The diagnosis of respiratory disease in children using a phone-based cough and symptom analysis algorithm: The smartphone recordings of cough sounds 2 (SMARTCOUGH-C 2) trial design.
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ABSTRACT:
SUBMITTER: Moschovis PP
PROVIDER: S-EPMC7990113 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.
| S-EPMC8586978 | biostudies-literature
Project description:IntroductionCoughing is a common symptom in pediatric lung disease and cough frequency has been shown to be correlated to disease activity in several conditions. Automated cough detection could provide a noninvasive digital biomarker for pediatric clinical trials or care. The aim of this study was to develop a smartphone-based algorithm that objectively and automatically counts cough sounds of children.MethodsThe training set was composed of 3228 pediatric cough sounds and 480,780 noncough sounds from various publicly available sources and continuous sound recordings of 7 patients admitted due to respiratory disease. A Gradient Boost Classifier was fitted on the training data, which was subsequently validated on recordings from 14 additional patients aged 0-14 admitted to the pediatric ward due to respiratory disease. The robustness of the algorithm was investigated by repeatedly classifying a recording with the smartphone-based algorithm during various conditions.ResultsThe final algorithm obtained an accuracy of 99.7%, sensitivity of 47.6%, specificity of 99.96%, positive predictive value of 82.2% and negative predictive value 99.8% in the validation dataset. The correlation coefficient between manual- and automated cough counts in the validation dataset was 0.97 (p < .001). The intra- and interdevice reliability of the algorithm was adequate, and the algorithm performed best at an unobstructed distance of 0.5-1 m from the audio source.ConclusionThis novel smartphone-based pediatric cough detection application can be used for longitudinal follow-up in clinical care or as digital endpoint in clinical trials.
| S-EPMC9306830 | biostudies-literature
Project description:BackgroundLittle is known of the language healthcare professionals use to describe cough sounds. We aimed to examine how they describe cough sounds and to assess whether these descriptions suggested they appreciate the basic sound qualities (as assessed by acoustic analysis) and the underlying diagnosis of the patient coughing.Methods53 health professionals from two large respiratory tertiary referral centres were recruited; 22 doctors and 31 staff from professions allied to medicine. Participants listened to 9 sequences of spontaneous cough sounds from common respiratory diseases. For each cough they selected patient gender, the most appropriate descriptors and a diagnosis. Cluster analysis was performed to assess which cough sounds attracted similar descriptions.ResultsGender was correctly identified in 93% of cases. The presence or absence of mucus was correct in 76.1% and wheeze in 39.3% of cases. However, identifying clinical diagnosis from cough was poor at 34.0%. Cluster analysis showed coughs with the same acoustics properties rather than the same diagnoses attracted the same descriptions.ConclusionThese results suggest that healthcare professionals can recognise some of the qualities of cough sounds but are poor at making diagnoses from them. It remains to be seen whether in the future cough sound acoustics will provide useful clinical information and whether their study will lead to the development of useful new outcome measures in cough monitoring.
| S-EPMC1413549 | biostudies-literature
Project description:BackgroundAsthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio recordings remains unsolved.ObjectiveThe objective of this study was to evaluate the automatic recognition and segmentation of nocturnal asthmatic coughs and cough epochs in smartphone-based audio recordings that were collected in the field. We also aimed to distinguish partner coughs from patient coughs in contact-free audio recordings by classifying coughs based on sex.MethodsWe used a convolutional neural network model that we had developed in previous work for automated cough recognition. We further used techniques (such as ensemble learning, minibatch balancing, and thresholding) to address the imbalance in the data set. We evaluated the classifier in a classification task and a segmentation task. The cough-recognition classifier served as the basis for the cough-segmentation classifier from continuous audio recordings. We compared automated cough and cough-epoch counts to human-annotated cough and cough-epoch counts. We employed Gaussian mixture models to build a classifier for cough and cough-epoch signals based on sex.ResultsWe recorded audio data from 94 adults with asthma (overall: mean 43 years; SD 16 years; female: 54/94, 57%; male 40/94, 43%). Audio data were recorded by each participant in their everyday environment using a smartphone placed next to their bed; recordings were made over a period of 28 nights. Out of 704,697 sounds, we identified 30,304 sounds as coughs. A total of 26,166 coughs occurred without a 2-second pause between coughs, yielding 8238 cough epochs. The ensemble classifier performed well with a Matthews correlation coefficient of 92% in a pure classification task and achieved comparable cough counts to that of human annotators in the segmentation of coughing. The count difference between automated and human-annotated coughs was a mean -0.1 (95% CI -12.11, 11.91) coughs. The count difference between automated and human-annotated cough epochs was a mean 0.24 (95% CI -3.67, 4.15) cough epochs. The Gaussian mixture model cough epoch-based sex classification performed best yielding an accuracy of 83%.ConclusionsOur study showed longitudinal nocturnal cough and cough-epoch recognition from nightly recorded smartphone-based audio from adults with asthma. The model distinguishes partner cough from patient cough in contact-free recordings by identifying cough and cough-epoch signals that correspond to the sex of the patient. This research represents a step towards enabling passive and scalable cough monitoring for adults with asthma.
| S-EPMC7388043 | biostudies-literature
Project description:Smart wearable sensors are essential for continuous health-monitoring applications and detection accuracy of symptoms and energy efficiency of processing algorithms are key challenges for such devices. While several machine-learning-based algorithms for the detection of abnormal breath sounds are reported in literature, they are either too computationally expensive to implement into a wearable device or inaccurate in multi-class detection. In this paper, a kernel-like minimum distance classifier (K-MDC) for acoustic signal processing in wearable devices was proposed. The proposed algorithm was tested with data acquired from open-source databases, participants, and hospitals. It was observed that the proposed K-MDC classifier achieves accurate detection in up to 91.23% of cases, and it reaches various detection accuracies with a fewer number of features compared with other classifiers. The proposed algorithm's low computational complexity and classification effectiveness translate to great potential for implementation in health-monitoring wearable devices.
| S-EPMC8950004 | biostudies-literature
Project description:PurposeRecent evidence suggests that cough hypersensitivity may be a common feature of chronic cough in adults. However, the clinical relevance remains unclear. This study evaluated the cough-related symptom profile and the clinical relevance and impact of cough hypersensitivity in adults with chronic cough.MethodsThis cross-sectional multi-center study compared cough-related laryngeal sensations and cough triggers in patients with unexplained chronic cough following investigations and in unselected patients newly referred for chronic cough. A structured questionnaire was used to assess abnormal laryngeal sensations and cough triggers. Patients with unexplained cough were also evaluated using the Leicester Cough Questionnaire (LCQ) and a cough visual analogue scale (VAS), and these scores were assessed for correlations with the number of triggers and laryngeal sensations.ResultsThis study recruited 478 patients, including 62 with unexplained chronic cough and 416 with chronic cough. Most participants reported abnormal laryngeal sensations and cough triggers. Laryngeal sensations (4.4 ± 1.5 vs. 3.9 ± 1.9; P = 0.049) and cough triggers (6.9 ± 2.6 vs. 5.0 ± 2.8; P < 0.001) were more frequent in patients with unexplained chronic cough than in those with chronic cough. The number of triggers and laryngeal sensations score significantly correlated with LCQ (r = -0.51, P < 0.001) and cough VAS score (r = 0.53, P < 0.001) in patients with unexplained chronic cough.ConclusionsCough hypersensitivity may be a common feature in adult patients with chronic cough, especially those with unexplained chronic cough. Cough-related health status and cough severity were inversely associated with the number of triggers and laryngeal sensations, suggesting potential relevance of assessing cough hypersensitivity in chronic cough patients.
| S-EPMC6658400 | biostudies-literature
Project description:STUDY OBJECTIVES:To develop and validate a novel non-contact system for whole-night sleep evaluation using breathing sounds analysis (BSA). DESIGN:Whole-night breathing sounds (using ambient microphone) and polysomnography (PSG) were simultaneously collected at a sleep laboratory (mean recording time 7.1 hours). A set of acoustic features quantifying breathing pattern were developed to distinguish between sleep and wake epochs (30 sec segments). Epochs (n = 59,108 design study and n = 68,560 validation study) were classified using AdaBoost classifier and validated epoch-by-epoch for sensitivity, specificity, positive and negative predictive values, accuracy, and Cohen's kappa. Sleep quality parameters were calculated based on the sleep/wake classifications and compared with PSG for validity. SETTING:University affiliated sleep-wake disorder center and biomedical signal processing laboratory. PATIENTS:One hundred and fifty patients (age 54.0±14.8 years, BMI 31.6±5.5 kg/m2, m/f 97/53) referred for PSG were prospectively and consecutively recruited. The system was trained (design study) on 80 subjects; validation study was blindly performed on the additional 70 subjects. MEASUREMENTS AND RESULTS:Epoch-by-epoch accuracy rate for the validation study was 83.3% with sensitivity of 92.2% (sleep as sleep), specificity of 56.6% (awake as awake), and Cohen's kappa of 0.508. Comparing sleep quality parameters of BSA and PSG demonstrate average error of sleep latency, total sleep time, wake after sleep onset, and sleep efficiency of 16.6 min, 35.8 min, and 29.6 min, and 8%, respectively. CONCLUSIONS:This study provides evidence that sleep-wake activity and sleep quality parameters can be reliably estimated solely using breathing sound analysis. This study highlights the potential of this innovative approach to measure sleep in research and clinical circumstances.
| S-EPMC4339734 | biostudies-literature
Project description:Investigating the dynamics of biodiversity via passive acoustic monitoring is a challenging task, owing to the difficulty of identifying different animal vocalizations. Several indices have been proposed to measure acoustic complexity and to predict biodiversity. Although these indices perform well under low-noise conditions, they may be biased when environmental and anthropogenic noises are involved. In this paper, we propose a periodicity coded non-negative matrix factorization (PC-NMF) for separating different sound sources from a spectrogram of long-term recordings. The PC-NMF first decomposes a spectrogram into two matrices: spectral basis matrix and encoding matrix. Next, on the basis of the periodicity of the encoding information, the spectral bases belonging to the same source are grouped together. Finally, distinct sources are reconstructed on the basis of the cluster of the basis matrix and the corresponding encoding information, and the noise components are then removed to facilitate more accurate monitoring of biological sounds. Our results show that the PC-NMF precisely enhances biological choruses, effectively suppressing environmental and anthropogenic noises in marine and terrestrial recordings without a need for training data. The results may improve behaviour assessment of calling animals and facilitate the investigation of the interactions between different sound sources within an ecosystem.
| S-EPMC5495775 | biostudies-literature
Project description:Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM) is caused by the deposition of wild-type transthyretin (TTR) amyloid fibrils in the heart. The age at diagnosis of ATTRwt-CM is reported to be approximately 70-80 years, and patients commonly present with non-disease-specific cardiac abnormalities, such as heart failure with preserved ejection fraction and diastolic dysfunction. The disease can be fatal if left untreated, with an approximate survival of 3-5 years from diagnosis. An oral TTR stabilizer, tafamidis, has enabled early intervention for the treatment of ATTRwt-CM. However, awareness of ATTRwt-CM remains low, and misdiagnosis and a delay in diagnosis are common. This review discusses the epidemiology, characteristics, treatment strategy, and red-flag symptoms and signs of ATTRwt-CM based on the published literature, as well as recent advances in diagnostic modalities that enable early and accurate diagnosis of the disease. We also discuss an algorithm for early and accurate diagnosis of ATTRwt-CM in daily clinical practice. In our diagnostic algorithm, a suspected diagnosis of ATTRwt-CM should be triggered by unexplained left ventricular hypertrophy (LVH), which is LVH that cannot be explained by an increased afterload due to hypertension or valvular disease. In addition, heart failure symptoms, laboratory test results (N-terminal pro-B-type natriuretic peptide, high-sensitivity troponin T, or high-sensitivity troponin I), electrocardiogram and imaging (echocardiogram or cardiac magnetic resonance) data, age (≥60 years), and medical history suggestive of ATTRwt-CM (e.g. carpal tunnel syndrome) should be examined. Detailed examinations using bone scintigraphy and monoclonal protein detection tests followed by tissue biopsy, amyloid typing, and TTR genetic testing are warranted for a definite diagnosis of ATTRwt-CM.
| S-EPMC8318452 | biostudies-literature
Project description:ImportanceBreathing sounds during sleep are an important characteristic feature of obstructive sleep apnea (OSA) and have been regarded as a potential biomarker. Breathing sounds during sleep can be easily recorded using a microphone, which is found in most smartphone devices. Therefore, it may be easy to implement an evaluation tool for prescreening purposes.ObjectiveTo evaluate OSA prediction models using smartphone-recorded sounds and identify optimal settings with regard to noise processing and sound feature selection.Design, setting, and participantsA cross-sectional study was performed among patients who visited the sleep center of Seoul National University Bundang Hospital for snoring or sleep apnea from August 2015 to August 2019. Audio recordings during sleep were performed using a smartphone during routine, full-night, in-laboratory polysomnography. Using a random forest algorithm, binary classifications were separately conducted for 3 different threshold criteria according to an apnea hypopnea index (AHI) threshold of 5, 15, or 30 events/h. Four regression models were created according to noise reduction and feature selection from the input sound to predict actual AHI: (1) noise reduction without feature selection, (2) noise reduction with feature selection, (3) neither noise reduction nor feature selection, and (4) feature selection without noise reduction. Clinical and polysomnographic parameters that may have been associated with errors were assessed. Data were analyzed from September 2019 to September 2020.Main outcomes and measuresAccuracy of OSA prediction models.ResultsA total of 423 patients (mean [SD] age, 48.1 [12.8] years; 356 [84.1%] male) were analyzed. Data were split into training (n = 256 [60.5%]) and test data sets (n = 167 [39.5%]). Accuracies were 88.2%, 82.3%, and 81.7%, and the areas under curve were 0.90, 0.89, and 0.90 for an AHI threshold of 5, 15, and 30 events/h, respectively. In the regression analysis, using recorded sounds that had not been denoised and had only selected attributes resulted in the highest correlation coefficient (r = 0.78; 95% CI, 0.69-0.88). The AHI (β = 0.33; 95% CI, 0.24-0.42) and sleep efficiency (β = -0.20; 95% CI, -0.35 to -0.05) were found to be associated with estimation error.Conclusions and relevanceIn this cross-sectional study, recorded sleep breathing sounds using a smartphone were used to create reasonably accurate OSA prediction models. Future research should focus on real-life recordings using various smartphone devices.
| S-EPMC9011176 | biostudies-literature