Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/23/6991 |
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| author | Jesús Jaime Moreno Escobar Oswaldo Morales Matamoros Ricardo Tejeida Padilla Liliana Chanona Hernández Juan Pablo Francisco Posadas Durán Ana Karen Pérez Martínez Ixchel Lina Reyes Hugo Quintana Espinosa |
| author_facet | Jesús Jaime Moreno Escobar Oswaldo Morales Matamoros Ricardo Tejeida Padilla Liliana Chanona Hernández Juan Pablo Francisco Posadas Durán Ana Karen Pérez Martínez Ixchel Lina Reyes Hugo Quintana Espinosa |
| author_sort | Jesús Jaime Moreno Escobar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:17:01Z |
| format | Article |
| id | doaj.art-e48a276e0389485b8f23e61b849303eb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:17:01Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-e48a276e0389485b8f23e61b849303eb2023-11-20T23:44:45ZengMDPI AGSensors1424-82202020-12-012023699110.3390/s20236991Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel ComputingJesús Jaime Moreno Escobar0Oswaldo Morales Matamoros1Ricardo Tejeida Padilla2Liliana Chanona Hernández3Juan Pablo Francisco Posadas Durán4Ana Karen Pérez Martínez5Ixchel Lina Reyes6Hugo Quintana Espinosa7Escuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional, Ciudad de México 07340, MexicoEscuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional, Ciudad de México 07340, MexicoEscuela Superior de Turismo, Instituto Politécnico Nacional, Ciudad de México 07630, MexicoEscuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional, Ciudad de México 07340, MexicoEscuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional, Ciudad de México 07340, MexicoEscuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional, Ciudad de México 07340, MexicoEscuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional, Ciudad de México 07340, MexicoEscuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional, Ciudad de México 07340, MexicoThere 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.https://www.mdpi.com/1424-8220/20/23/6991biomedical sensorsbiomedical systemsparallel computingTGAM1AD8232wireless systems |
| spellingShingle | Jesús Jaime Moreno Escobar Oswaldo Morales Matamoros Ricardo Tejeida Padilla Liliana Chanona Hernández Juan Pablo Francisco Posadas Durán Ana Karen Pérez Martínez Ixchel Lina Reyes Hugo Quintana Espinosa Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing Sensors biomedical sensors biomedical systems parallel computing TGAM1 AD8232 wireless systems |
| title | Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing |
| title_full | Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing |
| title_fullStr | Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing |
| title_full_unstemmed | Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing |
| title_short | Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing |
| title_sort | biomedical signal acquisition using sensors under the paradigm of parallel computing |
| topic | biomedical sensors biomedical systems parallel computing TGAM1 AD8232 wireless systems |
| url | https://www.mdpi.com/1424-8220/20/23/6991 |
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