Novel algorithm to identify and differentiate specific digital signature of breath sound in patients with diffuse parenchymal lung disease.

BACKGROUND AND OBJECTIVE: Auscultation is an important part of the clinical examination of different lung diseases. Objective analysis of lung sounds based on underlying characteristics and its subsequent automatic interpretations may help a clinical practice. METHODS: We collected the breath sounds from 8 normal subjects and 20 diffuse parenchymal lung disease (DPLD) patients using a newly developed instrument and then filtered off the heart sounds using a novel technology. The collected sounds were thereafter analysed digitally on several characteristics as dynamical complexity, texture information and regularity index to find and define their unique digital signatures for differentiating normality and abnormality. For convenience of testing, these characteristic signatures of normal and DPLD lung sounds were transformed into coloured visual representations. The predictive power of these images has been validated by six independent observers that include three physicians. RESULTS: The proposed method gives a classification accuracy of 100% for composite features for both the normal as well as lung sound signals from DPLD patients. When tested by independent observers on the visually transformed images, the positive predictive value to diagnose the normality and DPLD remained 100%. CONCLUSIONS: The lung sounds from the normal and DPLD subjects could be differentiated and expressed according to their digital signatures. On visual transformation to coloured images, they retain 100% predictive power. This technique may assist physicians to diagnose DPLD from visual images bearing the digital signature of the condition.

Detection of lungs status using morphological complexities of respiratory sounds.

Traditionally, the clinical diagnosis of a respiratory disease is made from a careful clinical examination including chest auscultation. Objective analysis and automatic interpretation of the lung sound based on its physical characters are strongly warranted to assist clinical practice. In this paper, a new method is proposed to distinguish between the normal and the abnormal subjects using the morphological complexities of the lung sound signals. The morphological embedded complexities used in these experiments have been calculated in terms of texture information (lacunarity), irregularity index (sample entropy), third order moment (skewness), and fourth order moment (Kurtosis). These features are extracted from a mixed data set of 10 normal and 20 abnormal subjects and are analyzed using two different classifiers: extreme learning machine (ELM) and support vector machine (SVM) network. The results are obtained using 5-fold cross-validation. The performance of the proposed method is compared with a wavelet analysis based method. The developed algorithm gives a better accuracy of 92.86% and sensitivity of 86.30% and specificity of 86.90% for a composite feature vector of four morphological indices.

Observation of Ultrahigh Photoconductivity in DNA-MoS2 Nano-Biocomposite.

A nano-biocomposite film with ultrahigh photoconductivity remains elusive and critical for bio-optoelectronic applications. A uniform, well-connected, high-concentration nanomaterial network in the biological matrix remains challenging to achieve high photoconductivity. Wafer-scale continuous nano-biocomposite film without surface deformations and cracks plays another major obstacle. Here ultrahigh photoconductivity is observed in deoxyribonucleic acid-molybdenum disulfide (DNA-MoS2) nano-biocomposite film by incorporating a high-concentration, well-percolated, and uniform MoS2 network in the ss-DNA matrix. This is achieved by utilizing DNA-MoS2 hydrogel formation, which results in crack-free, wafer-scale DNA-MoS2 nano-biocomposite films. Ultra-high photocurrent (5.5 mA at 1 V) with a record-high on/off ratio (1.3 × 106) is observed, five orders of magnitude higher than conventional biomaterials (≈101) reported so far. The incorporation of the Wely semimetal (Bismuth) as an electrical contact exhibits ultrahigh photoresponsivity (2.6 × 105 A W-1). Such high photoconductivity in DNA-MoS2 nano-biocomposite could bridge the gap between biology, electronics, and optics for innovative biomedicine, bioengineering, and neuroscience applications.

Graphene and metal-organic framework hybrids for high-performance sensors for lung cancer biomarker detection supported by machine learning augmentation.

Conventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast screening due to their limited availability, operational complexity, and high cost. As potential replacement, among several low-cost and portable methods, chemoresistive sensors for the detection of volatile organic compounds (VOCs) that represent biomarkers of lung cancer were explored as promising solutions, which unfortunately still face challenges. To address the key problems of these sensors, such as low sensitivity, high response time, and poor selectivity, this study presents the design of new chemoresistive sensors based on hybridised porous zeolitic imidazolate (ZIF-8) based metal-organic frameworks (MOFs) and laser-scribed graphene (LSG) structures, inspired by the architecture of the human lung. The sensing performance of the fabricated ZIF-8@LSG hybrid sensors was characterised using four dominant VOC biomarkers, including acetone, ethanol, methanol, and formaldehyde, which are identified as metabolomic signatures in lung cancer patients' exhaled breath. The results using simulated breath samples showed that the sensors exhibited excellent performance for a set of these biomarkers, including fast response (2-3 seconds), a wide detection range (0.8 ppm to 50 ppm), a low detection limit (0.8 ppm), and high selectivity, all obtained at room temperature. Intelligent machine learning (ML) recognition using the multilayer perceptron (MLP)-based classification algorithm was further employed to enhance the capability of these sensors, achieving an exceptional accuracy (approximately 96.5%) for the four targeted VOCs over the tested range (0.8-10 ppm). The developed hybridised nanomaterials, combined with the ML methodology, showcase robust identification of lung cancer biomarkers in simulated breath samples containing multiple biomarkers and a promising solution for their further improvements toward practical applications.

Low Ohmic contact resistance and high on/off ratio in transition metal dichalcogenides field-effect transistors via residue-free transfer.

Beyond-silicon technology demands ultrahigh performance field-effect transistors. Transition metal dichalcogenides provide an ideal material platform, but the device performances such as the contact resistance, on/off ratio and mobility are often limited by the presence of interfacial residues caused by transfer procedures. Here, we show an ideal residue-free transfer approach using polypropylene carbonate with a negligible residue coverage of ~0.08% for monolayer MoS2 at the centimetre scale. By incorporating a bismuth semimetal contact with an atomically clean monolayer MoS2 field-effect transistor on hexagonal boron nitride substrate, we obtain an ultralow Ohmic contact resistance of ~78 Ω µm, approaching the quantum limit, and a record-high on/off ratio of ~1011 at 15 K. Such an ultra-clean fabrication approach could be the ideal platform for high-performance electrical devices using large-area semiconducting transition metal dichalcogenides.

Boosting Phototransistor Performance in Monolayer TMDs via Multiple Reflections from DBR.

Transition-metal dichalcogenides (TMDs) are intensively studied for high-performance phototransistors. However, the device performance is limited by the single photoexcitation. Here, we show a unique strategy in which phototransistor performance can be boosted by fabricating the device on top of a distributed Bragg reflector (DBR). Monolayer molybdenum disulfide (MoS2) and tungsten disulfide (WS2) phototransistors were fabricated on DBR and SiO2 substrates for comparison. Furthermore, phototransistor performances including photocurrent, responsivity, photoinduced mobility, and subthreshold swing highlight 582 times enhancement in photoresponsivity ratio and 350 times enhancement in photocurrent ratio in the DBR sample using transparent graphene electrode and hBN encapsulation.

A novel feature extraction technique for pulmonary sound analysis based on EMD.

BACKGROUND AND OBJECTIVE: The stethoscope based auscultation technique is a primary diagnostic tool for chest sound analysis. However, the performance of this method is limited due to its dependency on physicians experience, knowledge and also clarity of the signal. To overcome this problem we need an automated computer-aided diagnostic system that will be competent in noisy environment. In this paper, a novel feature extraction technique is introduced for discriminating various pulmonary dysfunctions in an automated way based on pattern recognition algorithms. METHOD: In this work, the disease correlated relevant characteristics of lung sounds signals are identified in terms of statistical distribution parameters: mean, variance, skewness, and kurtosis. These features are extracted from selective morphological components of the mapped signal in the empirical mode decomposition domain. The feature set is fed to the classifier model to differentiate their corresponding classes. RESULTS: The significance of features developed are validated by conducting several experiments using supervised and unsupervised classifiers. Furthermore, the discriminating power of the proposed features is compared with three types of baseline features. The experimental result is evaluated by statistical analysis and also validated with physicians inference. CONCLUSIONS: It is found that the proposed features extraction technique is superior to the baseline methods in terms of classification accuracy, sensitivity and specificity. The developed method gives better results compared to baseline methods in any circumstance. The proposed method gives a higher accuracy of 94.16, sensitivity of 100 and specificity of 93.75 for an artificial neural network classifier.

A Noise Reduction Technique Based on Nonlinear Kernel Function for Heart Sound Analysis.

The main difficulty encountered in interpretation of cardiac sound is interference of noise. The contaminated noise obscures the relevant information, which are useful for recognition of heart diseases. The unwanted signals are produced mainly by lungs and surrounding environment. In this paper, a novel heart sound denoising technique has been introduced based on a combined framework of wavelet packet transform and singular value decomposition (SVD). The most informative node of the wavelet tree is selected on the criteria of mutual information measurement. Next, the coefficient corresponding to the selected node is processed by the SVD technique to suppress noisy component from heart sound signal. To justify the efficacy of the proposed technique, several experiments have been conducted with heart sound dataset, including normal and pathological cases at different signal to noise ratios. The significance of the method is validated by statistical analysis of the results. The biological information preserved in denoised heart sound signal is evaluated by the k-means clustering algorithm. The overall results show that the proposed method is superior than the baseline methods.

Respiratory sounds classification using statistical biomarker.

In this paper, we have proposed a new feature extraction technique based on statistical morphology of lung sound signal (LS). This work attempts to (i) generate certain intrinsic mode functions (IMFs), (ii) select a set of informative IMFs and (iii) extract relevant features from the selected IMFs and residue. Feature vector is formed by using the higher order moments: mean, standard deviation, skewness and kurtosis and employed as input to the classifier models for classification of three types of LS signals: crackle, wheeze and normal. The efficiency of these features is examined with an artificial neural network (ANN) classifier and compared the results with three baseline methods. The proposed method gives a superior performance in term of classification accuracy, sensitivity and specificity.

Enhancement of lung sounds based on empirical mode decomposition and Fourier transform algorithm.

BACKGROUND AND OBJECTIVE: There is always heart sound (HS) signal interfering during the recording of lung sound (LS) signals. This obscures the features of LS signals and creates confusion on pathological states, if any, of the lungs. In this work, a new method is proposed for reduction of heart sound interference which is based on empirical mode decomposition (EMD) technique and prediction algorithm. METHOD: In this approach, first the mixed signal is split into several components in terms of intrinsic mode functions (IMFs). Thereafter, HS-included segments are localized and removed from them. The missing values of the gap thus produced, is predicted by a new Fast Fourier Transform (FFT) based prediction algorithm and the time domain LS signal is reconstructed by taking an inverse FFT of the estimated missing values. RESULTS: The experiments have been conducted on simulated and recorded HS corrupted LS signals at three different flow rates and various SNR levels. The performance of the proposed method is evaluated by qualitative and quantitative analysis of the results. CONCLUSIONS: It is found that the proposed method is superior to the baseline method in terms of quantitative and qualitative measurement. The developed method gives better results compared to baseline method for different SNR levels. Our method gives cross correlation index (CCI) of 0.9488, signal to deviation ratio (SDR) of 9.8262, and normalized maximum amplitude error (NMAE) of 26.94 for 0 dB SNR value.

A non-invasive approach to investigation of ventricular blood pressure using cardiac sound features.

Heart sounds (HSs) are produced by the interaction of the heart valves, great vessels, and heart wall with blood flow. Previous researchers have demonstrated that blood pressure can be predicted by exploring the features of cardiac sounds. These features include the amplitude of the HSs, the ratio of the amplitude, the systolic time interval, and the spectrum of the HSs. A single feature or combinations of several features have been used for prediction of blood pressure with moderate accuracy. Experiments were conducted with three beagles under various levels of blood pressure induced by different doses of epinephrine. The HSs, blood pressure in the left ventricle and electrocardiograph signals were simultaneously recorded. A total of 31 records (18 262 cardiac beats) were collected. In this paper, 91 features in various domains are extracted and their linear correlations with the measured blood pressures are examined. These features are divided into four groups and applied individually at the input of a neural network to predict the left ventricular blood pressure (LVBP). The analysis shows that non-spectral features can track changes of the LVBP with lower standard deviation. Consequently, the non-spectral feature set gives the best prediction accuracy. The average correlation coefficient between the measured and the predicted blood pressure is 0.92 and the mean absolute error is 6.86 mmHg, even when the systolic blood pressure varies in the large range from 90 mmHg to 282 mmHg. Hence, systolic blood pressure can be accurately predicted even when using fewer HS features. This technique can be used as an alternative to real-time blood pressure monitoring and it has promising applications in home health care environments.

Characterization of porous core layer for controlling rare earth incorporation in optical fiber.

The porous core layer deposited by modified chemical vapour deposition process has been analyzed in terms of thickness, pore size distribution, homogeneity and characteristics of the soot particles to investigate their variation with deposition temperature and input vapour composition. The compositions selected were SiO(2), SiO(2)-GeO(2) and SiO(2)-P(2)O(5). Rare earth ions were incorporated into the deposit by a solution doping technique. The analysis of deposited microstructures was found to provide a quantitative indication about the rare earth incorporation and its variation with respect to process conditions. Thus the characterization provides a method of controlling rare earth doping and ultimate preform/fiber properties.

Prevalence of iodine deficiency disorders among schoolchildren in three blocks of Bardhaman District and Bardhaman Municipal area of West Bengal, India: a comparative study.

Urinary iodine levels in children (6-12 years) living in three rural blocks and in the municipal urban area of Bardhaman District, West Bengal, were analyzed to compare the status of recent iodine nutrition in the rural and urban population of the district. Goiter, indicating previous iodine status, was simultaneously estimated. Iodine levels in salt samples, that provide insight into the usage of iodized salt, were estimated. Data indicated that 56.6% of urban children and 51.1% of rural children were biochemically iodine repleted and had urinary iodine excretion (UIE) levels > or = 10microg/dl. Urban children (29.4%) and rural children (37.1%) were found to have goiter. Eighty percent and 50% of the rural and urban salt samples, respectively, were found to have iodine levels below 10 ppm; with significant urban-rural differences. The results indicate that iodine repletion in the surveyed area needs continuous surveillance of the proper distribution and use of iodized salt.

An Irregularity Measurement Based Cardiac Status Recognition Using Support Vector Machine.

An automated robust feature extraction technique is proposed in this paper based on inherent structural distribution of heart sound to analyze the phonocardiogram signal in presence of environmental noise and interference of lung sound signal. The structural complexity of the heart sound signal is estimated in terms of sample entropy using a nonlinear signal processing framework. The effectiveness of the feature is evaluated using a support vector machine under two different circumstances which include Gaussian noise and pulmonary perturbation. The analysis framework has been executed on a composite data set of 60 healthy and 60 pathological individuals for different SNR levels (-5 to 10 dB) and the performance accuracy is close to that of the clean signal. In addition, a comparative study has been done with conventional approaches which includes waveform analysis, spectral domain inspection, and spectrogram evaluation. The experimental results show that sample entropy based classification method gives an accuracy of 96.67% for clean data and 91.66% for noisy data of SNR 10 dB. The result suggests that the proposed method performs significantly well over the visual and audio test.

An automated tool for localization of heart sound components S1, S2, S3 and S4 in pulmonary sounds using Hilbert transform and Heron's formula.

ABSTRACT: The primary problem with lung sound (LS) analysis is the interference of heart sound (HS) which tends to mask important LS features. The effect of heart sound is more at medium and high flow rate than that of low flow rate. Moreover, pathological HS obscures LS in a higher degree than normal HS. To get over this problem, several HS reduction techniques have been developed. An important preprocessing step in HS reduction is localization of HS components. In this paper, a new HS localization algorithm is proposed which is based on Hilbert transform (HT) and Heron's formula. In the proposed method, the HS included segment is differentiated from the HS excluded segment by comparing their area with an adaptive threshold. The area of a HS component is calculated from the Hilbert envelope using Heron's triangular formula. The method is tested on real recorded and simulated HS corrupted LS signals. All the experiments are conducted under low, medium and high breathing flow rates. The proposed method shows a better performance than the comparative Singular Spectrum Analysis (SSA) based method in terms of accuracy (ACC), detection error rate (DER), false negative rate (FNR), and execution time (ET).

Reduction of heart sound interference from lung sound signals using empirical mode decomposition technique.

During the recording time of lung sound (LS) signals from the chest wall of a subject, there is always heart sound (HS) signal interfering with it. This obscures the features of lung sound signals and creates confusion on pathological states, if any, of the lungs. A novel method based on empirical mode decomposition (EMD) technique is proposed in this paper for reducing the undesired heart sound interference from the desired lung sound signals. In this, the mixed signal is split into several components. Some of these components contain larger proportions of interfering signals like heart sound, environmental noise etc. and are filtered out. Experiments have been conducted on simulated and real-time recorded mixed signals of heart sound and lung sound. The proposed method is found to be superior in terms of time domain, frequency domain, and time-frequency domain representations and also in listening test performed by pulmonologist.