Pub Date : 2020-11-17DOI: 10.5220/0010258302080214
K. Egorov, Elena Sokolova, Manvel Avetisian, A. Tuzhilin
Holter monitoring, a long-term ECG recording (24-hours and more), contains a large amount of valuable diagnostic information about the patient. Its interpretation becomes a difficult and time-consuming task for the doctor who analyzes them because every heartbeat needs to be classified, thus requiring highly accurate methods for automatic interpretation. In this paper, we present a three-stage process for analysing Holter recordings with robustness to noisy signal. First stage is a segmentation neural network (NN) with encoderdecoder architecture which detects positions of heartbeats. Second stage is a classification NN which will classify heartbeats as wide or narrow. Third stage in gradient boosting decision trees (GBDT) on top of NN features that incorporates patient-wise features and further increases performance of our approach. As a part of this work we acquired 5095 Holter recordings of patients annotated by an experienced cardiologist. A committee of three cardiologists served as a ground truth annotators for the 291 examples in the test set. We show that the proposed method outperforms the selected baselines, including two commercial-grade software packages and some methods previously published in the literature.
{"title":"Noise-Resilient Automatic Interpretation of Holter ECG Recordings","authors":"K. Egorov, Elena Sokolova, Manvel Avetisian, A. Tuzhilin","doi":"10.5220/0010258302080214","DOIUrl":"https://doi.org/10.5220/0010258302080214","url":null,"abstract":"Holter monitoring, a long-term ECG recording (24-hours and more), contains a large amount of valuable diagnostic information about the patient. Its interpretation becomes a difficult and time-consuming task for the doctor who analyzes them because every heartbeat needs to be classified, thus requiring highly accurate methods for automatic interpretation. In this paper, we present a three-stage process for analysing Holter recordings with robustness to noisy signal. First stage is a segmentation neural network (NN) with encoderdecoder architecture which detects positions of heartbeats. Second stage is a classification NN which will classify heartbeats as wide or narrow. Third stage in gradient boosting decision trees (GBDT) on top of NN features that incorporates patient-wise features and further increases performance of our approach. As a part of this work we acquired 5095 Holter recordings of patients annotated by an experienced cardiologist. A committee of three cardiologists served as a ground truth annotators for the 291 examples in the test set. We show that the proposed method outperforms the selected baselines, including two commercial-grade software packages and some methods previously published in the literature.","PeriodicalId":241968,"journal":{"name":"International Conference on Bio-inspired Systems and Signal Processing","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133576225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-23DOI: 10.5220/0009178703420347
A. Jeremic, D. Nikolić
Recently there has been an increase in the number of long-term cot-bed EEG systems being implemented in clinical practice in order to monitor neurological development of neonatal patients. Consequently a significant research effort has been made in the development of automatic EEG data analysis tools including but not limited to seizure detection as seizure frequency and/or intensity are one of the most important indicators of brain development. In this paper we propose to evaluate time dependent power spectral density using short time Fourier transform and using Frechet distance measure to detect presence and/or absence of seizures. We propose to use three different distance measures as they capture different properties of the corresponding PSD matrices. We evaluate the performance of the proposed algorithms using real data set obtained in the NICU of the McMaster University Hospital. In order to benchmark performance of our proposed techniques we trained and tested a support vector machine (SVM) classifier.
{"title":"Detecting Neonatal Seizures using Short Time Fourier Transform and Frechet Distance","authors":"A. Jeremic, D. Nikolić","doi":"10.5220/0009178703420347","DOIUrl":"https://doi.org/10.5220/0009178703420347","url":null,"abstract":"Recently there has been an increase in the number of long-term cot-bed EEG systems being implemented in clinical practice in order to monitor neurological development of neonatal patients. Consequently a significant research effort has been made in the development of automatic EEG data analysis tools including but not limited to seizure detection as seizure frequency and/or intensity are one of the most important indicators of brain development. In this paper we propose to evaluate time dependent power spectral density using short time Fourier transform and using Frechet distance measure to detect presence and/or absence of seizures. We propose to use three different distance measures as they capture different properties of the corresponding PSD matrices. We evaluate the performance of the proposed algorithms using real data set obtained in the NICU of the McMaster University Hospital. In order to benchmark performance of our proposed techniques we trained and tested a support vector machine (SVM) classifier.","PeriodicalId":241968,"journal":{"name":"International Conference on Bio-inspired Systems and Signal Processing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122302768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-12DOI: 10.5220/0009191401150120
M. Mezghani, N. Hagemeister, M. Kouki, Y. Ouakrim, A. Fuentes, N. Mezghani
The evaluation of knee biomechanics provides valuable clinical information. This can be done by means of a knee kinesiography exam which measures the three-dimensional rotation angles during walking, thus providing objective knowledge about knee function (3D kinematics). 3D kinematic data is quantifiable information that provides opportunities to develop automatic and objective methods for personalized computer-aided treatment systems. The purpose of this study is to explore a decision tree based method for predicting the impact of physical exercise on a knee osteoarthritis population. The prediction is based on 3D kinematic data i.e., flexion/extension, abduction/adduction and internal/external rotation of the knee. Experiments were conducted on a dataset of 309 patients who have engaged in physical exercise for 6 months and have been grouped into two classes, Improved state (I) and not-Improved state (nI) based on their state before (t0) and after the exercise (t6). The method developed was able to predict I and nI patien with knee osteoarthritis using 3D kinematic data with an accuracy of 82%. Results show the effectiveness of 3D kinematic signal analysis and the decision tree technique for predicting the impact of physical exercise based on patient knee osteoarthritis pain level.
{"title":"Prediction of the Impact of Physical Exercise on Knee Osteoarthritis Patients using Kinematic Signal Analysis and Decision Trees","authors":"M. Mezghani, N. Hagemeister, M. Kouki, Y. Ouakrim, A. Fuentes, N. Mezghani","doi":"10.5220/0009191401150120","DOIUrl":"https://doi.org/10.5220/0009191401150120","url":null,"abstract":"The evaluation of knee biomechanics provides valuable clinical information. This can be done by means of a knee kinesiography exam which measures the three-dimensional rotation angles during walking, thus providing objective knowledge about knee function (3D kinematics). 3D kinematic data is quantifiable information that provides opportunities to develop automatic and objective methods for personalized computer-aided treatment systems. The purpose of this study is to explore a decision tree based method for predicting the impact of physical exercise on a knee osteoarthritis population. The prediction is based on 3D kinematic data i.e., flexion/extension, abduction/adduction and internal/external rotation of the knee. Experiments were conducted on a dataset of 309 patients who have engaged in physical exercise for 6 months and have been grouped into two classes, Improved state (I) and not-Improved state (nI) based on their state before (t0) and after the exercise (t6). The method developed was able to predict I and nI patien with knee osteoarthritis using 3D kinematic data with an accuracy of 82%. Results show the effectiveness of 3D kinematic signal analysis and the decision tree technique for predicting the impact of physical exercise based on patient knee osteoarthritis pain level.","PeriodicalId":241968,"journal":{"name":"International Conference on Bio-inspired Systems and Signal Processing","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116636350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-12DOI: 10.5220/0008967302360243
J. Pestana, David Belo, H. Gamboa
The Electrocardiogram (ECG) cyclic behaviour gives insights on a subject’s emotional, behavioral and cardiovascular state, but often presents abnormal events. The noise made during the acquisition, and presence of symptomatic patterns are examples of anomalies. The proposed Deep Learning framework learns the normal ECG cycles and detects its deviation when the morphology changes. This technology is tested in two different settings having an autoencoder as base for learning features: detection of three different types of noise, and detection of six arrhythmia events. Two Convolutional Neural Network (CNN) algorithms were developed for noise detection achieving accuracies of 98.18% for a binary-class model and 70.74% for a multi-class model. The development of the arrhythmia detection algorithm also included a Gated Recurrent Unit (GRU) for grasping time-dependencies reaching an accuracy of 56.85% and an average sensitivity of 61.13%. The process of learning the abstraction of a ECG signal, currently sacrifices the accuracy for higher generalization, better discriminating the presence of abnormal events in ECG than detecting different types of events. Further improvement could represent a major contribution in symptomatic screening, active learning of unseen events and the study of pathologies to support physicians in the future.
{"title":"Detection of Abnormalities in Electrocardiogram (ECG) using Deep Learning","authors":"J. Pestana, David Belo, H. Gamboa","doi":"10.5220/0008967302360243","DOIUrl":"https://doi.org/10.5220/0008967302360243","url":null,"abstract":"The Electrocardiogram (ECG) cyclic behaviour gives insights on a subject’s emotional, behavioral and cardiovascular state, but often presents abnormal events. The noise made during the acquisition, and presence of symptomatic patterns are examples of anomalies. The proposed Deep Learning framework learns the normal ECG cycles and detects its deviation when the morphology changes. This technology is tested in two different settings having an autoencoder as base for learning features: detection of three different types of noise, and detection of six arrhythmia events. Two Convolutional Neural Network (CNN) algorithms were developed for noise detection achieving accuracies of 98.18% for a binary-class model and 70.74% for a multi-class model. The development of the arrhythmia detection algorithm also included a Gated Recurrent Unit (GRU) for grasping time-dependencies reaching an accuracy of 56.85% and an average sensitivity of 61.13%. The process of learning the abstraction of a ECG signal, currently sacrifices the accuracy for higher generalization, better discriminating the presence of abnormal events in ECG than detecting different types of events. Further improvement could represent a major contribution in symptomatic screening, active learning of unseen events and the study of pathologies to support physicians in the future.","PeriodicalId":241968,"journal":{"name":"International Conference on Bio-inspired Systems and Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129972395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-12DOI: 10.5220/0009368903770381
Minhong Wang, A. Tsanas, G. Blin, D. Robertson
Embryonic stem cells (ESCs) studies play an important role for understanding the molecular events that underlie cell lineage commitment and serve as models for the development of disease. However, the interactions between neighboring embryonic stem cells are not fully understood. Assessing proximity between different types of embryonic stem cells might provide more information about distinct behaviors of embryonic stem cells. In this study, we processed 186 cell colonies on disc constrained microdomains and 152 cell colonies on ellipse. We grouped cell colonies based on different observed patterns and grouped cells by their locations. By applying two measurements on embryonic stem cell colonies, minimum spanning tree and average distance to the five closest objects, we investigated the difference of proximity between different types of embryonic stem cells, the difference between grouped cell colonies and the difference between grouped cells. We found one type of ESC has a smaller average path based on minimum spanning tree and higher proximity than the other type. We report consistent results for different types of embryonic stem cells: these findings may be useful to set benchmarks for empirical models which replicate ESC behaviors.
{"title":"Assessing Preferred Proximity Between Different Types of Embryonic Stem Cells","authors":"Minhong Wang, A. Tsanas, G. Blin, D. Robertson","doi":"10.5220/0009368903770381","DOIUrl":"https://doi.org/10.5220/0009368903770381","url":null,"abstract":"Embryonic stem cells (ESCs) studies play an important role for understanding the molecular events that underlie cell lineage commitment and serve as models for the development of disease. However, the interactions between neighboring embryonic stem cells are not fully understood. Assessing proximity between different types of embryonic stem cells might provide more information about distinct behaviors of embryonic stem cells. In this study, we processed 186 cell colonies on disc constrained microdomains and 152 cell colonies on ellipse. We grouped cell colonies based on different observed patterns and grouped cells by their locations. By applying two measurements on embryonic stem cell colonies, minimum spanning tree and average distance to the five closest objects, we investigated the difference of proximity between different types of embryonic stem cells, the difference between grouped cell colonies and the difference between grouped cells. We found one type of ESC has a smaller average path based on minimum spanning tree and higher proximity than the other type. We report consistent results for different types of embryonic stem cells: these findings may be useful to set benchmarks for empirical models which replicate ESC behaviors.","PeriodicalId":241968,"journal":{"name":"International Conference on Bio-inspired Systems and Signal Processing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114700340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-12DOI: 10.5220/0008942602060213
X. Bao, Yansha Deng, N. Gall, E. Kamavuako
Phonocardiogram (PCG) and Electrocardiogram (ECG) are the two important signals for cardiac preliminary diagnosis. Using ECG as a reference for segmenting the PCG signal is a simple but reliable technique for the devices with integration capability of PCG and ECG recording. The aim of this work is to analyse the time delay between ECG and PCG at each auscultation site. To do so, we performed the experiments on 12 healthy subjects, where the ECG and PCG signals were collected simultaneously at two sites at each time. Our results reveal that 1) the inter-distance of the electrodes for ECG does not affect the occurrence time of the R-peak. 2) The delay between R-peak and onset of first heart sound (S1) depends on the auscultation site e.g. S1 onset occurs before the R-peak at auscultation site M. This study suggests that small integrated ECG-PCG devices can be made by reducing the distance between the ECG electrodes. In the meantime, distinguishing the auscultation location is necessary for performing more precise PCG segmentation using ECG as reference.
心音图(PCG)和心电图(ECG)是心脏初步诊断的两个重要信号。以心电为参考对心电信号进行分割是一种简单而可靠的技术，具有心电记录与心电记录的集成能力。本工作的目的是分析各听诊部位心电图和PCG之间的时间延迟。为此，我们对12名健康受试者进行了实验，每次在两个部位同时采集ECG和PCG信号。结果表明:1)心电图电极间距对r峰发生时间没有影响。2) r -峰值与第一心音(S1)发生的延迟取决于听诊部位，例如S1发生在听诊部位m的r -峰值之前。本研究建议通过缩短ECG电极之间的距离可以制造小型集成ECG- pcg设备。同时，识别听诊位置对于以心电为参考进行更精确的PCG分割是必要的。
{"title":"Analysis of ECG and PCG Time Delay around Auscultation Sites","authors":"X. Bao, Yansha Deng, N. Gall, E. Kamavuako","doi":"10.5220/0008942602060213","DOIUrl":"https://doi.org/10.5220/0008942602060213","url":null,"abstract":"Phonocardiogram (PCG) and Electrocardiogram (ECG) are the two important signals for cardiac preliminary diagnosis. Using ECG as a reference for segmenting the PCG signal is a simple but reliable technique for the devices with integration capability of PCG and ECG recording. The aim of this work is to analyse the time delay between ECG and PCG at each auscultation site. To do so, we performed the experiments on 12 healthy subjects, where the ECG and PCG signals were collected simultaneously at two sites at each time. Our results reveal that 1) the inter-distance of the electrodes for ECG does not affect the occurrence time of the R-peak. 2) The delay between R-peak and onset of first heart sound (S1) depends on the auscultation site e.g. S1 onset occurs before the R-peak at auscultation site M. This study suggests that small integrated ECG-PCG devices can be made by reducing the distance between the ECG electrodes. In the meantime, distinguishing the auscultation location is necessary for performing more precise PCG segmentation using ECG as reference.","PeriodicalId":241968,"journal":{"name":"International Conference on Bio-inspired Systems and Signal Processing","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132527928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-12DOI: 10.5220/0008876700380048
Shiva Khoshnoud, M. Nazari, M. Shamsi
Electroencephalography recordings have a scale-invariant structure and multifractal detrended fluctuation analysis (MF-DFA) could quantify the fluctuation dynamics of these recordings in different brain states. However, the channel-based electrical activity of the brain has low spatial resolution and considering the source-level activity patterns is a good answer for this restriction. In this work, the multifractal spectrum parameters of the channel-based EEG, as well as the corresponding source-based independent components in children with Attention Deficit Hyperactivity Disorder (ADHD) and the age-matched control group, has been investigated. Considering the perceptual timing deficit in children with ADHD, for the analysis of the multifractality, two brain states including the eyes-open rest and the time reproduction condition have been considered. The results obtained showed that switching from rest to the time reproduction condition increases the degree of multifractality and so the complexity and non-uniformity of the signal. While the channel-based multifractal properties could not significantly distinguish two groups, the results for the source-based multifractal analysis showed a significantly decreased degree of multifractality for children with ADHD in prefrontal, mid-frontal and right frontal source clusters suggesting reduced activation of these clusters in this group. Utilizing support vector machine (SVM) classifier it is found that, the sourcebased multifractal features provide a significantly higher accuracy rate of 86.67% in comparison to the channel-based measures.
{"title":"Source-based Multifractal Detrended Fluctuation Analysis for Discrimination of ADHD Children in a Time Reproduction Paradigm","authors":"Shiva Khoshnoud, M. Nazari, M. Shamsi","doi":"10.5220/0008876700380048","DOIUrl":"https://doi.org/10.5220/0008876700380048","url":null,"abstract":"Electroencephalography recordings have a scale-invariant structure and multifractal detrended fluctuation analysis (MF-DFA) could quantify the fluctuation dynamics of these recordings in different brain states. However, the channel-based electrical activity of the brain has low spatial resolution and considering the source-level activity patterns is a good answer for this restriction. In this work, the multifractal spectrum parameters of the channel-based EEG, as well as the corresponding source-based independent components in children with Attention Deficit Hyperactivity Disorder (ADHD) and the age-matched control group, has been investigated. Considering the perceptual timing deficit in children with ADHD, for the analysis of the multifractality, two brain states including the eyes-open rest and the time reproduction condition have been considered. The results obtained showed that switching from rest to the time reproduction condition increases the degree of multifractality and so the complexity and non-uniformity of the signal. While the channel-based multifractal properties could not significantly distinguish two groups, the results for the source-based multifractal analysis showed a significantly decreased degree of multifractality for children with ADHD in prefrontal, mid-frontal and right frontal source clusters suggesting reduced activation of these clusters in this group. Utilizing support vector machine (SVM) classifier it is found that, the sourcebased multifractal features provide a significantly higher accuracy rate of 86.67% in comparison to the channel-based measures.","PeriodicalId":241968,"journal":{"name":"International Conference on Bio-inspired Systems and Signal Processing","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114490458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-12DOI: 10.5220/0008924400680078
R. Zerafa, T. Camilleri, O. Falzon, K. Camilleri
This work investigates a novel autoregressive multiple model (AR-MM) probabilistic framework for the detection of steady-state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The proposed method is compared to standard SSVEP detection techniques using a 12-class SSVEP dataset recorded from 10 subjects. The results, obtained from a single-channel analysis, reveal that the AR-MM probabilistic framework significantly improves the SSVEP detection performance compared to the standard single-channel power spectral density analysis (PSDA) method. Specifically, an average classification accuracy of 82.02 ± 16.21 % and an information transfer rate (ITR) of 48.22 ± 17.25 bpm are obtained with a 2 s period for SSVEP detection with the AR-MM probabilistic framework. These results are found to be on average only 2.29 % and 3.73 % lower in classification accuracy compared to the state-of-the-art multichannel SSVEP detection methods, specifically the canonical correlation analysis (CCA) and the filter bank canonical correlation analysis (FBCCA) methods, respectively. In terms of training, it is shown that the proposed approach requires only a few seconds of data to train each model. This study revealed the potential of using the AR-MM probabilistic approach to distinguish between different classes using single-channel SSVEP data. The proposed method is particularly appealing for practical use in real-world BCI applications where a minimal amount of channels and training data are desirable.
{"title":"An Autoregressive Multiple Model Probabilistic Framework for the Detection of SSVEPs in Brain-Computer Interfaces","authors":"R. Zerafa, T. Camilleri, O. Falzon, K. Camilleri","doi":"10.5220/0008924400680078","DOIUrl":"https://doi.org/10.5220/0008924400680078","url":null,"abstract":"This work investigates a novel autoregressive multiple model (AR-MM) probabilistic framework for the detection of steady-state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The proposed method is compared to standard SSVEP detection techniques using a 12-class SSVEP dataset recorded from 10 subjects. The results, obtained from a single-channel analysis, reveal that the AR-MM probabilistic framework significantly improves the SSVEP detection performance compared to the standard single-channel power spectral density analysis (PSDA) method. Specifically, an average classification accuracy of 82.02 ± 16.21 % and an information transfer rate (ITR) of 48.22 ± 17.25 bpm are obtained with a 2 s period for SSVEP detection with the AR-MM probabilistic framework. These results are found to be on average only 2.29 % and 3.73 % lower in classification accuracy compared to the state-of-the-art multichannel SSVEP detection methods, specifically the canonical correlation analysis (CCA) and the filter bank canonical correlation analysis (FBCCA) methods, respectively. In terms of training, it is shown that the proposed approach requires only a few seconds of data to train each model. This study revealed the potential of using the AR-MM probabilistic approach to distinguish between different classes using single-channel SSVEP data. The proposed method is particularly appealing for practical use in real-world BCI applications where a minimal amount of channels and training data are desirable.","PeriodicalId":241968,"journal":{"name":"International Conference on Bio-inspired Systems and Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131149642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-12DOI: 10.5220/0009149402960300
H. Hussein, S. Kandil, Khadeeja Amr
{"title":"Fractional Order Analysis of the Activator Model for Gene Regulation Process","authors":"H. Hussein, S. Kandil, Khadeeja Amr","doi":"10.5220/0009149402960300","DOIUrl":"https://doi.org/10.5220/0009149402960300","url":null,"abstract":"","PeriodicalId":241968,"journal":{"name":"International Conference on Bio-inspired Systems and Signal Processing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133266012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-11DOI: 10.5220/0009361203690376
A. Tsanas, S. Arora
: Progress in exploring speech and Parkinson’s Disease (PD) has been hindered due to the use of different protocols across research labs/countries, single-site studies with relatively small numbers, and no external validation. We had recently reported on the Parkinson’s Voice Initiative (PVI), a large study where we collected 19,000+ sustained vowel phonations (control and PD groups) across seven countries, under acoustically non-controlled conditions. In this study, we explored how well findings generalize in the three English-speaking PVI cohorts (data collected in Boston, Oxford, and Toronto). We acoustically characterized each sustained vowel /a/ phonation using 307 dysphonia measures which had previously been successfully employed in speech-PD applications. We used the previously identified feature subset from the Boston cohort and explored hierarchical clustering with Ward’s linkage combined with 2D-data projections using t-distributed stochastic neighbor embedding to facilitate visual exploration of PD subgroups. Furthermore, we computed feature weights using LOGO to assess feature selection consistency towards differentiating PD from controls. Overall, findings are very consistent across the three cohorts, strongly suggesting the presence of four main PD clusters, and consistent identification of key contributing features. Collectively, these findings support the generalization of sustained vowels and robustness of the presented methodology across the English-speaking PVI cohorts.
{"title":"Large-scale Clustering of People Diagnosed with Parkinson's Disease using Acoustic Analysis of Sustained Vowels: Findings in the Parkinson's Voice Initiative Study","authors":"A. Tsanas, S. Arora","doi":"10.5220/0009361203690376","DOIUrl":"https://doi.org/10.5220/0009361203690376","url":null,"abstract":": Progress in exploring speech and Parkinson’s Disease (PD) has been hindered due to the use of different protocols across research labs/countries, single-site studies with relatively small numbers, and no external validation. We had recently reported on the Parkinson’s Voice Initiative (PVI), a large study where we collected 19,000+ sustained vowel phonations (control and PD groups) across seven countries, under acoustically non-controlled conditions. In this study, we explored how well findings generalize in the three English-speaking PVI cohorts (data collected in Boston, Oxford, and Toronto). We acoustically characterized each sustained vowel /a/ phonation using 307 dysphonia measures which had previously been successfully employed in speech-PD applications. We used the previously identified feature subset from the Boston cohort and explored hierarchical clustering with Ward’s linkage combined with 2D-data projections using t-distributed stochastic neighbor embedding to facilitate visual exploration of PD subgroups. Furthermore, we computed feature weights using LOGO to assess feature selection consistency towards differentiating PD from controls. Overall, findings are very consistent across the three cohorts, strongly suggesting the presence of four main PD clusters, and consistent identification of key contributing features. Collectively, these findings support the generalization of sustained vowels and robustness of the presented methodology across the English-speaking PVI cohorts.","PeriodicalId":241968,"journal":{"name":"International Conference on Bio-inspired Systems and Signal Processing","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131961083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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