17 research outputs found
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, mean of coherence function, and rate of absolute error. 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 > 0.85, C > 0.8, and RAE 1)
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 EconomÃa 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
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
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