Asthma severity identification from pulmonary acoustic signal for computerized decision support system.

OBJECTIVE: Breath sound has information about underlying pathology and condition of subjects. The purpose of this study was to examine asthmatic acuteness levels (Mild, Moderate, Severe) using frequency features extracted from wheeze sounds. Further, analysis was extended to observe behaviour of wheeze sounds in different datasets. METHODS: Segmented and validated wheeze sounds was collected from 55 asthmatic patients from the trachea and lower lung base (LLB) during tidal breathing maneuvers. Segmented wheeze sounds have been grouped in to nine datasets based on auscultation location, breath phases and a combination of phase and location. Frequency based features F25, F50, F75, F90, F99 and mean frequency (MF) were calculated from normalized power spectrum. Subsequently, multivariate analysis was performed. RESULTS: Generally frequency features observe statistical significance (p < 0.05) for the majority of datasets to differentiate severity level Ʌ = 0.432-0.939, F(12, 196-1534) = 2.731-11.196, p < 0.05, ɳ2 = 0.061-0.568. It was observed that selected features performed better (higher effect size) for trachea related samples Ʌ = 0.432-0.620, F(12, 196-498) = 6.575-11.196, p < 0.05, ɳ2 = 0.386-0.568. CONCLUSIONS: The results demonstrated dthat severity levels of asthmatic patients with tidal breathing can be identified through computerized wheeze sound analysis. In general, auscultation location and breath phases produce wheeze sounds with different characteristics.

Analysis of wheeze sounds during tidal breathing according to severity levels in asthma patients.

Objective: This study aimed to statistically analyze the behavior of time-frequency features in digital recordings of wheeze sounds obtained from patients with various levels of asthma severity (mild, moderate, and severe), and this analysis was based on the auscultation location and/or breath phase. Method: Segmented and validated wheeze sounds were collected from the trachea and lower lung base (LLB) of 55 asthmatic patients during tidal breathing maneuvers and grouped into nine different datasets. The quartile frequencies F25, F50, F75, F90 and F99, mean frequency (MF) and average power (AP) were computed as features, and a univariate statistical analysis was then performed to analyze the behavior of the time-frequency features. Results: All features generally showed statistical significance in most of the datasets for all severity levels [χ2 = 6.021-71.65, p < 0.05, η2 = 0.01-0.52]. Of the seven investigated features, only AP showed statistical significance in all the datasets. F25, F75, F90 and F99 exhibited statistical significance in at least six datasets [χ2 = 4.852-65.63, p < 0.05, η2 = 0.01-0.52], and F25, F50 and MF showed statistical significance with a large η2 in all trachea-related datasets [χ2 = 13.54-55.32, p < 0.05, η2 = 0.13-0.33]. Conclusion: The results obtained for the time-frequency features revealed that (1) the asthma severity levels of patients can be identified through a set of selected features with tidal breathing, (2) tracheal wheeze sounds are more sensitive and specific predictors of severity levels and (3) inspiratory and expiratory wheeze sounds are almost equally informative.

Big Data acquired by Internet of Things-enabled industrial multichannel wireless sensors networks for active monitoring and control in the smart grid Industry 4.0.

Smart Grid Industry 4.0 (SGI4.0) defines a new paradigm to provide high-quality electricity at a low cost by reacting quickly and effectively to changing energy demands in the highly volatile global markets. However, in SGI4.0, the reliable and efficient gathering and transmission of the observed information from the Internet of Things (IoT)-enabled Cyber-physical systems, such as sensors located in remote places to the control center is the biggest challenge for the Industrial Multichannel Wireless Sensors Networks (IMWSNs). This is due to the harsh nature of the smart grid environment that causes high noise, signal fading, multipath effects, heat, and electromagnetic interference, which reduces the transmission quality and trigger errors in the IMWSNs. Thus, an efficient monitoring and real-time control of unexpected changes in the power generation and distribution processes is essential to guarantee the quality of service (QoS) requirements in the smart grid. In this context, this paper describes the dataset contains measurements acquired by the IMWSNs during events monitoring and control in the smart grid. This work provides an updated detail comparison of our proposed work, including channel detection, channel assignment, and packets forwarding algorithms, collectively called CARP [1] with existing G-RPL [2] and EQSHC [3] schemes in the smart grid. The experimental outcomes show that the dataset and is useful for the design, development, testing, and validation of algorithms for real-time events monitoring and control applications in the smart grid.

Characterization and classification of asthmatic wheeze sounds according to severity level using spectral integrated features.

OBJECTIVE: This study aimed to investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate and severe) using spectral integrated (SI) features. METHOD: Segmented and validated wheeze sounds were obtained from auscultation recordings of the trachea and lower lung base of 55 asthmatic patients during tidal breathing manoeuvres. The segments were multi-labelled into 9 groups based on the auscultation location and/or breath phases. Bandwidths were selected based on the physiology, and a corresponding SI feature was computed for each segment. Univariate and multivariate statistical analyses were then performed to investigate the discriminatory behaviour of the features with respect to the severity levels in the various groups. The asthmatic severity levels in the groups were then classified using the ensemble (ENS), support vector machine (SVM) and k-nearest neighbour (KNN) methods. RESULTS AND CONCLUSION: All statistical comparisons exhibited a significant difference (p < 0.05) among the severity levels with few exceptions. In the classification experiments, the ensemble classifier exhibited better performance in terms of sensitivity, specificity and positive predictive value (PPV). The trachea inspiratory group showed the highest classification performance compared with all the other groups. Overall, the best PPV for the mild, moderate and severe samples were 95% (ENS), 88% (ENS) and 90% (SVM), respectively. With respect to location, the tracheal related wheeze sounds were most sensitive and specific predictors of asthma severity levels. In addition, the classification performances of the inspiratory and expiratory related groups were comparable, suggesting that the samples from these locations are equally informative.