Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

Analysis of speech and other sounds

This thesis comprises a study of various types of signal processing techniques, applied to the tasks of extracting information from speech, cough, and dolphin sounds. Established approaches to analysing speech sounds for the purposes of low data rate speech encoding, and more generally to determine the characteristics of the speech signal, are reviewed. Two new speech processing techniques, shift-and-add and CLEAN (which have previously been applied in the field of astronomical image processing), are developed and described in detail. Shift-and-add is shown to produce a representation of the long-term "average" characteristics of the speech signal. Under certain simplifying assumptions, this can be equated to the average glottal excitation. The iterative deconvolution technique called CLEAN is employed to deconvolve the shift-and-add signal from the speech signal. Because the resulting "CLEAN" signal has relatively few non-zero samples, it can be directly encoded at a low data rate. The performance of a low data rate speech encoding scheme that takes advantage of this attribute of CLEAN is examined in detail. Comparison with the multi-pulse LP C approach to speech coding shows that the new method provides similar levels of performance at medium data rates of about 16kbit/s. The changes that occur in the character of a person's cough sounds when that person is afflicted with asthma are outlined. The development and implementation of a micro-computer-based cough sound analysis system, designed to facilitate the ongoing study of these sounds, is described. The system performs spectrographic analysis on the cough sounds. A graphical user interface allows the sound waveforms and spectra to be displayed and examined in detail. Preliminary results are presented, which indicate that the spectral content of cough sounds are changed by asthma. An automated digital approach to studying the characteristics of Hector's dolphin vocalisations is described. This scheme characterises the sounds by extracting descriptive parameters from their time and frequency domain envelopes. The set of parameters so obtained from a sample of click sequences collected from free-ranging dolphins is analysed by principal component analysis. Results are presented which indicate that Hector's dolphins produce only a small number of different vocal sounds. In addition to the statistical analysis, several of the clicks, which are assumed to be used for echo-location, are analysed in terms of their range-velocity ambiguity functions. The results suggest that Hector's dolphins can distinguish targets separated in range by about 2cm, but are unable to separate targets that differ only in their velocity

Analysis of Respiratory Sounds: State of the Art

Objective This paper describes state of the art, scientific publications and ongoing research related to the methods of analysis of respiratory sounds. Methods and material Review of the current medical and technological literature using Pubmed and personal experience. Results The study includes a description of the various techniques that are being used to collect auscultation sounds, a physical description of known pathologic sounds for which automatic detection tools were developed. Modern tools are based on artificial intelligence and on technics such as artificial neural networks, fuzzy systems, and genetic algorithms… Conclusion The next step will consist in finding new markers so as to increase the efficiency of decision aid algorithms and tools

Algorithm-circuit co-design for detecting symptomatic patterns in biological signals

The advancement in scaled Silicon technology has accelerated the development of a wide range of applications in various fields including medical technology. It has immensely contributed to finding solutions for monitoring general health as well as alleviating intractable disorders in the form of implantable and wearable systems. This necessitates the development of energy efficient and functionally efficacious systems. This thesis has explored the algorithm-circuit co-design approach for developing an energy efficient epileptic seizure detection processor which could be used for implantable epilepsy prosthesis. Novel wavelet transform based algorithms are proposed for accurate detection of epileptic seizures. Energy efficient techniques at circuit level such as power and clock gating are utilized along with error resiliency at algorithm level to implement these algorithms in TSMC $65$nm bulk-Si technology. Furthermore, the methodology is extended to develop a generic pattern detection system, which could be used for health monitoring. The wavelet transform along with mathematical metrics and Mel cepstrum are used to develop an algorithm which can detect generic patterns in biological audio signals. The application of algorithm-circuit co-design methodology helps in practically implementing this system into a low power design. Using approximation of coefficients and multiplier-less implementation, the Mel cepstrum algorithm is modified to optimize the hardware cost without losing its functional efficacy. The system is user-specific and scalable for detecting various patterns in biological signals. The methodologies mentioned in this thesis are intended towards development of user-scalable, energy efficient and highly efficacious systems for detection of patterns in variety of biological signals

Mobile Sensing, Simulation and Machine-learning Techniques: Improving Observations in Public Health

Entering an era where mobile phones equipped with numerous sensors have become an integral part of our lives and wearable devices such as activity trackers are very popular, studying and analyzing the data collected by these devices can give insights to the researchers and policy makers about the ongoing illnesses, outbreaks and public health in general. In this regard, new machine learning techniques can be utilized for population screening, informing centers of disease control and prevention of potential threats and outbreaks. Big data streams if not present, will limit investigating the feasibility of such new techniques in this domain. To overcome this shortcoming, simulation models even if grounded by small-size data can represent a simple platform of the more complicated systems and then be utilized as safe and still precise environments for generating synthetic ground truth big data. The objective of this thesis is to use an agent-based model (ABM) which depicts a city consisting of restaurants, consumers, and an inspector, to investigate the practicability of using smartphones data in the machine-learning component of Hidden Markov Model trained by synthetic ground-truth data generated by the ABM model to detect food-borne related outbreaks and inform the inspector about them. To this end, we also compared the results of such arrangement with traditional outbreak detection methods. We examine this method in different formations and scenarios. As another contribution, we analyzed smart phone data collected through a real world experiment where the participants were using an application Ethica Data on their phones named. This application as the first platform turning smartphones into micro research labs allows passive sensor monitoring and sending over context-dependent surveys. The collected data was later analyzed to get insights into the participants' food consumption patterns. Our results indicate that Hidden Markov Models supplied with smart phone data provide accurate systems for foodborne outbreak detection. The results also support the applicability of smart phone data to obtain information about foodborne diseases. The results also suggest that there are some limitations in using Hidden Markov Models to detect the exact source of outbreaks