Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing

Abstract

There are several pathologies attacking the central nervous system and diverse therapies for each specific disease. These therapies seek as far as possible to minimize or offset the consequences caused by these types of pathologies and disorders in the patient. Therefore, comprehensive neurological care has been performed by neurorehabilitation therapies, to improve the patients’ life quality and facilitating their performance in society. One way to know how the neurorehabilitation therapies contribute to help patients is by measuring changes in their brain activity by means of electroencephalograms (EEG). EEG data-processing applications have been used in neuroscience research to be highly computing- and data-intensive. Our proposal is an integrated system of Electroencephalographic, Electrocardiographic, Bioacoustic, and Digital Image Acquisition Analysis to provide neuroscience experts with tools to estimate the efficiency of a great variety of therapies. The three main axes of this proposal are: parallel or distributed capture, filtering and adaptation of biomedical signals, and synchronization in real epochs of sampling. Thus, the present proposal underlies a general system, whose main objective is to be a wireless benchmark in the field. In this way, this proposal could acquire and give some analysis tools for biomedical signals used for measuring brain interactions when it is stimulated by an external system during therapies, for example. Therefore, this system supports extreme environmental conditions, when necessary, which broadens the spectrum of its applications. In addition, in this proposal sensors could be added or eliminated depending on the needs of the research, generating a wide range of configuration limited by the number of CPU cores, i.e., the more biosensors, the more CPU cores will be required. To validate the proposed integrated system, it is used in a Dolphin-Assisted Therapy in patients with Infantile Cerebral Palsy and Obsessive–Compulsive Disorder, as well as with a neurotypical one. Event synchronization of sample periods helped isolate the same therapy stimulus and allowed it to be analyzed by tools such as the Power Spectrum or the Fractal Geometry.

Design of Automatic Scoring System for Oral English Test Based on Sequence Matching and Big Data Analysis

“…e normalized root mean square errors of the methods in literature [18], literature [22], and literature [23] were close, but both were larger than the proposed methods. erefore, it could be proved that the proposed method had low error and good overall scoring performance.…”

“…e statistical characteristic quantity of the pronunciation sequence of the oral English test was detected under the interference background, as shown in Figure 1. rough the scale and time delay estimation of the pronunciation sequence signal of the oral English test, the spectral analysis method was used for wavelet scale decomposition [18], and the dynamic distribution characteristic quantity of the oral English test was obtained as follows (see Figure 1).…”

“…In order to further prove the effectiveness of the proposed method, the normalized root mean square error of the proposed method was compared with that of literature [18] and literature [22], so as to test the accuracy of automatic scoring of oral English computer test by different methods.…”

Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications

“…As changes in carrier frequency are expected to be relatively small (mostly due to the Doppler effect [ 19 ]), a small low-frequency Hz range [ 20 , 21 , 22 ] was considered in this experiment, with a sampling frequency of Hz. This makes normalized frequency be in the range of [0.23, 0.25].…”

“…This 9is the normalized frequency that is considered while handling a frequency-estimation problem [ 20 ]. On the other hand, small ranges of small frequencies are expected in power systems (e.g., 50 Hz) and biosystems (for example, electroencephalograms (EEG), in the range of 0–40 Hz) [ 21 , 22 ].…”