Recommendations Related To Wheeze Sound Data Acquisition

In the field of computerized respiratory sounds,a reliable data set with a sufficient number of subjects is required for the development of wheeze detection algorithm or for further analysis.Validated and accurate data is a critical issue in the field of research.In this study,the protocol related to wheeze sound data acquisition is discussed.Previously,most articles focused on wheeze detection or its parametric analysis,but no consideration was given to data acquisition.Second major purpose of this study is to exhibit particulars of our dataset which was attained for future analysis.We compile a database with a sufficient and reliable number of cases with all essential details,in contrast to commercially available wheeze sound data used for research,freely available online data on websites and data used to train medical students for auscultation

An Overview Of Breath Phase Detection – Techniques & Applications

The main aim of this study is to provide an overview on the state of the art techniques (acoustic and non-acoustic approaches) involved in breath phase detection and to highlight applications where breath phase detection is vital. Both acoustic and non-acoustic approaches are summarized in detail. The non-acoustic approach involves placement of sensors or flow measurement devices to estimate the breath phases, whereas the acoustic approach involves the use of sophisticated signal processing methods on respiratory sounds to detect breath phases. This article also briefly discusses the advantages and disadvantages of the acoustic and non-acoustic approaches of breath phase detection. The literature reveals that recent advancements in computing technology open avenues for researchers to apply sophisticated signal processing techniques and artificial intelligence algorithms to detect the breath phases in a non-invasive way. Future works that can be implemented after detecting the breath phases are also highlighted in this article

An Overview of Breath Phase Detection – Techniques & Applications

The main aim of this study is to provide an overview on the state of the art techniques (acoustic and nonacoustic approaches) involved in breath phase detection and to highlight applications where breath phase detection is vital. Both acoustic and non-acoustic approaches are summarized in detail. The non-acoustic approach involves placement of sensors or flow measurement devices to estimate the breath phases, whereas the acoustic approach involves the use of sophisticated signal processing methods on respiratory sounds to detect breath phases. This article also briefly discusses the advantages and disadvantages of the acoustic and non-acoustic approaches of breath phase detection. The literature reveals that recent advancements in computing technology open avenues for researchers to apply sophisticated signal processing techniques and artificial intelligence algorithms to detect the breath phases in a non-invasive way. Future works that can be implemented after detecting the breath phases are also highlighted in this article

Characterization And Classification Of Asthmatic Wheeze Sounds According To Severity Level Using Spectral Integrated Features

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 informativ

Identification Of Asthma Severity Levels Through Wheeze Sound Characterization And Classification Using Integrated Power Features

This study aimed to investigate and classify wheeze sound characteristics according to asthma severity levels (mild, moderate and severe) using integrated power (IP) features. Method: Validated and segmented wheeze sounds were obtained from the lower lung base (LLB) and trachea recordings of 55 asthmatic patients with different severity levels during tidal breathing manoeuvres. From the segments, nine datasets were obtained based on the auscultation location, breath phases and their combination. In this study, IP features were extracted for assessing asthma severity. Subsequently, univariate and multivariate (MANOVA) statistical analyses were separately implemented to analyse behaviour of wheeze sounds according to severity levels. Furthermore, the ensemble (ENS), knearest- neighbour (KNN) and support vector machine (SVM) classifiers were applied to classify the asthma severity levels. Results and conclusion: The univariate results of this study indicated that the majority of features significantly discriminated (p < 0.05) the severity levels in all the datasets. The MANOVA results yielded significantly (p < 0.05) large effect size in all datasets (including LLB-related) and almost all post hoc results were significant(p < 0.05). A comparison ofthe performance of classifiers revealed that eight ofthe nine datasets showed improved performance with the ENS classifier. The Trachea inspiratory (T-Inspir) dataset produced the highest performance. The overall best positive predictive rate (PPR) for the mild, moderate and severe severity levels were 100% (KNN), 92% (SVM) and 94% (ENS) respectively. Analysis related to auscultation locations revealed that tracheal wheeze sounds are more specific and sensitive predictors of asthma severity. Additionally, phase related investigations indicated that expiratory and inspiratory wheeze sounds are equally informative for the classification of asthma severit

Automation of the anesthetic process: New computer-based solutions to deal with the current frontiers in the assessment, modeling and control of anesthesia

The current trend in automating the anesthetic process focuses on developing a system for fully controlling the different variables involved in anesthesia. To this end, several challenges need to be addressed first. The main objective of this thesis is to propose new solutions that provide answers to the current problems in the field of assessing, modeling and controlling the anesthetic process. Undoubtedly, the main handicap to the development of a comprehensive proposal lies in the absence of a reliable measure of analgesia. This thesis proposes a novel fuzzy-logic-based scheme to evaluate the impact of including a new variable in a decision-making process. This scheme is validated by way of a preliminary analysis of the Analgesia Nociception Index (ANI) monitor on analgesic drug titration. Furthermore, the capacity of the ANI monitor to provide information to replicate the decisions of the experts in different clinical situations is studied. To this end, different artificial intelligence-based algorithms are used: specifically, the suitability of this index is evaluated against other variables commonly used in clinical practice. Regarding the modeling of anesthesia, this thesis presents an adaptive model that allows characterizing the pharmacological interaction effects between the hypnotic and analgesic drug on the depth of hypnosis. In addition, the proposed model takes into account both inter- and intra-patient variabilities observed in the response of the subjects. Finally, this work presents the synthesis of a robust optimal PID controller for regulating the depth of hypnosis by considering the effect of the uncertainties derived from the patient's pharmacological response. Moreover, a study is conducted on the limitations introduced when using a PID controller versus the development of higher order solutions under the same clinical and technical considerations