Hybrid method for noise rejection from breath sound using transient artifact reduction algorithm and spectral subtraction.

OBJECTIVES: Computerized breath sound based diagnostic methods are one of the emerging technologies gaining popularity in terms of detecting respiratory disorders. However, the breath sound signal used in such automated systems used to be too noisy, which affects the quality of the diagnostic interpretations. To address this problem, the proposed work presents the new hybrid approach to reject the noises from breath sound. METHODS: In this method, 80 chronic obstructive pulmonary disease (COPD), 75 asthmatics and 80 normal breath sounds were recorded from the participants of a hospital. Each of these breath sound data were decontaminated using hybrid method of Butterworth band-pass filter, transient artifact reduction algorithm and spectral subtraction algorithm. The study examined the algorithms noise rejection potential over each category of breath sound by estimating the noise rejection performance metrics, i.e., mean absolute error (MAE), mean square error (MSE), peak signal to noise ratio (PSNR), and signal to noise ratio (SNR). RESULTS: Using this algorithm, the study obtained a high value of SNR of 70 dB and that of PSNR of 72 dB. CONCLUSIONS: The study could definitely a suitable one to suppress noises and to produce noise free breath sound signal.

Computerized respiratory sound based diagnosis of pneumonia.

Globally, respiratory disorders are a great health burden, affecting as well as destroying human lives; pneumonia is one among them. Pneumonia stages can progress from mild stage to even towards deadly if it is misdiagnosed. Misdiagnosis happens as it exhibits the symptoms identical to other respiratory diseases. Respiratory sound (RS)-based detection of pneumonia could be the most perfect, convenient, as well as the economical solution to this serious problem. This paper presents a novel method to detect pneumonia based on RS. This study is carried out over 310 pneumonia RS and 318 healthy RS, recorded from a hospital. The noises from each RS are eliminated using the Butterworth band pass filter and sparsity-assisted signal smoothing algorithm. Approximate entropy, Shannon entropy, fractal dimension, and largest Lyapunov exponent are the nonlinear features, which are extracted from each denoised RS. The extracted features are inputted to support vector machine classifiers to distinguish pneumonia RS and healthy RS. This method discriminates against pneumonia and healthy RS with 99.8% classification accuracy, 99.8% sensitivity, 99.6% specificity, 99.6% positive predictive value, 99.6% F1-score, and area under curve value of 1.0. Future endeavours will be to examine the efficacy of the proposed algorithm to diagnose pneumonia from the real-time RS acquired from a pneumonia patient in a hospital. This proposed work could be a great support to clinicians in diagnosing pneumonia based on RS.

A pilot study to evaluate the changes in venous blood gas parameters and hypoxia biomarkers in health care workers using different kinds of masks.

Background: The study is aimed to investigate the metabolic alterations and changes in biochemical parameters associated with extended mask. Methods: It was a prospective comparative study conducted on 129 participants comprised of 37 healthy controls and 92 health care workers using different kind of masks like, cloth mask, surgical masks and N95-FFR/PPE. Two samples on day-1 and day-10 were collected for analysis of blood gas parameters, serum hypoxia-inducible factor-α (HIF-α), and erythropoietin (EPO). Results: Oxygen saturation percentage (sO2) of 72.68 (P = 0.033) was significantly low, whereas, Na+ (P = 0.05) and Ca2+ (P < 0.001) were raised in exposed individuals than the healthy controls. The serum HIF-α level of 3.26 ng/mL, was considerable higher in the exposed individuals than controls (P = 0.001). pO2 and sO2 were the lowest and HIF-α and EPO were raised in N95-FFR/PPE of all mask users (P < 0.01). A significant difference was evidenced for pCO2, pH, Na+, Ca2+, and EPO in the exposed group. A positive correlation between the duration of mask use (in hours) with HIF-α (r = 0.247, P = 0.005) and Ca2+ (r = 0.306, P < 0.001) was observed. The major complaints in N95-FFR/PPE users were headache (15.2%) and polydipsia (33.3%). Conclusion: The study findings depicted a significant metabolic alterations in PPE/N95 users which could be due to chronic hypoxic exposure of the tissues.

Mycobacterium kansasaii and Mycobacterium scrofulaceum dual pulmonary infection in an immunocompetent male: first report from India.

A 57-year-old farmer presented with chronic cough and recurrent hemoptysis, previously treated for sputum positive pulmonary tuberculosis. Referred to us for evaluation of drug resistant tuberculosis as his sputum was persistently positive for acid fast bacilli along with radiological worsening even after 6 months of antitubercular treatment. Bronchoalveolar lavage was done and he was diagnosed with a rare mixed non-tuberculous mycobacyteria (NTM) pulmonary infection despite no immune dysfunction. He was successfully treated with multidrug regimen of rifampicin, isoniazid, ethambutol and clarithromycin.

An Assessment of the Profile and Predictors of Outcomes in COVID-19 Patients Hospitalized in a Tertiary Care Institute in Central India.

Background Coronavirus disease 2019 (COVID-19) is the largest pandemic that has affected people around the globe. Various researches have been conducted worldwide, but there is a scarcity of data from Central India on the relationship between several risk factors for infection and mortality. Our study assessed the predictors and patient profiles of those with COVID-19, which will aid in prioritizing patient treatment and preventive measures. Methods A retrospective study was done between March and December 2020. The study included 5,552 COVID-19 patients admitted to the All India Institute of Medical Sciences (AIIMS), Raipur. A validated questionnaire form provided by the WHO was used. Data for multiple clinical and nonclinical parameters were collected, and analysis was done using SPSS version 26 (IBM Corp., Armonk, NY, USA) and STATA version 12 (StataCorp LLC, College Station, TX, USA). Mortality and risk assessment of patients was done using multivariate logistic regression. Result In our study cohort of 5,552 COVID-19 patients, the median age was found to be 47 years (interquartile range (IQR): 31-60 years; range: 14-100 years), and 3,557 (64%) were male. Predominantly, patients presented with fever (41.30%), cough (40.20%), and dyspnea (29.29%). The major comorbidities were hypertension (29.70%), diabetes (25.40%), and chronic cardiac disease (5.79%). The common complications were liver dysfunction (26.83%), viral pneumonitis (23.66%), acute renal injury (15.25%), and acute respiratory distress syndrome (ARDS) (13.41%). In multivariate analysis, age (more than 40 years) (odds ratio (OR): 2.63; 95% confidence interval (CI): 1.531-4.512; p<0.001), diabetes (OR: 1.61; 95% CI: 1.088-2.399; p=0.017), obesity (OR: 6.88; 95% CI: 2.188-12.153; p=0.004), leukocytosis (OR: 1.74; 95% CI: 1.422-2.422; p<0.001), lymphocytopenia (OR: 2.54, 95% CI: 1.718-3.826; p<0.001), thrombocytopenia (OR: 1.15; 95% CI: 1.777-8.700; p=0.001), and ferritin concentration > 1,000 ng/mL (OR: 4.67; 95% CI: 1.991-10.975; p<0.001) were the independent predictors of mortality among COVID-19 patients. Conclusion The leading comorbidities in our study were hypertension, followed by diabetes. Patients who were 40 years or older, obese patients, and diabetic patients have a higher mortality risk. The poor prognostic predictors in COVID-19 patients were high ferritin levels (>1,000 ng/mL), leukocytosis, lymphocytopenia, and thrombocytopenia.

Influence of laboratory biomarkers on inflammatory indices for assessing severity progression in COVID-19 cases.

Background and Objective: This study explored the role of various laboratory biomarkers on inflammatory indices for predicting disease progression toward severity in COVID-19 patients. Methods: This retrospective study was conducted on 1233 adults confirmed for COVID-19. The participants were grouped undermild, moderate, and severe grade disease. Serum bio-inflammatory index (SBII) and systemic inflammatory index (SII) were calculated and correlated with disease severity. The study variables, including clinical details and laboratory variables, were analyzed for impact on the inflammatory indices and severity status using a sequential multiple regression model to determine the predictors for mortality. Receiver operating characteristics defined the cut-off values for severity. Results: Among the study population, 56.2%, 20.7%, and 23.1% were categorized as mild, moderate, and severe COVID-19 cases. Diabetes with hypertension was the most prevalent comorbid condition. The odds for males to have the severe form of the disease was 1.6 times (95% CI = 1.18-2.18, P = 0.002). The median (inter-quartile-range) of SBII was 549 (387.84-741.34) and SII was 2097.6 (1113.9-4153.73) in severe cases. Serum urea, electrolytes, gamma-glutamyl transferase, red-cell distribution width-to-hematocrit ratio, monocytopenia, and eosinopenia exhibited a significant influence on the SpO2, SBII, and SII. Both SBII (r = -0.582, P < 0.001) and SII (r = -0.52, P < 0.001) strongly correlated inversely with SpO2 values [Figures 3a and 3b]. More than 80% of individuals admitted with severe grade COVID-19 had values of more than 50th percentile of SBII and SII. The sensitivity and specificity of SBII at 343.67 for severity were 81.4% and 70.1%, respectively. SII exhibited 77.2% sensitivity and 70.8% specificity at 998.72. Conclusion: Serial monitoring of the routinely available biomarkers would provide considerable input regarding inflammatory status and severity progression in COVID-19.

Circadian rhythmicity of heart rate variability and its impact on cardiac autonomic modulation in asthma.

The commonly observed nocturnal attack of asthma is accompanied by circadian variations in airway inflammation and other physiological variables. It is also documented to present with a significantly higher risk of adverse cardiovascular events that are associated with lower heart rate variability (HRV) and depressed sympathetic and enhanced parasympathetic modulations. However, available literature is scarce with regard to the impact of alteration in circadian rhythmicity of long-term HRV and its day-night variation in asthmatic patients. Thus, 72-h continuous recording of RR interval and oxygen saturation was done to study the circadian variability of HRV (in terms of time and frequency domain indices) and also to assess the pattern of alterations in sympathetic and parasympathetic tones at different times of the day in asthmatic patients (n = 32) and healthy control subjects (n = 31). Repeated-measure analysis of variance and independent-samples t-test revealed significantly increased parasympathetic tone [in terms of increased square root of the mean squared differences of successive NN intervals (RMSSD), percentage of number of pairs of adjacent RR interval differing by more than 50 ms (pNN50), standard deviation of NN intervals (SDNN), and high frequency (HF)] with reduced sympathetic activity [decreased low frequency (LF) and LF/HF ratio] at early morning hours (between 04:00 and 10:00 h) in the asthma patients in contrast to the healthy subjects who had opposite response. Also, significant phase delay (p<0.05) of all the HRV indices and SpO2, was evident by cosinor analysis. Therefore, disturbed circadian rhythm of HRV indices and early morning increased parasympathetic tone points toward the possible pathophysiological basis of exacerbated asthmatic symptoms at late night/early morning hours and susceptibility of future cardiovascular pathologies. This also necessitates the assessment of HRV rhythm while dealing with the therapeutic management of asthma patients.

Osteopetrosis in a Patient of Systemic Sclerosis Sine Scleroderma: A Rare Association.

Systemic sclerosis is a connective tissue disorder of unknown etiology. Although it is a multisystemic disorder, skin thickening is considered as a hallmark of the disease. It usually involves the lungs, gastrointestinal, and musculoskeletal systems. However, a rare subset of systemic sclerosis, systemic sclerosis sine scleroderma, is characterized by internal organ involvement and positive serologic markers with the total or partial absence of cutaneous manifestations. We present a rare association of osteopetrosis in a case of systemic sclerosis sine scleroderma, in a 22-year-old male patient, who presented with pulmonary symptoms as his chief complaints, unreported so far in literature.

Respiratory Sound Based Classification of Chronic Obstructive Pulmonary Disease: a Risk Stratification Approach in Machine Learning Paradigm.

This article investigates the classification of normal and COPD subjects on the basis of respiratory sound analysis using machine learning techniques. Thirty COPD and 25 healthy subject data are recorded. Total of 39 lung sound features and 3 spirometry features are extracted and evaluated. Various parametric and nonparametric tests are conducted to evaluate the relevance of extracted features. Classifiers such as support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), decision tree and discriminant analysis (DA) are used to categorize normal and COPD breath sounds. Classification based on spirometry parameters as well as respiratory sound parameters are assessed. Maximum classification accuracy of 83.6% is achieved by the SVM classifier while using the most relevant lung sound parameters i.e. median frequency and linear predictive coefficients. Further, SVM classifier and LR classifier achieved classification accuracy of 100% when relevant lung sound parameters, i.e. median frequency and linear predictive coefficient are combined with the spirometry parameters, i.e. forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1). It is concluded that combining lung sound based features with spirometry data can improve the accuracy of COPD diagnosis and hence the clinician's performance in routine clinical practice. The proposed approach is of great significance in a clinical scenario wherein it can be used to assist clinicians for automated COPD diagnosis. A complete handheld medical system can be developed in the future incorporating lung sounds for COPD diagnosis using machine learning techniques.