Measuring Coupling of Rhythmical Time Series Using Cross Sample Entropy and Cross Recurrence Quantification Analysis

The aim of this investigation was to compare and contrast the use of cross sample entropy (xSE) and cross recurrence quantification analysis (cRQA) measures for the assessment of coupling of rhythmical patterns. Measures were assessed using simulated signals with regular, chaotic, and random fluctuations in frequency, amplitude, and a combination of both. Biological data were studied as models of normal and abnormal locomotor-respiratory coupling. Nine signal types were generated for seven frequency ratios. Fifteen patients with COPD (abnormal coupling) and twenty-one healthy controls (normal coupling) walked on a treadmill at three speeds while breathing and walking were recorded. xSE and the cRQA measures of percent determinism,maximum line,mean line, and entropy were quantified for both the simulated and experimental data. In the simulated data, xSE, percent determinism, and entropy were influenced by the frequency manipulation. The 1 : 1 frequency ratio was different than other frequency ratios for almost all measures and/or manipulations. The patients with COPD used a 2 : 3 ratio more often and xSE, percent determinism,maximum line, mean line, and cRQA entropy were able to discriminate between the groups. Analysis of the effects of walking speed indicated that all measures were able to discriminate between speeds

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Towards vocal-behaviour and vocal-health assessment using distributions of acoustic parameters

Voice disorders at different levels are affecting those professional categories that make use of voice in a sustained way and for prolonged periods of time, the so-called occupational voice users. In-field voice monitoring is needed to investigate voice behaviour and vocal health status during everyday activities and to highlight work-related risk factors. The overall aim of this thesis is to contribute to the identification of tools, procedures and requirements related to the voice acoustic analysis as objective measure to prevent voice disorders, but also to assess them and furnish proof of outcomes during voice therapy. The first part of this thesis includes studies on vocal-load related parameters. Experiments were performed both in-field and in laboratory. A one-school year longitudinal study of teachers’ voice use during working hours was performed in high school classrooms using a voice analyzer equipped with a contact sensor; further measurements took place in the semi-anechoic and reverberant rooms of the National Institute of Metrological Research (I.N.Ri.M.) in Torino (Italy) for investigating the effects of very low and excessive reverberation in speech intensity, using both microphones in air and contact sensors. Within this framework, the contributions of the sound pressure level (SPL) uncertainty estimation using different devices were also assessed with proper experiments. Teachers adjusted their voice significantly with noise and reverberation, both at the beginning and at the end of the school year. Moreover, teachers who worked in the worst acoustic conditions showed higher SPLs and a worse vocal health status at the end of the school year. The minimum value of speech SPL was found for teachers in classrooms with a reverberation time of about 0.8 s. Participants involved into the in-laboratory experiments significantly increased their speech intensity of about 2.0 dB in the semi-anechoic room compared with the reverberant room, when describing a map. Such results are related to the speech monitorings performed with the vocal analyzer, whose uncertainty estimation for SPL differences resulted of about 1 dB. The second part of this thesis was addressed to vocal health and voice quality assessment using different speech materials and devices. Experiments were performed in clinics, in collaboration with the Department of Surgical Sciences of Università di Torino (Italy) and the Department of Clinical Science, Intervention and Technology of Karolinska Institutet in Stockholm (Sweden). Individual distributions of Cepstral Peak Prominence Smoothed (CPPS) from voluntary patients and control subjects were investigated in sustained vowels, reading, free speech and excerpted vowels from continuous speech, which were acquired with microphones in air and contact sensors. The main influence quantities of the estimated cepstral parameters were also identified, which are the fundamental frequency of the vocalization and the broadband noise superimposed to the signal. In addition, the reliability of CPPS estimation with respect to the frequency content of the vocal spectrum was evaluated, which is mainly dependent on the bandwidth of the measuring chain used to acquire the vocal signal. Regarding the speech materials acquired with the microphone in air, the 5th percentile resulted the best statistic for CPPS distributions that can discriminate healthy and unhealthy voices in sustained vowels, while the 95th percentile was the best in both reading and free speech tasks. The discrimination thresholds were 15 dB (95\% Confidence Interval, CI, of 0.7 dB) and 18 dB (95\% CI of 0.6 dB), respectively, where lower values indicate a high probability to have unhealthy voice. Preliminary outcomes on excerpted vowels from continuous speech stated that a CPPS mean value lower than 14 dB designates pathological voices. CPPS distributions were also effective as proof of outcomes after interventions, e.g. voice therapy and phonosurgery. Concerning the speech materials acquired with the electret contact sensor, a reasonable discrimination power was only obtained in the case of sustained vowel, where the standard deviation of CPPS distribution higher than 1.1 dB (95\% CI of 0.2 dB) indicates a high probability to have unhealthy voice. Further results indicated that a reliable estimation of CPPS parameters is obtained provided that the frequency content of the spectrum is not lower than 5 kHz: such outcome provides a guideline on the bandwidth of the measuring chain used to acquire the vocal signal

Models and Analysis of Vocal Emissions for Biomedical Applications

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

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

Characteristics and coupling of cardiac and locomotor rhythms during treadmill walking tasks

Studying the variability of physiological subsystems (e.g., cardiac and locomotor control systems) has been insightful in understanding how functional and dysfunctional patterns emerge within their behaviors. The coupling of these subsystems (termed cardiolocomotor coupling) is believed to be important to maintain healthy functioning in the diverse conditions in which individuals must operate. Aging and pathology result in alterations to both the patterns of individual systems, as well as to how those systems couple to each other. By examining cardiac and locomotor rhythms concurrently during treadmill walking, it is possible to ascertain how these two rhythms relate to each other in different populations (i.e., younger and older adults) and with varying task constraints (i.e., a gait synchronization task or fast walking task). The purpose of this research was to simultaneously document the characteristics of cardiac and gait rhythms in younger (18-35 yrs) and older (63-80 yrs) healthy adults while walking and to establish the extent to which changes in these systems are coupled when gait is constrained. This study consisted of two repeated-measures experiments that participants completed on two separate days. Both experiments consisted of three 15-minute phases. During the first (baseline) and third (retention) phases of both experiments, participants walked with no cues or specific instructions at their preferred walking speed. During the second phase, participants were asked to synchronize their step falls to the timing of a visual cue (experiment 1) or to walk at 125% of their preferred walking speed (experiment 2). Fifty-one healthy adults (26 older, 67.67±4.70 yrs, 1.72±0.09 m, 70.13±14.30 kg; 25 younger, 24.57±4.29 yrs, 1.76±0.09 m, 73.34±15.35 kg) participated in this study. Participants’ cardiac rhythms (R-R interval time series) and locomotor rhythms (stride interval, step width, and step length time series) were measured while walking on a treadmill. Characteristics of variability in cardiac and locomotor rhythms and the coupling between cardiac and gait rhythms were measured. Results revealed that younger and older healthy adults alter gait patterns similarly when presented with a gait synchronization or fast walking task and that these tasks also alter cardiac patterns. Likewise, both groups exhibited enhanced cardiolocomotor coupling when tasked with a step timing constraint or increased speed during treadmill walking. Combined, these findings suggest that walking tasks likely alter both locomotor and cardiac dynamics and the coupling of physiological subsystems could be insightful in understanding the diverse effects aging and pathology have on individuals

Quantitative analysis of hypoglycemia-induced EEG alterations in type 1 diabetes

The main risk for patients affected by type 1 diabetes (T1D) is to fall in hypoglycemia, an event which leads to both short and long-terms automatic failure and can be life-threatening especially when occurs at night without subject awareness. Moreover, T1D patients can develop asymptomatic hypoglycemia, reducing the prompt response of the counterregulatory system triggered by the fall in blood glucose. Avoiding hypoglycemia is important in children and adolescents because hypoglycemia episodes may have clinically relevant effects on cognition. Also in adults, cognitive tests assessed that hypoglycemia results in altered cerebral activity, most likely due to the complete dependence of the brain for glucose supply. The first organ influenced by this fall of glucose in the blood is the brain. Indeed, a lot of studies proved the mirroring of cognitive dysfunction due to hypoglycemia in the spectral power of the electroencephalogram (EEG) signal. In particular, the increase of the power in low frequency EEG bands is a well-known effect during hypoglycemia that seems more pronounced in the EEG recording in the posterior areas of the brain. Pilot studies about the real-time processing of the EEG signal to detect hypoglycemia have indicated that it might be possible to alert the patients by means of EEG analysis. The main advantages in exploiting EEG analysis is that the blood glucose threshold to enter in hypoglycemia has large inter-subjects variations, on the contrary the EEG onset in general occurs before the state of hypoglycemia is critical, i.e., the brain starts to experience neuroglycopoenia and its functions completely fail. The main aim of this work is to broaden out the quantitative analysis on the altered EEG activity due to hypoglycemia in T1D patients to identify potential margins of improvement in EEG processing and further features sensitive to hypoglycemia. In particular, the analyses are extended to different domains, i.e., time and frequency domains, to deepen the knowledge on the effects of hypoglycemia in the brain. So far, studies in the literature have mainly evaluated these changes only on a single EEG channel level on the frequency domain, but limited information is available on the hypoglycemia influence on brain network dynamics and on connection between different brain areas. To do so, this dissertation is structured in 7 chapters, briefly presented below. Chapter 1 will start with a brief overview about the impact of T1D and its main effects on daily life. Moreover, the main consequences of hypoglycemia in human brain will be described by reporting the main findings in the literature. Chapter 2 will present the database where EEG data and blood glucose samples were collected in parallel for about 8 h in 31 T1D hospitalized patients during an hyperinsulinemic - hypoglycemic clamp experiment. Chapter 3 will address on the main effects of hypoglycemia in the frequency domain. After testing the well-known changes in the spectral power of the EEG signal during hypoglycemia, a multivariate analysis based on the concept of Information Partial Directed Coherence will be presented. In particular, we will confirm the general slowing in the frequency domain and we will show how hypoglycemia affects the EEG functional connectivity. Chapter 4 will consider the effects of hypoglycemia on EEG complexity. Fractal dimension features, describing both amplitude and frequency properties, will be computed and compared with the results based on Sample Entropy. We will reveal a decrease of EEG signal complexity in the hypoglycemic condition. Chapter 5 will focus on the consequences of hypoglycemia in the so-called microstates or "athoms of thought". We will hypothesize that the changes in the frequency domain and the decrease of the EEG signal complexity in hypoglycemia have in common the same resting EEG electric potential amplitude map. Chapter 6 will describe how hypoglycemia influences the results of cognitive tests, and the relationship between the drop in the tests performance and the EEG quantitative measures presented in the previous chapters. We will find a direct correlation among the changes in the power spectra, the cognitive tests performance and the changes of one resting EEG electric potential amplitude map. Eventually, Chapter 7 will close the dissertation by interpreting the ensemble of the results from both the medical and engineering point of view, and presenting the possible future developments of this work

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 4th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2005, held 29-31 October 2005, 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

Voice disorders assessed by (cross-) Sample Entropy of electroglottogram and microphone signals

The quantitative analysis of vocal disorders by nonlinear signal processing methods has been extensively used in the last two decades. In this work, two algorithms for nonlinear time-series analysis, Sample Entropy and cross-Sample Entropy, are used on electroglottogram (EGG) and microphone (MIC) signals recorded from 51 normal and 80 dysphonic subjects, to obtain summary measures of voice disorders through SampEn and cross-SampEn indices. Such parameters quantify, respectively, the degree of irregularity (in the sense of self-dissimilarity) within a time-series and of asynchrony (in the sense of cross-dissimilarity) between two distinct time-series. The aims of this work are: to determine if statistically significant differences in terms of signal irregularity quantified by SampEn occur between normal and pathological subjects, investigating whether or not such differences can be equally seen in EGG and MIC; to assess if cross-SampEn reveals different degrees of asynchrony between EGG and MIC signals in the two groups. Results show that SampEn in pathological subjects is higher than in normal subjects for both EGG and MIC time-series, with a statistically significant difference detectable from both signals (Pe < 10-4 for EGG and Pe < 10-7 for MIC). Cross-SampEn exhibits a statistically significant difference too, showing a higher degree of cross-dissimilarity between EGG and MIC time-series for pathological subjects (Pe < 10-4). In conclusion, SampEn and cross-SampEn well quantify the increase of complexity of both EGG and MIC signals and the decrease of their cross-similarity in presence of vocal disorders. Thanks to the complementarity of nonlinear indicators to the traditionally considered linear ones, SampEn and cross-SampEn appear as suitable candidates to enter the pool of approaches to investigate speech pathologies and to obtain potentially new insights on their nature