Voiceprint and machine learning models for early detection of bulbar dysfunction in ALS

Background and Objective: Bulbar dysfunction is a term used in amyotrophic lateral sclerosis (ALS). It refers to motor neuron disability in the corticobulbar area of the brainstem which leads to a dysfunction of speech and swallowing. One of the earliest symptoms of bulbar dysfunction is voice deterioration characterized by grossly defective articulation, extremely slow laborious speech, marked hypernasality and severe harshness. Recently, research efforts have focused on voice analysis to capture this dysfunction. The main aim of this paper is to provide a new methodology to diagnose this dysfunction automatically at early stages of the disease, earlier than clinicians can do. Methods: The study focused on the creation of a voiceprint consisting of a pattern generated from the quasi-periodic components of a steady portion of the five Spanish vowels and the computation of the five principal and independent components of this pattern. Then, a set of statistically significant features was obtained using multivariate analysis of variance and the outcomes of the most common supervised classification models were obtained. Results: The best model (random forest) obtained an accuracy, sensitivity and specificity of 88.3%, 85.0% and 95.0% respectively when classifying bulbar vs. control participants but the results worsened when classifying bulbar vs. no-bulbar patients (accuracy, sensitivity and specificity of 78.7%, 80.0% and 77.5% respectively for support vector machines). Due to the great uncertainty found in the annotated corpus of the ALS patients without bulbar involvement, we used a safe semi-supervised support vector machine to relabel the ALS participants diagnosed without bulbar involvement as bulbar and no-bulbar. The performance of the results obtained increased, especially when classifying bulbar and no-bulbar patients obtaining an accuracy, sensitivity and specificity of 91.0%, 83.3% and 100.0% respectively for support vector machines. This demonstrates that our model can improve the diagnosis of bulbar dysfunction compared not only with clinicians, but also the methods published to date. Conclusions: The results obtained demonstrate the efficiency and applicability of the methodology presented in this paper. It may lead to the development of a cheap and easy-to-use tool to identify this dysfunction in early stages of the disease and monitor progress.This work was approved by the Research Ethics Committee for Biomedical Research Projects (CEIm) at the Bellvitge University Hospital in Barcelona and was supported by the Ministerio de Economía y Competitividad (TIN2017-84553-C2-2-R) and the Ministerio de Ciencia e Innovacion (PID2020-113614RBC22). AT is a member of CIMNE, a Severo Ochoa Centre of Excellence (2019-2023) under grant CEX2018-000797-S, funded by MCIN/AEI/10.13039/501100011033. The Neurology Department of the Bellvitge University Hospital in Barcelona permitted the recording of the voices of the participants in its facilities. The clinical records were provided by Carlos Augusto Salazar Talavera. Dr. Marta Fulla and Maria Carmen Majos Bellmunt contributed advice about the process of eliciting the sounds

Measuring instantaneous and spectral information entropies by shannon entropy of choi-williams distribution in the context of electroencephalography

The theory of Shannon entropy was applied to the Choi-Williams time-frequency distribution (CWD) of time series in order to extract entropy information in both time and frequency domains. In this way, four novel indexes were defined: (1) partial instantaneous entropy, calculated as the entropy of the CWD with respect to time by using the probability mass function at each time instant taken independently; (2) partial spectral information entropy, calculated as the entropy of the CWD with respect to frequency by using the probability mass function of each frequency value taken independently; (3) complete instantaneous entropy, calculated as the entropy of the CWD with respect to time by using the probability mass function of the entire CWD; (4) complete spectral information entropy, calculated as the entropy of the CWD with respect to frequency by using the probability mass function of the entire CWD. These indexes were tested on synthetic time series with different behavior (periodic, chaotic and random) and on a dataset of electroencephalographic (EEG) signals recorded in different states (eyes-open, eyes-closed, ictal and non-ictal activity). The results have shown that the values of these indexes tend to decrease, with different proportion, when the behavior of the synthetic signals evolved from chaos or randomness to periodicity. Statistical differences (p-value < 0.0005) were found between values of these measures comparing eyes-open and eyes-closed states and between ictal and non-ictal states in the traditional EEG frequency bands. Finally, this paper has demonstrated that the proposed measures can be useful tools to quantify the different periodic, chaotic and random components in EEG signals. © 2014 by the authors; licensee MDPI, Basel, Switzerland.Peer ReviewedPostprint (published version

A Machine-Learning Model for Lung Age Forecasting by Analyzing Exhalations

Spirometers are important devices for following up patients with respiratory diseases. These are mainly located only at hospitals, with all the disadvantages that this can entail. This limits their use and consequently, the supervision of patients. Research efforts focus on providing digital alternatives to spirometers. Although less accurate, the authors claim they are cheaper and usable by many more people worldwide at any given time and place. In order to further popularize the use of spirometers even more, we are interested in also providing user-friendly lung-capacity metrics instead of the traditional-spirometry ones. The main objective, which is also the main contribution of this research, is to obtain a person’s lung age by analyzing the properties of their exhalation by means of a machine-learning method. To perform this study, 188 samples of blowing sounds were used. These were taken from 91 males (48.4%) and 97 females (51.6%) aged between 17 and 67. A total of 42 spirometer and frequency-like features, including gender, were used. Traditional machine-learning algorithms used in voice recognition applied to the most significant features were used. We found that the best classification algorithm was the Quadratic Linear Discriminant algorithm when no distinction was made between gender. By splitting the corpus into age groups of 5 consecutive years, accuracy, sensitivity and specificity of, respectively, 94.69%, 94.45% and 99.45% were found. Features in the audio of users’ expiration that allowed them to be classified by their corresponding lung age group of 5 years were successfully detected. Our methodology can become a reliable tool for use with mobile devices to detect lung abnormalities or diseases.This research was funded by the Spanish Ministerio de Ciencia e Innovación under contract PID2020-113614RB-C22

Transcription factor binding site detection through position cross-mutual information variability analysis

Regulatory sequence detection is a fundamental challenge in computational biology. One key process in protein synthesis starts with the binding of the transcription factor to its binding site. Different sites can show binding to the same factor. This variability found in binding sequences increases the difficulty of their detection using computational algorithms. In this manuscript, a method for the detection of binding sites is proposed, based on the correlation between binding sequence positions through information theoretical measures. Efficiency values of the method are reported in the form of Receiver Operating Characteristic curves on the detection of different transcription factors of the Saccharomyces cerevisiae organism. We compare our results with other known motif detection Motif Discovery scan (MDscan).Peer ReviewedPostprint (published version

Non-linear dynamic analysis of RR signals in patients with and without excessive daytime sleepiness

Linear and non-linear measures applied to heart rate variability (HRV) can be used to quantify modulation of the sympathetic and parasympathetic branches of the autonomic nervous system. RR signals were obtained from the ECG recorded during five Maintenance of Wakefulness (MWT) and Multiple Sleep Latency (MSLT) tests alternated throughout the day from patients suffering sleep disturbance. Two different end-points were considered: Study A, excessive daytime sleepiness (EDS) versus without daytime sleepiness (WDS); Study B, Pre-CPAP versus Post-CP AP (continuous positive airway pressure therapy) in EDS. Measures obtained from spectral analysis (PSD), time-frequency representation (TFR), auto-correntropy (ACORR) and auto-mutual-information function (AMIF) were applied to describe autonomic nervous system activity and RR regularity. Statistical differences between EDS and WDS groups were found in MSLT events. During MSLT, the parasympathetic activity and RR regularity in EDS were affected by CPAP therapy. Non-linear measures obtained from EDS in Post-CPAP differed from Pre-CPAP (p-value<0.05) and tended to be similar to WDS.Peer ReviewedPostprint (published version

Caracterización del electroencefalograma mediante etropía multiescala para la detección de la somnolencia

La somnolencia diurna excesiva es un o de los síntomas provenientes de varios trastornos relacionados con el sueño. Diferentes investigaciones afirman que hay más personas afectadas de somnolencia diurna que las diagnosticadas clínicamente , por este motivo se realizan estudios clínicos para detectarla. Los protocolos p ara la identificación de somnolencia son complica dos, largos y molestos para los pacientes . Esto conlleva a la necesidad de realizar estudios basados en el procesado de la señal electrocardiográfica (EEG) para la detección automática de esta enfermedad. El análisis de la señal EEG proporciona información valios a y de forma no invasiva sobre el esta do del paciente. Para caracterizar la somnolencia, en el presente estudio se han desarrol lado dos metodologías basadas en el análisis lineal de la señal EEG en el dominio de la frecuencia (densidad de potencia espectral (PSD)) y en el análisis no lineal de la señal en el dominio del tiempo (entropía multi escala (MSE)). Para ello se ha analizado la señal EEG de 19 electrodos superficiales . Diferentes índices han sido definidos con el fin de describir el comportamiento del E EG entre pacientes con somnolencia diurna (CSD) y pacientes sanos ( sin somnolencia diurna (S S D )). Se ha encontrado que tanto las técnicas MSE como PSD contribuyen a destacar l a s características del EEG de l o s dos grupos de pacientes (CSD y SSD ) y por lo tanto, facilitan encontrar índices que caracterizan la enfermedad y así detectar la más fácilmente .Postprint (author's final draft

Measuring instantaneous and spectral information entropies by shannon entropy of choi-williams distribution in the context of electroencephalography

The theory of Shannon entropy was applied to the Choi-Williams time-frequency distribution (CWD) of time series in order to extract entropy information in both time and frequency domains. In this way, four novel indexes were defined: (1) partial instantaneous entropy, calculated as the entropy of the CWD with respect to time by using the probability mass function at each time instant taken independently; (2) partial spectral information entropy, calculated as the entropy of the CWD with respect to frequency by using the probability mass function of each frequency value taken independently; (3) complete instantaneous entropy, calculated as the entropy of the CWD with respect to time by using the probability mass function of the entire CWD; (4) complete spectral information entropy, calculated as the entropy of the CWD with respect to frequency by using the probability mass function of the entire CWD. These indexes were tested on synthetic time series with different behavior (periodic, chaotic and random) and on a dataset of electroencephalographic (EEG) signals recorded in different states (eyes-open, eyes-closed, ictal and non-ictal activity). The results have shown that the values of these indexes tend to decrease, with different proportion, when the behavior of the synthetic signals evolved from chaos or randomness to periodicity. Statistical differences (p-value < 0.0005) were found between values of these measures comparing eyes-open and eyes-closed states and between ictal and non-ictal states in the traditional EEG frequency bands. Finally, this paper has demonstrated that the proposed measures can be useful tools to quantify the different periodic, chaotic and random components in EEG signals. © 2014 by the authors; licensee MDPI, Basel, Switzerland.Peer Reviewe

Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals

To remove peak and spike artifacts in biological time series has represented a hard challenge in the last decades. Several methods have been implemented mainly based on adaptive filtering in order to solve this problem. This work presents an algorithm for removing peak and spike artifacts based on a threshold built on the analytic signal envelope. The algorithm was tested on simulated and real EEG signals that contain peak and spike artifacts with random amplitude and frequency occurrence. The performance of the filter was compared with commonly used adaptive filters. Three indexes were used for testing the performance of the filters: Correlation coefficient (p), mean of coherence function (C), and rate of absolute error (RAE). All these indexes were calculated between filtered signal and original signal without noise. It was found that the new proposed filter was able to reduce the amplitude of peak and spike artifacts with rho > 0.85, C > 0.8, and RAE 1).Peer Reviewe

The ecology of Polymyxa betae, a fungal root parasite of Beta vulgaris

SIGLEAvailable from British Library Document Supply Centre- DSC:D60868 / BLDSC - British Library Document Supply CentreGBUnited Kingdo