overall, the proposed approach provides a promising solution to the problem of action prediction using EEG signals, and further research can be conducted to explore its potential applications and optimize its performance. Full article

(This article belongs to the Proceedings of Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference)

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10 pages, 1000 KiB

Open AccessProceeding Paper

Analysis of Nanodrug Delivery in Blood Flowing through Blood Vessels Using Machine Learning Models

by Spurthi Joanna Selladurai, Neetu Srivastava and Ioannis E. Sarris

Eng. Proc. 2023, 50(1), 8; https://doi.org/10.3390/engproc2023050008 - 31 Oct 2023

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Abstract

This study provides a framework to strategize localized efficient drug delivery in second-order blood flowing through porous blood vessels using machine learning algorithms. With the assumption of long blood vessels, the flow-governing equation, the Navier–Stokes equation, is reduced to a simpler model which [...] Read more.

This study provides a framework to strategize localized efficient drug delivery in second-order blood flowing through porous blood vessels using machine learning algorithms. With the assumption of long blood vessels, the flow-governing equation, the Navier–Stokes equation, is reduced to a simpler model which is consistent with the lubrication theory. We solved this equation analytically with slip conditions and obtained the analytical expression of the velocity profile for the Newtonian model. We modelled the concentration of nanodrugs with an advection diffusion equation to analyze the effect of concentration on the localized disease. The particle concentration at the blood vessel wall was evaluated using the finite-difference method. To analyze the particle concentration, we implemented machine learning algorithms including Gradient Boost, XG Boost, Regression Tree, MLP Regressor, and CatBoost Regressor. Our conclusion predicts the optimum machine learning algorithm for transferring the delivery of the nanoparticle drug. Full article

(This article belongs to the Proceedings of Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference)

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8 pages, 3079 KiB

Open AccessProceeding Paper

Data-Driven Drug Repurposing in Diabetes Mellitus through an Enhanced Knowledge Graph

by Sotiris Ouzounis, Alexandros Kanterakis, Vasilis Panagiotopoulos, Dionisis Cavouras, Panagiotis Zoumpoulakis, Minos-Timotheos Matsoukas, Theodora Katsila and Ioannis Kalatzis

Eng. Proc. 2023, 50(1), 9; https://doi.org/10.3390/engproc2023050009 - 31 Oct 2023

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Abstract

Diabetes mellitus affects more than 400 million people worldwide, and the incidence of disease is rising. Current anti-hyperglycemic agents share major drawbacks, such as hypoglycemia and low potency due to a lack of target specificity. Drug repurposing accelerates drug research and development pipelines [...] Read more.

Diabetes mellitus affects more than 400 million people worldwide, and the incidence of disease is rising. Current anti-hyperglycemic agents share major drawbacks, such as hypoglycemia and low potency due to a lack of target specificity. Drug repurposing accelerates drug research and development pipelines and empowers chemical space enrichment. Herein, we propose a data-driven approach towards drug repurposing in diabetes mellitus by integrating heterogeneous biomedical data in a unified knowledge graph. Through extensive data mining in public repositories, diabetes-related multimodal data have been retrieved. Several data analysis techniques were employed to extract information and define semantic associations, followed by data parsing and, next, descriptive statistics, regression, and cluster analysis. Biomedical entity recognition and negation detection were performed by natural language processing. Predefined biological ontologies served as reference endpoints for class definition upon data integration. Graph analytics were performed, and drug–drug, protein–protein, drug–protein, and drug–disease interactions were established. A majority vote-based machine learning framework for the prediction of human cytochrome P450 inhibitors was also integrated into the proposed enhanced knowledge graph analysis that facilitates data-driven ranking for drug repurposing candidates in diabetes mellitus. The presented method yields a ranked list of repurposing candidates. Full article

(This article belongs to the Proceedings of Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference)

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9 pages, 2016 KiB

Open AccessProceeding Paper

Higher Education of Biomedical Engineering in Greece: Undergraduate Students’ Outcomes from 1989 to 2019

by Panagiotis Liaparinos, Spiros Kostopoulos, Dimitris Glotsos and Ioannis Kalatzis

Eng. Proc. 2023, 50(1), 10; https://doi.org/10.3390/engproc2023050010 - 31 Oct 2023

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Abstract

This manuscript presents the educational evaluation performance of the BME department in Greece. The results are provided in terms of the (i) diploma degree and (ii) duration of studies, enumerating 1845 graduated students in total, over the past 30 years. The following conclusions [...] Read more.

This manuscript presents the educational evaluation performance of the BME department in Greece. The results are provided in terms of the (i) diploma degree and (ii) duration of studies, enumerating 1845 graduated students in total, over the past 30 years. The following conclusions can be drawn: (a) The mean grade value of all time was approximately 6.5; (b) the majority of students (59%) graduated after 7.4 study years with an average grade of 6.1; and (c) the most cost-effective degrees seem to be those that correspond to 5–6 study years for graduation. Full article

(This article belongs to the Proceedings of Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference)

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8 pages, 3680 KiB

Open AccessProceeding Paper

Unraveling Imaginary and Real Motion: A Correlation Indices Study in BCI Data

by Stavros T. Miloulis, Ioannis Zorzos, Ioannis Kakkos, Aikaterini Karampasi, Errikos C. Ventouras, Ioannis Kalatzis, Charalampos Papageorgiou, Panteleimon Asvestas and George K. Matsopoulos

Eng. Proc. 2023, 50(1), 11; https://doi.org/10.3390/engproc2023050011 - 07 Nov 2023

Viewed by 343

Abstract

The efficient translation of brain signals into an output device is an essential characteristic to establish a Brain-computer Interface (BCI) link. This research investigates the applicability of diverse correlation indices for the differentiation of specific movements (left, right, both, or none) and states [...] Read more.

The efficient translation of brain signals into an output device is an essential characteristic to establish a Brain-computer Interface (BCI) link. This research investigates the applicability of diverse correlation indices for the differentiation of specific movements (left, right, both, or none) and states (real or imaginary) in a private BCI dataset, including EEG recordings of 32 participants. As such, the recorded brain activation data were employed to illustrate the differences between visual- and auditory-event-related responses during task performance. Our methodology involved a two-pronged approach. Firstly, EEG data were collected, capturing both the visual- and auditory-event-related signals that corresponded to each of the four movement classes. Secondly, we performed a comparative analysis of the collected dataset using various correlation algorithms, such as Pearson, Spearman, and Kendall, among others, to evaluate their effectiveness in differentiating between movements and states. The results demonstrated distinctive correlation patterns, as the selected indices effectively distinguished between real and imaginary movements, as well as between different lower limp movements in most cases. Moreover, the correlation schemas of certain individuals presented greater sensitivity in discerning nuances within the dataset. In this regard, it can be inferred that the chosen correlation indices can provide valuable insights into the aforementioned differentiation in EEG data. The results open up potential paths for improving BCI interfaces and contributing to more accurate prediction models. Full article

(This article belongs to the Proceedings of Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference)

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8 pages, 1068 KiB

Open AccessProceeding Paper

A Gene Selection Strategy for Enhancing Single-Cell RNA-Seq Data Integration

by Konstantinos Lazaros, Georgios N. Dimitrakopoulos, Panagiotis Vlamos and Aristidis G. Vrahatis

Eng. Proc. 2023, 50(1), 12; https://doi.org/10.3390/engproc2023050012 - 08 Nov 2023

Viewed by 719

Abstract

Cancer remains a pervasive and formidable disease within modern societies, necessitating the utilization of advanced techniques in both diagnosis and therapy. Molecular biology has emerged as a crucial tool in deciphering the underlying biological mechanisms that contribute to various types of cancer. Notably, [...] Read more.

Cancer remains a pervasive and formidable disease within modern societies, necessitating the utilization of advanced techniques in both diagnosis and therapy. Molecular biology has emerged as a crucial tool in deciphering the underlying biological mechanisms that contribute to various types of cancer. Notably, single-cell sequencing has garnered significant attention as a state-of-the-art method for profiling gene expression in individual cells, unveiling previously concealed mechanisms and biological phenomena. With the abundance of single-cell datasets available, there is a pressing need to integrate related datasets into larger ones to enhance our understanding of biological processes and augment predictive capabilities. In this study, we investigated the impact of gene selection, achieved through the implementation of feature selection techniques, on the integration of single-cell datasets. By systematically exploring the effects of gene selection, we aim to enhance the integration process, leading to improved biological insights and enhanced predictive power. The proposed method aims to enhance two cutting-edge data integration methodologies for single-cell RNA sequencing (scRNA-seq). The method utilizes a strategy that combines two key components: a statistical approach to isolate the high variability in gene expression across cells or samples and a feature selection strategy based on XgBoost to keep genes that are important for distinguishing among healthy and cancerous cells. Full article

(This article belongs to the Proceedings of Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference)

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9 pages, 1422 KiB

Open AccessProceeding Paper

Acoustic and Temporal Analysis of Speech for Schizophrenia Management

by Alexantrina Mouratai, Nikolaos Dimopoulos, Athanasios Dimitriadis, Pantelis Koudounas, Dimitris Glotsos and Luis Pinto-Coelho

Eng. Proc. 2023, 50(1), 13; https://doi.org/10.3390/engproc2023050013 - 08 Nov 2023

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Abstract

Currently, there are no established objective biomarkers for diagnosing or monitoring schizophrenia. Studies have shown that there are noteworthy differences in the speech of schizophrenics. The primary goal of the current study is to examine possible acoustic differences in vowel production between Greek [...] Read more.

Currently, there are no established objective biomarkers for diagnosing or monitoring schizophrenia. Studies have shown that there are noteworthy differences in the speech of schizophrenics. The primary goal of the current study is to examine possible acoustic differences in vowel production between Greek speakers with schizophrenia and healthy controls. Eleven Greek speakers with schizophrenia and twelve healthy controls participated in the study. The results showed significant differences between the two groups in F1 and F2 frequencies, in jitter and shimmer as well as in the total length of pauses in spontaneous speech. These can pave the way for future developments toward the detection of disease patterns using inexpensive and non-invasive methods. Full article

(This article belongs to the Proceedings of Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference)

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7 pages, 643 KiB

Open AccessProceeding Paper

Statistical Analysis of a Questionnaire-Based Survey for Assessing the Impact of Tai Ji on the World Health Organization Definitions Regarding Quality of Life (QoL)

by Dimitris Tsolakidis, Despina Kakatsaki and Dimitris Glotsos

Eng. Proc. 2023, 50(1), 14; https://doi.org/10.3390/engproc2023050014 - 16 Nov 2023

Viewed by 809

Abstract

In this study, the aim was to evaluate the impact of Tai Ji on perceived quality of life (QoL). To this end, an anonymous online questionnaire was designed, compatible with the guidelines of the WHO for the definition of QoL, on the Microsoft [...] Read more.

In this study, the aim was to evaluate the impact of Tai Ji on perceived quality of life (QoL). To this end, an anonymous online questionnaire was designed, compatible with the guidelines of the WHO for the definition of QoL, on the Microsoft Forms platform, using multiple-choice questions, short text answers, and Likert-based scales. The questionnaire was made public to practitioners of the Flow Tai Ji Center in Greece. The results showed that Tai Ji greatly improved the overall QoL for most participants, with more than 80% positive opinions. Full article

(This article belongs to the Proceedings of Advances in Biomedical Sciences, Engineering and Technology (ABSET) Conference)

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