152 research outputs found
Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey
Cough acoustics contain multitudes of vital information about
pathomorphological alterations in the respiratory system. Reliable and accurate
detection of cough events by investigating the underlying cough latent features
and disease diagnosis can play an indispensable role in revitalizing the
healthcare practices. The recent application of Artificial Intelligence (AI)
and advances of ubiquitous computing for respiratory disease prediction has
created an auspicious trend and myriad of future possibilities in the medical
domain. In particular, there is an expeditiously emerging trend of Machine
learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting
cough signatures. The enormous body of literature on cough-based AI algorithms
demonstrate that these models can play a significant role for detecting the
onset of a specific respiratory disease. However, it is pertinent to collect
the information from all relevant studies in an exhaustive manner for the
medical experts and AI scientists to analyze the decisive role of AI/ML. This
survey offers a comprehensive overview of the cough data-driven ML/DL detection
and preliminary diagnosis frameworks, along with a detailed list of significant
features. We investigate the mechanism that causes cough and the latent cough
features of the respiratory modalities. We also analyze the customized cough
monitoring application, and their AI-powered recognition algorithms. Challenges
and prospective future research directions to develop practical, robust, and
ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table
Cough Monitoring Through Audio Analysis
The detection of cough events in audio recordings requires the analysis of a significant amount of data as cough is typically monitored continuously over several hours to capture naturally occurring cough events. The recorded data is mostly composed of undesired sound events such as silence, background noise, and speech. To reduce computational costs and to address the ethical concerns raised from the collection of audio data in public environments, the data requires pre-processing prior to any further analysis.
Current cough detection algorithms typically use pre-processing methods to remove undesired audio segments from the collected data but do not preserve the privacy of individuals being recorded while monitoring respiratory events. This study reveals the need for an automatic pre-processing method that removes sensitive data from the recording prior to any further analysis to ensure privacy preservation of individuals.
Specific characteristics of cough sounds can be used to discard sensitive data from audio recordings at a pre-processing stage, improving privacy preservation, and decreasing ethical concerns when dealing with cough monitoring through audio analysis.
We propose a pre-processing algorithm that increases privacy preservation and significantly decreases the amount of data to be analysed, by separating cough segments from other non-cough segments, including speech, in audio recordings. Our method verifies the presence of signal energy in both lower and higher frequency regions and discards segments whose energy concentrates only on one of them. The method is iteratively applied on the same data to increase the percentage of data reduction and privacy preservation.
We evaluated the performance of our algorithm using several hours of audio recordings with manually pre-annotated cough and speech events. Our results showed that 5 iterations of the proposed method can discard up to 88.94% of the speech content present in the recordings, allowing for a strong privacy preservation while considerably reducing the amount of data to be further analysed by 91.79%.
The data reduction and privacy preservation achievements of the proposed pre-processing algorithm offers the possibility to use larger datasets captured in public environments and would beneficiate all cough detection algorithms by preserving the privacy of subjects and by-stander conversations recorded during cough monitoring
An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time
This paper presents an investigation into the development of an intelligent mobile-enabled expert system to perform an automatic detection of tuberculosis (TB) disease in real-time. One third of the global population are infected with the TB bacterium, and the prevailing diagnosis methods are either resource-intensive or time consuming. Thus, a reliable and easy–to-use diagnosis system has become essential to make the world TB free by 2030, as envisioned by the World Health Organisation. In this work, the challenges in implementing an efficient image processing platform is presented to extract the images from plasmonic ELISAs for TB antigen-specific antibodies and analyse their features. The supervised machine learning techniques are utilised to attain binary classification from eighteen lower-order colour moments. The proposed system is trained off-line, followed by testing and validation using a separate set of images in real-time. Using an ensemble classifier, Random Forest, we demonstrated 98.4% accuracy in TB antigen-specific antibody detection on the mobile platform. Unlike the existing systems, the proposed intelligent system with real time processing capabilities and data portability can provide the prediction without any opto-mechanical attachment, which will undergo a clinical test in the next phase.</p
Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment
Objective: The automatic discrimination between the coughing sounds produced
by patients with tuberculosis (TB) and those produced by patients with other
lung ailments.
Approach: We present experiments based on a dataset of 1358 forced cough
recordings obtained in a developing-world clinic from 16 patients with
confirmed active pulmonary TB and 35 patients suffering from respiratory
conditions suggestive of TB but confirmed to be TB negative. Using nested
cross-validation, we have trained and evaluated five machine learning
classifiers: logistic regression (LR), support vector machines (SVM), k-nearest
neighbour (KNN), multilayer perceptrons (MLP) and convolutional neural networks
(CNN).
Main Results: Although classification is possible in all cases, the best
performance is achieved using LR. In combination with feature selection by
sequential forward selection (SFS), our best LR system achieves an area under
the ROC curve (AUC) of 0.94 using 23 features selected from a set of 78
high-resolution mel-frequency cepstral coefficients (MFCCs). This system
achieves a sensitivity of 93\% at a specificity of 95\% and thus exceeds the
90\% sensitivity at 70\% specificity specification considered by the World
Health Organisation (WHO) as a minimal requirement for a community-based TB
triage test.
Significance: The automatic classification of cough audio sounds, when
applied to symptomatic patients requiring investigation for TB, can meet the
WHO triage specifications for the identification of patients who should undergo
expensive molecular downstream testing. This makes it a promising and viable
means of low cost, easily deployable frontline screening for TB, which can
benefit especially developing countries with a heavy TB burden.Comment: This paper has been accepted in Physiological Measurement (2021
An overview of technologies and devices against COVID-19 pandemic diffusion: virus detection and monitoring solutions
none5siThe year 2020 will remain in the history for the diffusion of the COVID-19 virus, originating a pandemic on a world scale with over a million deaths. From the onset of the pandemic, the scientific community has made numerous efforts to design systems to detect the infected subjects in ever-faster times, allowing both to intervene on them, to avoid dangerous complications, and to contain the pandemic spreading. In this paper, we present an overview of different innovative technologies and devices fielded against the SARS-CoV-2
virus. The various technologies applicable to the rapid and reliable detection of the COVID-19 virus have been explored. Specifically, several magnetic, electrochemical, and plasmonic biosensors have been proposed in the scientific literature, as an alternative to nucleic acid-based real-time reverse transcription Polymerase Chain Reaction (PCR) (RT-qPCR) assays, overcoming the limitations featuring this typology of tests (the need for expensive instruments and reagents, as well as of specialized staff, and their reliability).
Furthermore, we investigated the IoT solutions and devices, reported on the market and in the scientific literature, to contain the pandemic spreading, by avoiding the contagion, acquiring the parameters of suspected users, and monitoring them during the quarantine period.openR. de Fazio, A. Sponziello, D. Cafagna, R. Velazquez, P. Viscontide Fazio, R.; Sponziello, A.; Cafagna, D.; Velazquez, R.; Visconti, P
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