8 research outputs found
A distributed architecture for the monitoring and analysis of time series data
It is estimated that the quantity of digital data being transferred, processed or stored at any one time currently stands at 4.4 zettabytes (4.4 × 2 70 bytes) and this figure is expected to have grown by a factor of 10 to 44 zettabytes by 2020. Exploiting this data is, and will remain, a significant challenge. At present there is the capacity to store 33% of digital data in existence at any one time; by 2020 this capacity is expected to fall to 15%. These statistics suggest that, in the era of Big Data, the identification of important, exploitable data will need to be done in a timely manner. Systems for the monitoring and analysis of data, e.g. stock markets, smart grids and sensor networks, can be made up of massive numbers of individual components. These components can be geographically distributed yet may interact with one another via continuous data streams, which in turn may affect the state of the sender or receiver. This introduces a dynamic causality, which further complicates the overall system by introducing a temporal constraint that is difficult to accommodate. Practical approaches to realising the system described above have led to a multiplicity of analysis techniques, each of which concentrates on specific characteristics of the system being analysed and treats these characteristics as the dominant component affecting the results being sought. The multiplicity of analysis techniques introduces another layer of heterogeneity, that is heterogeneity of approach, partitioning the field to the extent that results from one domain are difficult to exploit in another. The question is asked can a generic solution for the monitoring and analysis of data that: accommodates temporal constraints; bridges the gap between expert knowledge and raw data; and enables data to be effectively interpreted and exploited in a transparent manner, be identified? The approach proposed in this dissertation acquires, analyses and processes data in a manner that is free of the constraints of any particular analysis technique, while at the same time facilitating these techniques where appropriate. Constraints are applied by defining a workflow based on the production, interpretation and consumption of data. This supports the application of different analysis techniques on the same raw data without the danger of incorporating hidden bias that may exist. To illustrate and to realise this approach a software platform has been created that allows for the transparent analysis of data, combining analysis techniques with a maintainable record of provenance so that independent third party analysis can be applied to verify any derived conclusions. In order to demonstrate these concepts, a complex real world example involving the near real-time capturing and analysis of neurophysiological data from a neonatal intensive care unit (NICU) was chosen. A system was engineered to gather raw data, analyse that data using different analysis techniques, uncover information, incorporate that information into the system and curate the evolution of the discovered knowledge. The application domain was chosen for three reasons: firstly because it is complex and no comprehensive solution exists; secondly, it requires tight interaction with domain experts, thus requiring the handling of subjective knowledge and inference; and thirdly, given the dearth of neurophysiologists, there is a real world need to provide a solution for this domai
Linguistic and Gender Variation in Speech Emotion Recognition using Spectral Features
This work explores the effect of gender and linguistic-based vocal variations
on the accuracy of emotive expression classification. Emotive expressions are
considered from the perspective of spectral features in speech (Mel-frequency
Cepstral Coefficient, Melspectrogram, Spectral Contrast). Emotions are
considered from the perspective of Basic Emotion Theory. A convolutional neural
network is utilised to classify emotive expressions in emotive audio datasets
in English, German, and Italian. Vocal variations for spectral features
assessed by (i) a comparative analysis identifying suitable spectral features,
(ii) the classification performance for mono, multi and cross-lingual emotive
data and (iii) an empirical evaluation of a machine learning model to assess
the effects of gender and linguistic variation on classification accuracy. The
results showed that spectral features provide a potential avenue for increasing
emotive expression classification. Additionally, the accuracy of emotive
expression classification was high within mono and cross-lingual emotive data,
but poor in multi-lingual data. Similarly, there were differences in
classification accuracy between gender populations. These results demonstrate
the importance of accounting for population differences to enable accurate
speech emotion recognition.Comment: Presented at AICS 2021 Conference - Machine Learning for Time Series
Section Published in CEUR Vol-3105 http://ceur-ws.org/Vol-3105/paper34.pdf
This publication has emanated from research supported in part by a Grant from
Science Foundation Ireland under Grant number 18/CRT/6222 Associated source
code https://github.com/ZacDair/SER_Platform_AICS 12 Pages, 5 Figure
Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images
Biomedical image datasets can be imbalanced due to the rarity of targeted
diseases. Generative Adversarial Networks play a key role in addressing this
imbalance by enabling the generation of synthetic images to augment datasets.
It is important to generate synthetic images that incorporate a diverse range
of features to accurately represent the distribution of features present in the
training imagery. Furthermore, the absence of diverse features in synthetic
images can degrade the performance of machine learning classifiers. The mode
collapse problem impacts Generative Adversarial Networks' capacity to generate
diversified images. Mode collapse comes in two varieties: intra-class and
inter-class. In this paper, both varieties of the mode collapse problem are
investigated, and their subsequent impact on the diversity of synthetic X-ray
images is evaluated. This work contributes an empirical demonstration of the
benefits of integrating the adaptive input-image normalization with the Deep
Convolutional GAN and Auxiliary Classifier GAN to alleviate the mode collapse
problems. Synthetically generated images are utilized for data augmentation and
training a Vision Transformer model. The classification performance of the
model is evaluated using accuracy, recall, and precision scores. Results
demonstrate that the DCGAN and the ACGAN with adaptive input-image
normalization outperform the DCGAN and ACGAN with un-normalized X-ray images as
evidenced by the superior diversity scores and classification scores.Comment: Submitted to the Elsevier Journa
Classification of Stress via Ambulatory ECG and GSR Data
In healthcare, detecting stress and enabling individuals to monitor their
mental health and wellbeing is challenging. Advancements in wearable technology
now enable continuous physiological data collection. This data can provide
insights into mental health and behavioural states through psychophysiological
analysis. However, automated analysis is required to provide timely results due
to the quantity of data collected. Machine learning has shown efficacy in
providing an automated classification of physiological data for health
applications in controlled laboratory environments. Ambulatory uncontrolled
environments, however, provide additional challenges requiring further
modelling to overcome. This work empirically assesses several approaches
utilising machine learning classifiers to detect stress using physiological
data recorded in an ambulatory setting with self-reported stress annotations. A
subset of the training portion SMILE dataset enables the evaluation of
approaches before submission. The optimal stress detection approach achieves
90.77% classification accuracy, 91.24 F1-Score, 90.42 Sensitivity and 91.08
Specificity, utilising an ExtraTrees classifier and feature imputation methods.
Meanwhile, accuracy on the challenge data is much lower at 59.23% (submission
#54 from BEaTS-MTU, username ZacDair). The cause of the performance disparity
is explored in this work.Comment: Associated Code to enable reproducible experimental work -
https://github.com/ZacDair/EMBC_Release SMILE dataset provided by
Computational Wellbeing Group (COMPWELL)
https://compwell.rice.edu/workshops/embc2022/dataset -
https://compwell.rice.edu
Variance in Classifying Affective State via Electrocardiogram and Photoplethysmography
Advances in wearable technology have significantly increased the sensitivity
and accuracy of devices for recording physiological signals. Commercial
off-the-shelf wearable devices can gather large quantities of physiological
data un-obtrusively. This enables momentary assessments of human physiology,
which provide valuable insights into an individual's health and psychological
state. Leveraging these insights provides significant benefits for
human-to-computer interaction and personalised healthcare. This work
contributes an analysis of variance occurring in features representative of
affective states extracted from electrocardiograms and photoplethysmography;
subsequently identifies the cardiac measures most descriptive of affective
states from both signals and provides insights into signal and emotion-specific
cardiac measures; finally baseline performance for automated affective state
detection from physiological signals is established.Comment: Associated source code https://github.com/ZacDair/Emo_Phys_Eva
A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis
In biomedical image analysis, the applicability of deep learning methods is
directly impacted by the quantity of image data available. This is due to deep
learning models requiring large image datasets to provide high-level
performance. Generative Adversarial Networks (GANs) have been widely utilized
to address data limitations through the generation of synthetic biomedical
images. GANs consist of two models. The generator, a model that learns how to
produce synthetic images based on the feedback it receives. The discriminator,
a model that classifies an image as synthetic or real and provides feedback to
the generator. Throughout the training process, a GAN can experience several
technical challenges that impede the generation of suitable synthetic imagery.
First, the mode collapse problem whereby the generator either produces an
identical image or produces a uniform image from distinct input features.
Second, the non-convergence problem whereby the gradient descent optimizer
fails to reach a Nash equilibrium. Thirdly, the vanishing gradient problem
whereby unstable training behavior occurs due to the discriminator achieving
optimal classification performance resulting in no meaningful feedback being
provided to the generator. These problems result in the production of synthetic
imagery that is blurry, unrealistic, and less diverse. To date, there has been
no survey article outlining the impact of these technical challenges in the
context of the biomedical imagery domain. This work presents a review and
taxonomy based on solutions to the training problems of GANs in the biomedical
imaging domain. This survey highlights important challenges and outlines future
research directions about the training of GANs in the domain of biomedical
imagery.Comment: Submitted to the Journa
Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery
Publicly available diabetic retinopathy (DR) datasets are imbalanced,
containing limited numbers of images with DR. This imbalance contributes to
overfitting when training machine learning classifiers. The impact of this
imbalance is exacerbated as the severity of the DR stage increases, affecting
the classifiers' diagnostic capacity. The imbalance can be addressed using
Generative Adversarial Networks (GANs) to augment the datasets with synthetic
images. Generating synthetic images is advantageous if high-quality and
diversified images are produced. To evaluate the quality and diversity of
synthetic images, several evaluation metrics, such as Multi-Scale Structural
Similarity Index (MS-SSIM), Cosine Distance (CD), and Fr\'echet Inception
Distance (FID) are used. Understanding the effectiveness of each metric in
evaluating the quality and diversity of GAN-based synthetic images is critical
to select images for augmentation. To date, there has been limited analysis of
the appropriateness of these metrics in the context of biomedical imagery. This
work contributes an empirical assessment of these evaluation metrics as applied
to synthetic Proliferative DR imagery generated by a Deep Convolutional GAN
(DCGAN). Furthermore, the metrics' capacity to indicate the quality and
diversity of synthetic images and a correlation with classifier performance is
undertaken. This enables a quantitative selection of synthetic imagery and an
informed augmentation strategy. Results indicate that FID is suitable for
evaluating the quality, while MS-SSIM and CD are suitable for evaluating the
diversity of synthetic imagery. Furthermore, the superior performance of
Convolutional Neural Network (CNN) and EfficientNet classifiers, as indicated
by the F1 and AUC scores, for the augmented datasets demonstrates the efficacy
of synthetic imagery to augment the imbalanced dataset.Comment: 29 Pages, 8 Figures, submitted to MEDAL23: Advances in Deep
Generative Models for Medical Artificial Intelligence (Springer Nature
series