10,495 research outputs found
Convolutional neural network for breathing phase detection in lung sounds
We applied deep learning to create an algorithm for breathing phase detection
in lung sound recordings, and we compared the breathing phases detected by the
algorithm and manually annotated by two experienced lung sound researchers. Our
algorithm uses a convolutional neural network with spectrograms as the
features, removing the need to specify features explicitly. We trained and
evaluated the algorithm using three subsets that are larger than previously
seen in the literature. We evaluated the performance of the method using two
methods. First, discrete count of agreed breathing phases (using 50% overlap
between a pair of boxes), shows a mean agreement with lung sound experts of 97%
for inspiration and 87% for expiration. Second, the fraction of time of
agreement (in seconds) gives higher pseudo-kappa values for inspiration
(0.73-0.88) than expiration (0.63-0.84), showing an average sensitivity of 97%
and an average specificity of 84%. With both evaluation methods, the agreement
between the annotators and the algorithm shows human level performance for the
algorithm. The developed algorithm is valid for detecting breathing phases in
lung sound recordings
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
Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds.
COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds
2D respiratory sound analysis to detect lung abnormalities
In this paper, we analyze deep visual features from 2D data representation(s) of the respiratory sound to detect evidence of lung abnormalities. The primary motivation behind this is that visual cues are more important in decision-making than raw data (lung sound). Early detection and prompt treatments are essential for any future possible respiratory disorders, and respiratory sound is proven to be one of the biomarkers. In contrast to state-of-the-art approaches, we aim at understanding/analyzing visual features using our Convolutional Neural Networks (CNN) tailored Deep Learning Models, where we consider all possible 2D data such as Spectrogram, Mel-frequency Cepstral Coefficients (MFCC), spectral centroid, and spectral roll-off. In our experiments, using the publicly available respiratory sound database named ICBHI 2017 (5.5 hours of recordings containing 6898 respiratory cycles from 126 subjects), we received the highest performance with the area under the curve of 0.79 from Spectrogram as opposed to 0.48 AUC from the raw data from a pre-trained deep learning model: VGG16. We also used machine learning algorithms using reliable data to improve Our study proved that 2D data representation could help better understand/analyze lung abnormalities as compared to 1D data. Our findings are also contrasted with those of earlier studies. For purposes of generality, we used the MFCC of neutrinos to determine if picture data or raw data produced superior results
Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1
A reliable, remote, and continuous real-time respiratory sound monitor with
automated respiratory sound analysis ability is urgently required in many
clinical scenarios-such as in monitoring disease progression of coronavirus
disease 2019-to replace conventional auscultation with a handheld stethoscope.
However, a robust computerized respiratory sound analysis algorithm has not yet
been validated in practical applications. In this study, we developed a lung
sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds
(duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels,
13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze
labels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous
adventitious sound labels (all crackles). We conducted benchmark tests for long
short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM
(BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM,
CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and
adventitious sound detection. We also conducted a performance comparison
between the LSTM-based and GRU-based models, between unidirectional and
bidirectional models, and between models with and without a CNN. The results
revealed that these models exhibited adequate performance in lung sound
analysis. The GRU-based models outperformed, in terms of F1 scores and areas
under the receiver operating characteristic curves, the LSTM-based models in
most of the defined tasks. Furthermore, all bidirectional models outperformed
their unidirectional counterparts. Finally, the addition of a CNN improved the
accuracy of lung sound analysis, especially in the CAS detection tasks.Comment: 48 pages, 8 figures. To be submitte
Deep Learning for Predicting Congestive Heart Failure
Congestive heart failure (CHF) is one of the most debilitating cardiac disorders. It is a costly disease in terms of both lives and financial outlays, given the high rate of hospital re-admissions and mortality. Heart failure (HF) is notoriously difficult to identify on time, and is frequently accompanied by additional comorbidities that further complicate diagnosis. Many decision support systems (DSS) have been developed to facilitate diagnosis and to raise the standard of screening and monitoring operations, even for non-expert staff. This is confirmed in the literature by records of highly performing diagnosis-aid systems, which are unfortunately not very relevant to expert cardiologists. In order to assist cardiologists in predicting the trajectory of HF, we propose a deep learning-based system which predicts severity of disease progression by employing medical patient history. We tested the accuracy of four models on a labeled dataset, composed of 1037 records, to predict CHF severity and progression, achieving results comparable to studies based on much larger datasets, none of which used longitudinal multi-class prediction. The main contribution of this work is that it demonstrates that a fairly complicated approach can achieve good results on a medium size dataset, providing a reasonably accurate means of determining the evolution of CHF well in advance. This potentially constitutes a significant aid for healthcare managers and expert cardiologists in designing different therapies for medication, healthy lifestyle changes and quality of life (QoL) management, while also promoting allocation of resources with an evidence-based approach. © 2022 by the authors
Towards a tricorder: clinical, health economic, and ethical investigation of point-of-care artificial intelligence electrocardiogram for heart failure
Heart failure (HF) is an international public health priority and a focus of the NHS Long Term
Plan. There is a particular need in primary care for screening and early detection of heart failure with
reduced ejection fraction (HFrEF) – the most common and serious HF subtype, and the only one
with an abundant evidence base for effective therapies. Digital health technologies (DHTs) integrating
artificial intelligence (AI) could improve diagnosis of HFrEF. Specifically, through a convergence of
DHTs and AI, a single-lead electrocardiogram (ECG) can be recorded by a smart stethoscope and
interrogated by AI (AI-ECG) to potentially serve as a point-of-care HFrEF test. However, there are
concerning evidence gaps for such DHTs applying AI; across intersecting clinical, health economic,
and ethical considerations. My thesis therefore investigates hypotheses that AI-ECG is 1.) Reliable,
accurate, unbiased, and can be patient self-administered, 2.) Of justifiable health economic impact for
primary care deployment, and 3.) Appropriate across ethical domains for deployment as a tool for
patient self-administered screening. The theoretical basis for this work is presented in the Introduction (Chapter 1). Chapter 2 describes the first large-scale, multi-centre independent external validation study of AI-ECG, prospectively recruiting 1,050 patients and highlighting impressive performance: area under the curve, sensitivity, and specificity up to 0·91 (95% confidence interval: 0·88–0·95), 91·9% (78·1–98·3), and
80·2% (75·5–84·3) respectively; and absence of bias by age, sex, and ethnicity. Performance was
independent of operator, and usability of the tool extended to patients being able to self-examine.
Chapter 3 presents a clinical and health economic outcomes analysis using a contemporary digital
repository of 2.5 million NHS patient records. A propensity-matched cohort was derived using all
patients diagnosed with HF from 2015-2020 (n = 34,208). Novel findings included the unacceptable
reality that 70% of index HF diagnoses are made through hospitalisation; where index diagnosis through primary care conferred a medium-term survival advantage and long-term cost saving (ÂŁ2,500
per patient). This underpins a health economic model for the deployment of AI-ECG across primary
care. Chapter 4 approaches a normative ethical analysis focusing on equity, agency, data rights, and
responsibility for safe, effective, and trustworthy implementation of an unprecedented at-home patient
self-administered AI-ECG screening programme. I propose approaches to mitigating any potential
harms, towards preserving and promoting trust, patient engagement, and public health.
Collectively, this thesis marks novel work highlighting AI-ECG as tool with the potential to
address major cardiovascular public health priorities. Scrutiny through complimentary clinical, health
economic, and ethical considerations can directly serve patients and health systems by blueprinting
best-practice for the evaluation and implementation of DHTs integrating AI – building the conviction
needed to realise the full potential of such technologies.Open Acces
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