Stratifying asthma severity in children using cough sound analytic technology.

Introduction: Asthma is a common childhood respiratory disorder characterized by wheeze, cough and respiratory distress responsive to bronchodilator therapy. Asthma severity can be determined by subjective, manual scoring systems such as the Pulmonary Score (PS). These systems require significant medical training and expertise to rate clinical findings such as wheeze characteristics, and work of breathing. In this study, we report the development of an objective method of assessing acute asthma severity based on the automated analysis of cough sounds.Methods: We collected a cough sound dataset from 224 children; 103 without acute asthma and 121 with acute asthma. Using this database coupled with clinical diagnoses and PS determined by a clinical panel, we developed a machine classifier algorithm to characterize the severity of airway constriction. The performance of our algorithm was then evaluated against the PS from a separate set of patients, independent of the training set.Results: The cough-only model discriminated no/mild disease (PS 0-1) from severe disease (PS 5,6) but required a modified respiratory rate calculation to separate very severe disease (PS > 6). Asymptomatic children (PS 0) were separated from moderate asthma (PS 2-4) by the cough-only model without the need for clinical inputs.Conclusions: The PS provides information in managing childhood asthma but is not readily usable by non-medical personnel. Our method offers an objective measurement of asthma severity which does not rely on clinician-dependent inputs. It holds potential for use in clinical settings including improving the performance of existing asthma-rating scales and in community-management programs.AbbreviationsAMaccessory muscleBIbreathing indexCIconfidence intervalFEV1forced expiratory volume in one secondLRlogistic regressionPEFRpeak expiratory flow ratePSpulmonary scoreRRrespiratory rateSDstandard deviationSEstandard errorWAWestern Australia.

Polysomnographic risk factors for vigilance-related cognitive decline and obstructive sleep apnea.

PURPOSE: Cognitive decline (CD) and obstructive sleep apnea (OSA) are often comorbid. Some modifiable risk factors (RF) for CD are also associated with OSA. Diagnostic polysomnography (PSG) measures these RF and may identify at risk patients prior to the onset of CD. We aim to determine whether there are severe RF associated with established CD and an increasing severity of OSA that could identify patients at risk for CD for medical intervention. METHODS: We gathered information from subjects having type 1 PSG for suspected OSA. The psychomotor vigilance task (PVT) measured established CD (group 0 and group1). We compared levels of severe RF in group 0 and group 1 with a larger group (group 2) without the PVT. We used severe standardized values of excessive daytime sleepiness (Epworth Sleepiness Score [ESS]), overnight change of systolic blood pressure (ΔSBP), change of oxygen desaturation (ΔSpO2), and sleep arousal (ArI) as RF. We compared the severe levels of ESS, ΔSBP, ΔSpO2, and ArI by group and OSA severity. RESULTS: A total of 136 patients underwent diagnostic PSG. PVT parameters were available for 43 subjects. The severity of the RF was consistent with risk for CD (ΔSBP 22.0 ± 5.6, ESS 18.2 ± 2.2, ArI 58.8 ± 18.7, ΔSpO2 61.7 ± 21.9). The levels of RF increased with increasing severity of OSA. There were significant between-group differences for severe ΔSpO2 (p = 0.004) and ΔSpO2 + ArI (p = 0.019). CONCLUSION: The levels of RF increase with increasing OSA severity. Subjects with severe RF ΔSpO2 and ΔSpO2 + ArI are likely to have PVT-determined CD. Risk factor analysis may screen for CD.

A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children.

BACKGROUND: The differential diagnosis of paediatric respiratory conditions is difficult and suboptimal. Existing diagnostic algorithms are associated with significant error rates, resulting in misdiagnoses, inappropriate use of antibiotics and unacceptable morbidity and mortality. Recent advances in acoustic engineering and artificial intelligence have shown promise in the identification of respiratory conditions based on sound analysis, reducing dependence on diagnostic support services and clinical expertise. We present the results of a diagnostic accuracy study for paediatric respiratory disease using an automated cough-sound analyser. METHODS: We recorded cough sounds in typical clinical environments and the first five coughs were used in analyses. Analyses were performed using cough data and up to five-symptom input derived from patient/parent-reported history. Comparison was made between the automated cough analyser diagnoses and consensus clinical diagnoses reached by a panel of paediatricians after review of hospital charts and all available investigations. RESULTS: A total of 585 subjects aged 29 days to 12 years were included for analysis. The Positive Percent and Negative Percent Agreement values between the automated analyser and the clinical reference were as follows: asthma (97, 91%); pneumonia (87, 85%); lower respiratory tract disease (83, 82%); croup (85, 82%); bronchiolitis (84, 81%). CONCLUSION: The results indicate that this technology has a role as a high-level diagnostic aid in the assessment of common childhood respiratory disorders. TRIAL REGISTRATION: Australian and New Zealand Clinical Trial Registry (retrospective) - ACTRN12618001521213 : 11.09.2018.

Corrections to "Automatic Croup Diagnosis Using Cough Sound Recognition".

Presents corrections to shareholder information from this paper, "Automatic croup diagnosis using cough sound recognition," (Sharan, R.V., et al), IEEE Trans. Biomed. Eng., vol. 66, no. 2, pp. 485-495, Feb. 2019.

Corrigendum: Predicting spirometry readings using cough sound features and regression (2018 Physiol. Meas. 39 095001).

The purpose of this submission is to provide missing information to complete the conflict of interest statement associated with the article. The statements provided here augment the already provided information rather than replace it.

Automatic Croup Diagnosis Using Cough Sound Recognition.

OBJECTIVE: Croup, a respiratory tract infection common in children, causes an inflammation of the upper airway restricting normal breathing and producing cough sounds typically described as seallike "barking cough." Physicians use the existence of barking cough as the defining characteristic of croup. This paper aims to develop automated cough sound analysis methods to objectively diagnose croup. METHODS: In automating croup diagnosis, we propose the use of mathematical features inspired by the human auditory system. In particular, we utilize the cochleagram for feature extraction, a time-frequency representation where the frequency components are based on the frequency selectivity property of the human cochlea. Speech and cough share some similarities in the generation process and physiological wetware used. As such, we also propose the use of mel-frequency cepstral coefficients which has been shown to capture the relevant aspects of the short-term power spectrum of speech signals. Feature combination and backward sequential feature selection are also experimented with. Experimentation is performed on cough sound recordings from patients presenting various clinically diagnosed respiratory tract infections divided into croup and non-croup. The dataset is divided into training and test sets of 364 and 115 patients, respectively, with automatically segmented cough sound segments. RESULTS: Croup and non-croup patient classification on the test dataset with the proposed methods achieve a sensitivity and specificity of 92.31% and 85.29%, respectively. CONCLUSION: Experimental results show the significant improvement in automatic croup diagnosis against earlier methods. SIGNIFICANCE: This paper has the potential to automate croup diagnosis based solely on cough sound analysis.

Predicting spirometry readings using cough sound features and regression.

OBJECTIVE: Spirometry is a commonly used method of measuring lung function. It is useful in the definitive diagnosis of diseases such as asthma and chronic obstructive pulmonary disease (COPD). However, spirometry requires cooperative patients, experienced staff, and repeated testing to ensure the consistency of measurements. There is discomfort associated with spirometry and some patients are not able to complete the test. In this paper, we investigate the possibility of using cough sound analysis for the prediction of spirometry measurements. APPROACH: Our approach is based on the premise that the mechanism of cough generation and the forced expiratory maneuver of spirometry share sufficient similarities enabling this prediction. Using an iPhone, we collected mostly voluntary cough sounds from 322 adults presenting to a respiratory function laboratory for pulmonary function testing. Subjects had the following diagnoses: obstructive, restrictive, or mixed pattern diseases, or were found to have no lung disease along with normal spirometry. The cough sounds were automatically segmented using the algorithm described in Sharan et al (2018 IEEE Trans. Biomed. Eng.). We then represented cough sounds with various cough sound descriptors and built linear and nonlinear regression models connecting them to spirometry parameters. Augmentation of cough features with subject demographic data is also experimented with. The dataset was divided into 272 training subjects and 50 test subjects for experimentation. MAIN RESULTS: The performance of the auto-segmentation algorithm was evaluated on 49 randomly selected subjects from the overall dataset with a sensitivity and PPV of 84.95% and 98.51%, respectively. Our regression models achieved a root mean square error (and correlation coefficient) for standard spirometry parameters FEV1, FVC, and FEV1/FVC of 0.593L (0.810), 0.725L (0.749), and 0.164 (0.547), respectively, on the test dataset. In addition, we could achieve sensitivity, specificity, and accuracy of 70% or higher by applying the GOLD standard for COPD diagnosis on the estimated spirometry test results. SIGNIFICANCE: The experimental results show high positive correlation in predicting FEV1 and FVC and moderate positive correlation in predicting FEV1/FVC. The results show possibility of predicting spirometry results using cough sound analysis.

Snore Sound Analysis Can Detect the Presence of Obstructive Sleep Apnea Specific to NREM or REM Sleep.

STUDY OBJECTIVES: Severities of obstructive sleep apnea (OSA) estimated both for the overall sleep duration and for the time spent in rapid eye movement (REM) and non-rapid eye movement (NREM) sleep are important in managing the disease. The objective of this study is to investigate a method by which snore sounds can be analyzed to detect the presence of OSA in NREM and REM sleep. METHODS: Using bedside microphones, snoring and breathing-related sounds were acquired from 91 patients with OSA (35 females and 56 males) undergoing routine diagnostic polysomnography studies. A previously developed automated mathematical algorithm was applied to label each snore sound as belonging to either NREM or REM sleep. The snore sounds were then used to compute a set of mathematical features characteristic to OSA and to train a logistic regression model (LRM) to classify patients into an OSA or non-OSA category in each sleep state. The performance of the LRM was estimated using a leave-one-patient-out cross-validation technique within the entire dataset. We used the polysomnography-based diagnosis as our reference method. RESULTS: The models achieved 80% to 86% accuracy for detecting OSA in NREM sleep and 82% to 85% in REM sleep. When separate models were developed for females and males, the accuracy for detecting OSA in NREM sleep was 91% in females and 88% to 89% in males. Accuracy for detecting OSA in REM sleep was 88% to 91% in females and 89% to 91% in males. CONCLUSIONS: Snore sounds carry sufficient information to detect the presence of OSA during NREM and REM sleep. Because the methods used include technology that is fully automated and sensors that do not have a physical connection to the patient, it has potential for OSA screening in the home environment. The accuracy of the method can be improved by developing sex-specific models.

Automatic picking of snore events from overnight breath sound recordings.

Snoring is one of the earliest symptoms of Obstructive Sleep Apnea (OSA). However, the unavailability of an objective snore definition is a major obstacle in developing automated snore analysis system for OSA screening. The objectives of this paper is to develop a method to identify and extract snore sounds from a continuous sound recording following an objective definition of snore that is independent of snore loudness. Nocturnal sounds from 34 subjects were recorded using a non-contact microphone and computerized data-acquisition system. Sound data were divided into non-overlapping training (n = 21) and testing (n = 13) datasets. Using training dataset an Artificial Neural Network (ANN) classifier were trained for snore and non-snore classification. Snore sounds were defined based on the key observation that sounds perceived as `snores' by human are characterized by repetitive packets of energy that are responsible for creating the vibratory sound peculiar to snorers. On the testing dataset, the accuracy of ANN classifier ranged between 86 - 89%. Our results indicate that it is possible to define snoring using loudness independent, objective criteria, and develop automated snore identification and extraction algorithms.

Cough sound analysis for diagnosing croup in pediatric patients using biologically inspired features.

This paper aims to diagnose croup in children using cough sound signal classification. It proposes the use of a time-frequency image-based feature, referred as the cochleagram image feature (CIF). Unlike the conventional spectrogram image, the cochleagram utilizes a gammatone filter which models the frequency selectivity property of the human cochlea. This helps reveal more spectral information in the time-frequency image making it more useful for feature extraction. The cochleagram image is then divided into blocks and central moments are extracted as features. Classification is performed using logistic regression model (LRM) and support vector machine (SVM) on a comprehensive real-world cough sound signal database containing 364 patients with various clinically diagnosed respiratory tract infections divided into croup and non-croup. The best results, sensitivity of 88.37% and specificity of 91.59%, are achieved using SVM classification on a combined feature set of CIF and the conventional mel-frequency cepstral coefficients (MFCCs).

Wavelet augmented cough analysis for rapid childhood pneumonia diagnosis.

Pneumonia is the cause of death for over a million children each year around the world, largely in resource poor regions such as sub-Saharan Africa and remote Asia. One of the biggest challenges faced by pneumonia endemic countries is the absence of a field deployable diagnostic tool that is rapid, low-cost and accurate. In this paper, we address this issue and propose a method to screen pneumonia based on the mathematical analysis of cough sounds. In particular, we propose a novel cough feature inspired by wavelet-based crackle detection work in lung sound analysis. These features are then combined with other mathematical features to develop an automated machine classifier, which can separate pneumonia from a range of other respiratory diseases. Both cough and crackles are symptoms of pneumonia, but their existence alone is not a specific enough marker of the disease. In this paper, we hypothesize that the mathematical analysis of cough sounds allows us to diagnose pneumonia with sufficient sensitivity and specificity. Using a bedside microphone, we collected 815 cough sounds from 91 patients with respiratory illnesses such as pneumonia, asthma, and bronchitis. We extracted wavelet features from cough sounds and combined them with other features such as Mel Cepstral coefficients and non-Gaussianity index. We then trained a logistic regression classifier to separate pneumonia from other diseases. As the reference standard, we used the diagnosis by physicians aided with laboratory and radiological results as deemed necessary for a clinical decision. The methods proposed in this paper achieved a sensitivity and specificity of 94% and 63%, respectively, in separating pneumonia patients from non-pneumonia patients based on wavelet features alone. Combining the wavelets with features from our previous work improves the performance further to 94% and 88% sensitivity and specificity. The performance far surpasses that of the WHO criteria currently in common use in resource-limited settings.

Cough sound analysis can rapidly diagnose childhood pneumonia.

Pneumonia annually kills over 1,800,000 children throughout the world. The vast majority of these deaths occur in resource poor regions such as the sub-Saharan Africa and remote Asia. Prompt diagnosis and proper treatment are essential to prevent these unnecessary deaths. The reliable diagnosis of childhood pneumonia in remote regions is fraught with difficulties arising from the lack of field-deployable imaging and laboratory facilities as well as the scarcity of trained community healthcare workers. In this paper, we present a pioneering class of technology addressing both of these problems. Our approach is centred on the automated analysis of cough and respiratory sounds, collected via microphones that do not require physical contact with subjects. Cough is a cardinal symptom of pneumonia but the current clinical routines used in remote settings do not make use of coughs beyond noting its existence as a screening-in criterion. We hypothesized that cough carries vital information to diagnose pneumonia, and developed mathematical features and a pattern classifier system suited for the task. We collected cough sounds from 91 patients suspected of acute respiratory illness such as pneumonia, bronchiolitis and asthma. Non-contact microphones kept by the patient's bedside were used for data acquisition. We extracted features such as non-Gaussianity and Mel Cepstra from cough sounds and used them to train a Logistic Regression classifier. We used the clinical diagnosis provided by the paediatric respiratory clinician as the gold standard to train and validate our classifier. The methods proposed in this paper could separate pneumonia from other diseases at a sensitivity and specificity of 94 and 75% respectively, based on parameters extracted from cough sounds alone. The inclusion of other simple measurements such as the presence of fever further increased the performance. These results show that cough sounds indeed carry critical information on the lower respiratory tract, and can be used to diagnose pneumonia. The performance of our method is far superior to those of existing WHO clinical algorithms for resource-poor regions. To the best of our knowledge, this is the first attempt in the world to diagnose pneumonia in humans using cough sound analysis. Our method has the potential to revolutionize the management of childhood pneumonia in remote regions of the world.

Automatic identification of wet and dry cough in pediatric patients with respiratory diseases.

Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis especially in children. Wet coughs are more likely to be associated with lower respiratory track bacterial infections. At present during a typical consultation session, the wet/dry decision is based on the subjective judgment of a physician. It is not available for the non-trained person, long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop an automated technology to classify cough into 'wet' and 'dry' categories. We propose novel features and a Logistic regression model (LRM) for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric coughs (C = 536) recorded using a bed-side non-contact microphone from N = 78 patients. Results of the automatic classification were compared against two expert human scorers. The sensitivity and specificity of the LRM in picking wet coughs were between 87 and 88% with 95% confidence interval on training/validation dataset (310 cough events from 60 patients) and 84 and 76% respectively on prospective dataset (117 cough events from 18 patients). The kappa agreement with two expert human scorers on prospective dataset was 0.51. These results indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.

Objective measure of sleepiness and sleep latency via bispectrum analysis of EEG.

Chronic sleepiness is a common symptom in the sleep disorders, such as, Obstructive Sleep Apnea, Periodic leg movement disorder, narcolepsy, etc. It affects 8% of the adult population and is associated with significant morbidity and increased risk to individual and society. MSLT and MWT are the existing tests for measuring sleepiness. Sleep Latency (SL) is the main measures of sleepiness computed in these tests. These are the laboratory-based tests and require services of an expert sleep technician. There are no tests available to detect inadvertent sleep onset in real time and which can be performed in any professional work environment to measure sleepiness level. In this article, we propose a fully automated, objective sleepiness analysis technique based on the single channel of EEG. The method uses a one-dimensional slice of the EEG Bispectrum representing a nonlinear transformation of the underlying EEG generator to compute a novel index called Sleepiness Index. The SL is then computed from the SI. Working on the patient's database of 42 subjects we computed SI and estimated SL. A strong significant correlation (r ≥ 0.70, s < 0.001) was found between technician scored SL and that computed via SI. The proposed technology holds promise in the automation of the MSLT and MWT tests. It can also be developed into a sleep management system, wherein the SI is incorporated into a sleepiness index alert unit to alarm the user when sleepiness level crosses the predetermined threshold.

Interhemispheric asynchrony correlates with severity of respiratory disturbance index in patients with sleep apnea.

Obstructive sleep apnea (OSA) hypopnea syndrome is a disorder characterized by airway obstructions during sleep; full obstructions are known as apnea and partial obstructions are called hypopnea. Sleep in OSA patients is significantly disturbed with frequent apnea/hypopnea and arousal events. We illustrate that these events lead to functional asymmetry of the brain as manifested by the interhemispheric asynchrony (IHA) computed using EEG recorded on the scalp. In this paper, based on the higher order spectra of IHA time series, we propose a new index [interhemispheric synchrony index (IHSI)], for characterizing brain asynchrony in OSA. The IHSI computation does not depend on subjective criteria and can be completely automated. The proposed method was evaluated on overnight EEG data from a clinical database of 36 subjects referred to a hospital sleep laboratory. Our results indicated that the IHSI could classify the patients into OSA/non-OSA classes with an accuracy of 91% (ρ = 0.0001), at the respiratory disturbance index threshold of 10, suggesting that the brain asynchrony carries vital information on OSA.

A state transition-based method for quantifying EEG sleep fragmentation.

Sleep fragmentation is the predominant factor causing excessive daytime sleepiness in diseases such as sleep apnea and periodic leg movement syndrome. The reference standard for quantifying sleep fragmentation is the arousal index (ArI), which is defined as the average number of arousals per hour of sleep. Arousal scoring is tedious and subjective resulting in considerable inter- and intra-rater variability. Moreover, ArI is only weakly correlated with other indicators of sleep fragmentation such as the total sleep time (TST) and the sleep efficiency (SE). This introduces consistency problems, making the ArI difficult to interpret in practice. In this article, we address these issues by proposing a novel measure of sleep fragmentation termed the weighted-transition sleep fragmentation index (chi). This new measure is derived by capturing the different sleep states transitions and assigning weights to them. A significant correlation was found between chi and all other indices of sleep fragmentation (r = 0.72, sigma = 0.0001, r = -0.59, sigma = 0.001, r = -0.72, sigma = 0.0001, respectively, for ArI, TST and SE. These results suggest that chi is an accurate and useful tool for clinical practice.