Ítems relacionados
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An algorithm for obtaining the frequency and the times of respiratory phases from nasal and oral acoustic signals(Institute of Advanced Engineering and Science, 2023-02-01)This work proposes a computational algorithm which extracts the frequency, timings and signal segments corresponding to respiratory phases, through buccal and nasal acoustic signal processing. The proposal offers a computational solution for medical applications which require on-site or remote patient monitoring and evaluation of pulmonary pathologies, such as coronavirus disease 2019 (COVID-19). The state of the art presents a few respiratory evaluation proposals through buccal and nasal acoustic signals. Most proposals focus on respiratory signals acquired by a medical professional, using stethoscopes and electrodes located on the thorax. In this case the signal acquisition process is carried out through the use of a low cost and easy to use mask, which is equipped with strategically positioned and connected electret microphones, to maximize the proposed algorithm's performance. The algorithm employs signal processing techniques such as signal envelope detection, decimation, fast Fourier transform (FFT) and detection of peaks and time intervals via estimation of local maxima and minima in a signal's envelope. For the validation process a database of 32 signals of different respiratory modes and frequencies was used. Results show a maximum average error of 2.23% for breathing rate, 2.81% for expiration time and 3.47% for inspiration time.Acceso abierto
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Design and implementation of a prototype of an electrooculographic signal processing system oriented to control - by wink - the instant messaging environment Telegram for users with motor limitations in upper limbs(Signal Processing, Images and Computer Vision (STSIVA), 2015 20th Symposium on, 2015-09)This paper is based upon the design and implementation of a Human Computer Interface (HCI) as a support tool for people with upper limb disabilities, especially those with difficulties in speech, so as to interact and communicate by Instant Messaging Environments (IME). For this, we have implemented the appropriate hardware for acquisition and conditioning of the electrooculografic signal. As for the software, we have developed a wink pattern recognition algorithm composed of three processes: zero crossing technique adapted to the level of direct current (DC), comparison of the energy threshold and the Pearson correlation coefficient. Besides, a virtual keyboard was implemented to allow users to select, by means of winks, the characters that they wanted to transmit messages by the IME Telegram. Finally, the system was tested by potential users obtaining a success rate of 94.90% that proof how effective and reliable it is.Acceso restringido temporalmente
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Correspondence Between TOVA Test Results and Characteristics of EEG Signals Acquired Through the Muse Sensor in Positions AF7–AF8(2021-01-01)This paper seeks to study the correspondence between the results of the test of variable of attention (TOVA) and the signals acquired by the Muse electroencephalogram (EEG) in the positions AF7 and AF8 of the cerebral cortex. There are a variety of research papers that estimates an index of attention in which the different characteristics in discrete signals of the brain activity were used. However, many of these results were obtained without contrasting them with standardized tests. Due to this fact, in the present work, the results will be compared with the score of the TOVA, which aims to identify an attention disorder in a person. The indicators obtained from the test are the response time variability, the average response time, and the d′ prime score. During the test, the characteristics of the EEG signals in the alpha, beta, theta, and gamma subbands such as the energy, average power, and standard deviation were extracted. For this purpose, the acquired signals are filtered to reduce the effect of the movement of the muscles near the cerebral cortex and then went through a subband decomposition process by applying transformed wavelet packets. The results show a well-marked correspondence between the parameters of the EEG signal of the indicated subbands and the visual attention indicators provided by TOVA. This correspondence was measured through Pearson’s correlation coefficient which had an average result of 0.8.Acceso restringido temporalmente
