A comparative study of telephone and face-to-face use of phone screening interview for autistic spectrum disorder symptoms (PSI-ASD) / Estudo comparativo da aplicação por telefone e presencial da entrevista de triagem por telefone de sintomas do espectro autista (PSI-TEA)

ABSTRACT Objective: Describe reliabilities evidence of the Phone Screening Interview (PSI), a telephone screening interview for autism spectrum disorder (ASD) symptoms, capable of investigating mild to moderate ASD symptoms. Moreover, the PSI also works for verbal and non-verbal children and is consistent with the DSM-5 diagnostic criteria. Methods: An interview was performed with sixty-eight parents of children between 2 and 15 years old attended by the Psychiatry Ambulatory of Santa Casa de Misericórdia do Rio de Janeiro through the PSI in person and by telephone. Results: No significant differences in comparison between averages of the total score of the face-to-face and telephone applications were observed. The agreement analysis between the items indicated three items with lower values, leading to the modification of some questions, culminating in a new interview version for further studies. Given the disagreement in the values found, the order of application of the interviews seems to not impact the results, demonstrating strong correlations between both interviews, even with a different order of application. Aiming to facilitate the use of the scale by different examiners, the interobserver reliability was investigated, which did not show significant differences in the means. Conclusion: The study suggests that the telephone interview can be used similarly to the face-to-face interview, by different evaluators, with no impact on its efficiency in detecting ASD symptoms.

RESUMO Objetivo: Descrever evidências de confiabilidade da Phone Screening Interview (PSI), uma entrevista para rastreio telefônico de sintomas do Transtorno do Espectro Autista (TEA) de fácil aplicação, capaz de investigar sintomas de TEA leve a moderado, aplicável a crianças verbais e não verbais e consistente com os critérios diagnósticos do DSM-5. Métodos: Sessenta e oito pais de crianças com idade entre 2 e 15 anos atendidas pelo Ambulatório de Psiquiatria da Santa Casa de Misericórdia do Rio de Janeiro foram entrevistados por meio da PSI, tanto de maneira presencial quanto telefônica. Resultados: As médias da pontuação total da aplicação presencial e telefônica foram comparadas, não sendo obtidas diferenças significativas. A análise de concordância entre os itens apontou três itens com valores muito baixos, levando à modificação de algumas perguntas, culminando em uma nova versão, para estudos posteriores. Diante da discordância de valores encontrada, foi verificado que a ordem de aplicação das entrevistas não impactaria os resultados, demonstrando fortes correlações entre as entrevistas, mesmo com ordem de aplicação diferente. Para viabilizar o uso da escala por diferentes examinadores, investigou-se a confiabilidade interobservadores, que não mostrou diferenças significativas nas médias. Conclusão: O estudo sugere que a entrevista telefônica pode ser utilizada de forma semelhante à presencial, por diferentes avaliadores, sem impacto em sua eficiência na detecção de sintomas de TEA.

Advances in methods and applications of electroencephalogram microstate analysis / 生物医学工程学杂志

Electroencephalogram (EEG) is characterized by high temporal resolution, and various EEG analysis methods have developed rapidly in recent years. The EEG microstate analysis method can be used to study the changes of the brain in the millisecond scale, and can also present the distribution of EEG signals in the topological level, thus reflecting the discontinuous and nonlinear characteristics of the whole brain. After more than 30 years of enrichment and improvement, EEG microstate analysis has penetrated into many research fields related to brain science. In this paper, the basic principles of EEG microstate analysis methods are summarized, and the changes of characteristic parameters of microstates, the relationship between microstates and brain functional networks as well as the main advances in the application of microstate feature extraction and classification in brain diseases and brain cognition are systematically described, hoping to provide some references for researchers in this field.

Electrocardiogram signal classification based on fusion method of residual network and self-attention mechanism / 生物医学工程学杂志

In the diagnosis of cardiovascular diseases, the analysis of electrocardiogram (ECG) signals has always played a crucial role. At present, how to effectively identify abnormal heart beats by algorithms is still a difficult task in the field of ECG signal analysis. Based on this, a classification model that automatically identifies abnormal heartbeats based on deep residual network (ResNet) and self-attention mechanism was proposed. Firstly, this paper designed an 18-layer convolutional neural network (CNN) based on the residual structure, which helped model fully extract the local features. Then, the bi-directional gated recurrent unit (BiGRU) was used to explore the temporal correlation for further obtaining the temporal features. Finally, the self-attention mechanism was built to weight important information and enhance model's ability to extract important features, which helped model achieve higher classification accuracy. In addition, in order to mitigate the interference on classification performance due to data imbalance, the study utilized multiple approaches for data augmentation. The experimental data in this study came from the arrhythmia database constructed by MIT and Beth Israel Hospital (MIT-BIH), and the final results showed that the proposed model achieved an overall accuracy of 98.33% on the original dataset and 99.12% on the optimized dataset, which demonstrated that the proposed model can achieve good performance in ECG signal classification, and possessed potential value for application to portable ECG detection devices.

Automatic sleep staging based on power spectral density and random forest / 生物医学工程学杂志

The method of using deep learning technology to realize automatic sleep staging needs a lot of data support, and its computational complexity is also high. In this paper, an automatic sleep staging method based on power spectral density (PSD) and random forest is proposed. Firstly, the PSDs of six characteristic waves (K complex wave, δ wave, θ wave, α wave, spindle wave, β wave) in electroencephalogram (EEG) signals were extracted as the classification features, and then five sleep states (W, N1, N2, N3, REM) were automatically classified by random forest classifier. The whole night sleep EEG data of healthy subjects in the Sleep-EDF database were used as experimental data. The effects of using different EEG signals (Fpz-Cz single channel, Pz-Oz single channel, Fpz-Cz + Pz-Oz dual channel), different classifiers (random forest, adaptive boost, gradient boost, Gaussian naïve Bayes, decision tree, K-nearest neighbor), and different training and test set divisions (2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, single subject) on the classification effect were compared. The experimental results showed that the effect was the best when the input was Pz-Oz single-channel EEG signal and the random forest classifier was used, no matter how the training set and test set were transformed, the classification accuracy was above 90.79%. The overall classification accuracy, macro average F1 value, and Kappa coefficient could reach 91.94%, 73.2% and 0.845 respectively at the highest, which proved that this method was effective and not susceptible to data volume, and had good stability. Compared with the existing research, our method is more accurate and simpler, and is suitable for automation.

Applications and challenges of wearable electroencephalogram signals in depression recognition and personalized music intervention / 生物医学工程学杂志

Rapid and accurate identification and effective non-drug intervention are the worldwide challenges in the field of depression. Electroencephalogram (EEG) signals contain rich quantitative markers of depression, but whole-brain EEG signals acquisition process is too complicated to be applied on a large-scale population. Based on the wearable frontal lobe EEG monitoring device developed by the authors' laboratory, this study discussed the application of wearable EEG signal in depression recognition and intervention. The technical principle of wearable EEG signals monitoring device and the commonly used wearable EEG devices were introduced. Key technologies for wearable EEG signals-based depression recognition and the existing technical limitations were reviewed and discussed. Finally, a closed-loop brain-computer music interface system for personalized depression intervention was proposed, and the technical challenges were further discussed. This review paper may contribute to the transformation of relevant theories and technologies from basic research to application, and further advance the process of depression screening and personalized intervention.

Research progress on attention level evaluation based on electroencephalogram signals / 生物医学工程学杂志

Attention level evaluation refers to the evaluation of people's attention level through observation or experimental testing, and its research results have great application value in education and teaching, intelligent driving, medical health and other fields. With its objective reliability and security, electroencephalogram signals have become one of the most important technical means to analyze and express attention level. At present, there is little review literature that comprehensively summarize the application of electroencephalogram signals in the field of attention evaluation. To this end, this paper first summarizes the research progress on attention evaluation; then the important methods for electroencephalogram attention evaluation are analyzed, including data preprocessing, feature extraction and selection, attention evaluation methods, etc.; finally, the shortcomings of the current development in the field of electroencephalogram attention evaluation are discussed, and the future development trend is prospected, to provide research references for researchers in related fields.

The study on the characteristics of active force of neck muscles under rapid braking conditions / 生物医学工程学杂志

This paper studies the active force characteristics of the neck muscles under the condition of rapid braking, which can provide theoretical support for reducing the neck injury of pilots when carrier-based aircraft blocks the landing. We carried out static loading and real vehicle braking experiments under rapid braking conditions, collected the active contraction force and electromyography (EMG) signals of neck muscles, and analyzed the response characteristics of neck muscle active force response. The results showed that the head and neck forward tilt time was delayed and the amplitude decreased during neck muscle pre-tightening. The duration of the neck in the extreme position decreased, and the recovery towards the seat direction was faster. The EMG signals of trapezius muscle was higher than sternocleidomastoid muscle. This suggests that pilots can reduce neck injury by pre-tightening the neck muscles during actual braking flight. In addition, we can consider the design of relevant fittings for pre-tightening the neck muscles.

Human muscle fatigue monitoring method and its application for exoskeleton interactive control / 生物医学工程学杂志

Aiming at the human-computer interaction problem during the movement of the rehabilitation exoskeleton robot, this paper proposes an adaptive human-computer interaction control method based on real-time monitoring of human muscle state. Considering the efficiency of patient health monitoring and rehabilitation training, a new fatigue assessment algorithm was proposed. The method fully combined the human neuromuscular model, and used the relationship between the model parameter changes and the muscle state to achieve the classification of muscle fatigue state on the premise of ensuring the accuracy of the fatigue trend. In order to ensure the safety of human-computer interaction, a variable impedance control algorithm with this algorithm as the supervision link was proposed. On the basis of not adding redundant sensors, the evaluation algorithm was used as the perceptual decision-making link of the control system to monitor the muscle state in real time and carry out the robot control of fault-tolerant mechanism decision-making, so as to achieve the purpose of improving wearing comfort and improving the efficiency of rehabilitation training. Experiments show that the proposed human-computer interaction control method is effective and universal, and has broad application prospects.

Síndrome inflamatória multissistêmica em crianças infectadas por coronavírus: uma revisão integrativa / Multisystemic inflamatory syndrome in children infected by coronavirus: an integrative review / Síndrome inflamatorio multisistémico en niños infectados por coronavirus: una revisión integradora

Durante a pandemia de COVID-19, foram observadas manifestações atípicas em pacientes pediátricos em diversas regiões do mundo, e o conjunto desses sintomas caracterizou uma nova patologia denominada Síndrome Inflamatória Multissistêmica em Crianças (MIS-C), ou Síndrome Inflamatória Multissistêmica Pediátrica Temporariamente associada ao COVID-19 (PIMS- TS). O objetivo desta revisão foi analisar as manifestações clínicas e as possíveis complicações relacionadas a tal quadro inflamatório. Foi realizada uma busca por artigos científicos nas bases de dados Embase, PubMed e Web of Science, por meio da combinação dos descritores "MIS-C", "PIMS- TS" e "COVID-19". Após a análise dos artigos encontrados, e considerando critérios de inclusão e exclusão, foram selecionados 15 estudos para compor esta revisão. A maioria dos estudos mencionaram complicações gastrointestinais, cardiovasculares, respiratórias e mucocutâneas. Ademais, foram encontrados marcadores que indicavam estado inflamatório generalizado e coagulopatia. Assim, concluiu-se que MIS-C provavelmente é uma síndrome manifestada após a infecção por SARS-CoV-2, podendo ocasionar quadros mais graves, mas com baixas taxas de mortalidade.

During the COVID-19 pandemic, atypical manifestations were observed in pediatric patients in different regions of the world, and the set of these symptoms characterized a new pathology called Multisystemic Inflammatory Syndrome in Children (MIS-C), or Pediatric Multisystemic Inflammatory Syndrome Temporarily associated with COVID-19 (PIMS-TS). The purpose of this review was to analyze the clinical manifestations and possible complications related to such an inflammatory condition. A search for scientific articles was carried out in the databases Embase, PubMed and Web of Science, by combining the descriptors "MIS-C", "PIMS-TS" and "COVID-19". After analyzing the articles found, and considering inclusion and exclusion criteria, 15 studies were selected to compose this review. Most studies mentioned gastrointestinal, cardiovascular, respiratory and mucocutaneous complications. In addition, markers were found that indicated generalized inflammatory status and coagulopathy. Thus, it was concluded that MIS-C is probably a syndrome manifested after infection by SARS-CoV-2, which can cause more severe conditions, but with low mortality rates.

Durante la pandemia de COVID-19 se observaron manifestaciones atípicas en pacientes pediátricos de diferentes regiones del mundo, y el conjunto de estos síntomas caracterizó una nueva patología denominada Síndrome Inflamatorio Multisistémico en Niños (SMI-C), o Síndrome Inflamatorio Multisistémico Pediátrico Asociado Temporalmente a COVID-19 (SIPM-TS). El propósito de esta revisión fue analizar las manifestaciones clínicas y las posibles complicaciones relacionadas con dicha condición inflamatoria. Se realizó una búsqueda de artículos científicos en las bases de datos Embase, PubMed y Web of Science, combinando los descriptores "MIS-C", "PIMS- TS" y "COVID-19". Tras analizar los artículos encontrados, y teniendo en cuenta los criterios de inclusión y exclusión, se seleccionaron 15 estudios para componer esta revisión. La mayoría de los estudios mencionaron complicaciones gastrointestinales, cardiovasculares, respiratorias y mucocutáneas. Además, se encontraron marcadores que indicaban un estado inflamatorio generalizado y coagulopatía. Así pues, se concluyó que el SMI-C es probablemente un síndrome que se manifiesta tras la infección por el SARS-CoV-2, que puede causar cuadros más graves, pero con bajas tasas de mortalidad.

Aplicación Android para la adquisición inalámbrica y visualización de señales biomédicas / Android application for wireless acquisition and visualization of biomedical signals / Aplicativo Android para aquisição sem fio e visualização de sinais biomédicos

RESUMEN Introducción: Los avances tecnológicos experimentados por los teléfonos y tabletas con sistema operativo Android han permitido el desarrollo de innumerables aplicaciones en el área de la medicina. Hasta nuestro conocimiento, en nuestro sistema de salud, no se reporta el uso de un dispositivo portátil que permita al especialista, monitorear a distancia, de forma inalámbrica las señales biomédicas asociadas a un paciente. Objetivo: Desarrollar una aplicación Android (herramienta) que permita adaptarse a múltiples sistemas de monitorización inalámbrica con el fin de capturar, visualizar y almacenar señales biomédicas. Método: Se muestra la arquitectura general del sistema de comunicación inalámbrico que integra a la herramienta y se propone el diseño software de la herramienta y el diagrama de interacción de las cinco actividades que la componen: "Menú", "Pacientes", "Configuración", "Escáner", "Graficar". Resultados: Se mostraron las diferentes pantallas y funcionalidades de la aplicación, para dos dispositivos médicos cubanos (y modos): Sistema de Medición Biomédica para la Exploración Vestibular (Recepción) y Sistema de Monitoreo Electrocardiográfico Inalámbrico para dispositivos Android (opera en modo Transmisión/Recepción). Conclusiones: La aplicación proporciona una interfaz sencilla e intuitiva, lo que facilita la interacción con el usuario. Su evaluación cualitativa mediante pruebas pilotos mostró excelentes resultados en ambos casos.

ABSTRACT Introduction: Technological advances experienced by phones and tablets with Android operating system have enabled the development of countless applications in the field of medicine. As far as we know, there is not reported in our health system the use of a portable device that allows the specialist to wirelessly monitor remotely the biomedical signals associated with the patient. Objective: Development of an Android application (as a tool) that can be adapted to multiple wireless monitoring systems in order to capture, visualize and store biomedical signals. Method: The general architecture of the wireless communication system that integrates the tool is shown and the software design of the tool and the interaction diagram of the five activities that compose it are proposed: "Menu", "Patients", "Configuration", "Scanner", "Graph". Results: Different screens and functionalities of the application were shown, compatibles for two Cuban medical devices (and modes): Biomedical Measurement System for Vestibular Exploration (Reception) and the Wireless Electrocardiographic Monitoring System for Android devices (operates in Transmission/Reception mode). Conclusions: The application provides a simple and intuitive interface, which facilitates interaction with the user. Its qualitative evaluation through rapid tests showed excellent results in both cases.

RESUMO Introdução: Os avanços tecnológicos vivenciados pelos telefones e tablets com sistema operacional Android permitiram o desenvolvimento de inúmeras aplicações na área da Medicina. Até onde sabemos, em nosso sistema de saúde, não foi relatado o uso de um dispositivo portátil que permita ao especialista monitorar remotamente, sem fio, os sinais biomédicos associados a um paciente. Objetivo: Desenvolver um aplicativo Android (ferramenta) que possa ser adaptado a vários sistemas de monitoramento sem fio para capturar, exibir e armazenar sinais biomédicos. Método: Apresenta-se a arquitetura geral do sistema de comunicação sem fio que integra a ferramenta e propõe-se o desenho do software da ferramenta e o diagrama de interação das cinco atividades que a compõem: "Menu", "Pacientes", "Configuração", "Scanner", "Gráfico". Resultados: Foram mostradas as diferentes telas e funcionalidades do aplicativo para dois dispositivos médicos cubanos (e modos): Sistema de Medição Biomédica para Exame Vestibular (Recepção) e Sistema de Monitoramento Eletrocardiográfico Sem Fio para dispositivos Android (opera no modo Transmissão/Transmissão). Conclusões: O aplicativo oferece uma interface simples e intuitiva, o que facilita a interação com o usuário. Sua avaliação qualitativa por meio de testes pilotos apresentou excelentes resultados em ambos os casos.

Estrategia para la toma de decisiones en el reconocimiento automático de estados de sedación anestésica / Decision-making strategy for automatic recognition of sedation states / Estratégia de tomada de decisão para reconhecimento automático de estados de sedação

RESUMEN Introducción: La Anestesiología es la especialidad médica dedicada a la atención específica de los pacientes durante procedimientos quirúrgicos y en cuidados intensivos. Esta especialidad basada en los avances científicos y tecnológicos, ha incorporado el uso del monitoreo electroencefalográfico, facilitando el control continuo de estados de sedación anestésica durante las cirugías, con una adecuada concentración de fármacos. Objetivo: Proponer una estrategia de clasificación para el reconocimiento automático de tres estados de sedación anestésica en señales electroencefalográficas. Método: Se utilizaron con consentimiento informado escrito los registros electroencefalográficos de 27 pacientes sometidos a cirugía abdominal, excluyendo aquellos con antecedentes de epilepsia, enfermedades cerebrovasculares y otras afecciones neurológicas. Se aplicaron en total 12 fármacos anestésicos y dos relajantes musculares con montaje de 19 electrodos según el Sistema Internacional 10-20. Se eliminaron artefactos en los registros y se aplicaron técnicas de Inteligencia artificial para realizar el reconocimiento automático de los estados de sedación. Resultados: Se propuso una estrategia basada en el uso de máquinas de soporte vectorial con algoritmo multiclase Uno-Contra-Resto y la métrica Similitud Coseno, para realizar el reconocimiento automático de tres estados de sedación: profundo, moderado y ligero, en señales registradas por el canal frontal F4 y los occipitales O1 y O2. Se realizó una comparación de la propuesta con otros métodos de clasificación. Conclusiones: Se computa una exactitud balanceada del 92,67 % en el reconocimiento de los tres estados de sedación en las señales registradas por el canal electroencefalográfico F4, lo cual favorece el desarrollo de la monitorización anestésica.

ABSTRACT Introduction: Anesthesiology is the medical specialty concerned with the specific care of patients during surgical and intensive care procedures. This specialty, based on scientific and technological advances, has incorporated the use of electroencephalographic monitoring, facilitating the continuous control in the use of anesthesia for patient´s sedation states during surgeries, with an adequate concentration of drugs. Objective: Proposal for a classification strategy for automatic recognition of three sedation states in electroencephalographic signals. Methods: We used, with written informed consent, the electroencephalographic records of 27 patients undergoing abdominal surgery, excluding those with a history of epilepsy, cerebrovascular disease and other neurological conditions. A total of 12 drugs to produce anesthesia and two muscle relaxants with 19 electrodes, mounted according to the International System 10 -20, were applied. Artifacts in the records were eliminated and artificial intelligence techniques were applied to perform automatic recognition of sedation states. Results: A strategy based on the use of support vector machines with a multiclass algorithm One-against-Rest and the Cosine Similarity metric was proposed to perform the automatic recognition of three sedation states: deep, moderate and light, in signals recorded by the frontal channel F4 and the occipital channels O1 and O2. A comparison was carried out between the proposal showed and other classification methods. Conclusions: A balanced accuracy of 92.67% is computed about the recognition of the three states of sedation in the signals recorded by the electroencephalographic channel F4, which helps in a better anesthetic monitoring process.

RESUMO Introdução: A Anestesiologia é a especialidade médica dedicada ao atendimento específico de pacientes durante procedimentos cirúrgicos e em terapia intensiva. Essa especialidade, baseada nos avanços científicos e tecnológicos, incorporou o uso da monitorização eletroencefalográfica, facilitando o controle contínuo dos estados de sedação anestésica durante as cirurgias, com concentração adequada de fármacos. Objetivo: Propor uma estratégia de classificação para o reconhecimento automático de três estados de sedação anestésica em sinais eletroencefalográficos. Método: Foram utilizados registros eletroencefalográficos de 27 pacientes submetidos à cirurgia abdominal com consentimento informado por escrito, excluindo aqueles com histórico de epilepsia, doenças cerebrovasculares e outras condições neurológicas. Um total de 12 drogas anestésicas e dois relaxantes musculares foram aplicados com um conjunto de 19 eletrodos de acordo com o Sistema Internacional 10-20. Artefatos nos prontuários foram removidos e técnicas de inteligência artificial foram aplicadas para realizar o reconhecimento automático dos estados de sedação. Resultados: Foi proposta uma estratégia baseada no uso de máquinas de vetores de suporte com algoritmo One-Against-Rest multiclasse e a métrica Cosine Similarity para realizar o reconhecimento automático de três estados de sedação: profundo, moderado e leve, em sinais registrados pelo canal frontal F4 e os canais occipitais O1 e O2. Foi feita uma comparação da proposta com outros métodos de classificação. Conclusões: Uma acurácia equilibrada de 92,67% é computada no reconhecimento dos três estados de sedação nos sinais registrados pelo canal eletroencefalográfico F4, o que favorece o desenvolvimento da monitorização anestésica.

Silent speech of vowels in persons of different cognitive styles / Habla silenciosa de las vocales en personas de diferente estilo cognitivo

Abstract Introduction: This research measures the differences in silent speech of the vowels / a / - / u / in Spanish, in students with different cognitive styles in the Field Dependence - Independence (FDI) dimension. Method: Fifty-one (51) adults participated in the study. Electroencephalographic (EEG) signals were taken from 14 electrodes placed on the scalp in the language region located in the left hemisphere. Previously, the embedded figures test (EFT) was applied in order to classify them into dependent, intermediate and field independent persons. To analyse the EEG data, the signals were decomposed into intrinsic mode functions (IMF) and a mixed repeated measures analysis was performed. Results: It was found that the Power Spectral Density (PSD) in the vowels is independent of the cognitive style and its magnitude depends on the position of the electrodes. Conclusions: The results suggest that there are no significant differences in PSDs in the silent speech of vowels /a/-/u/ in persons of different cognitive styles. Significant differences were found in the PSDs according to the position of the 14 electrodes used. In our configuration, the silent speech of vowels can be studied using electrodes placed in premotor, motor and Wernicke areas.

Resumen Introducción: La investigación mide las diferencias en el habla silenciosa de las vocales /a/-/u/ en español, en estudiantes de diferente estilo cognitivo en la dimensión Dependencia - Independencia de campo (DIC). Método: En el estudio participaron 51 adultos. Se tomaron señales electroencefalográficas (EEG), a partir de 14 electrodos dispuestos sobre el cuero cabelludo de la región del lenguaje ubicada en el hemisferio izquierdo. Previamente les fue aplicado el test de figuras enmascaradas EFT con el fin de clasificarlos en personas dependientes, intermedios e independientes de campo. Para analizar los datos del EEG se descompusieron las señales en funciones de modo intrínseco (IMF) y se realizó un análisis mixto de medidas repetidas. Resultados: Se halló que la densidad espectral de potencia (PSD) en las vocales es independiente del estilo cognitivo y su magnitud depende de la posición de los electrodos. Conclusión: Los resultados sugieren que no existen diferencias significativas en los PSD en el habla silenciosa de las vocales /a/-/u/ en las personas de diferente estilo cognitivo. Se hallaron diferencias significativas en los PSD de acuerdo con la posición de los 14 electrodos utilizados. En nuestra configuración, el habla silenciosa de las vocales puede ser estudiada mediante electrodos situados en las áreas premotora, motora y de Wernicke.

ELECTROCARDIOGRAPHY DEFORMATIONS

An electrocardiogram records the electrical signals in the heart. It's a common and painless test used to quickly detect heart problems and monitor the heart's health. An electrocardiogram — also called ECG or EKG — is often done in a health care provider's office, a clinic or a hospital room. ECG machines are standard equipment in operating rooms and ambulances. Some personal devices, such as smartwatches, offer ECG monitoring. Ask your health care provider if this is an option for you.

Level of Birth Preparedness and Complication Readiness among Pregnant Women Residing In Urban Slums of Shivamogga City, India

Background: Safe motherhood is about informing and educating woman about danger signs in pregnancy, how to identify and seek advice from health personnel and prepare for safe confinement. In public health system, in India it is the responsibility of ASHA to motivate the pregnant woman in her area for safe institutional delivery. BPACR is a tool which assesses, how well the pregnant women are prepared for the challenges in pregnancy. Aim& Objective: To ascertain the level of awareness of Birth Preparedness and Complication Readiness (BPACR) among antenatal mothers residing in urban slums . Methodology: A community based cross-sectional study was conducted among pregnant women residing in urban slums of Shivamogga, Karnataka. Data was collected using pre-designed questionnaire, 揗onitoring BP/CR?tools and indicators for maternal and new born health� of the 揓HPIEGO�. Data was analysed and results were tabulated. Results: In this study, only 42% of pregnant women knew about the term 態irth preparedness� while the rest 58% pregnant women did not know it. Education status and complication experienced during present or previous pregnancy were found to have significant association with BPACR. Identification of blood donor and skilled birth provider were less among study group. Conclusions: Awareness of danger signs and complication readiness was found to be good in our study.

Single-channel electroencephalogram signal used for sleep state recognition based on one-dimensional width kernel convolutional neural networks and long-short-term memory networks / 生物医学工程学杂志

Aiming at the problem that the unbalanced distribution of data in sleep electroencephalogram(EEG) signals and poor comfort in the process of polysomnography information collection will reduce the model's classification ability, this paper proposed a sleep state recognition method using single-channel EEG signals (WKCNN-LSTM) based on one-dimensional width kernel convolutional neural networks(WKCNN) and long-short-term memory networks (LSTM). Firstly, the wavelet denoising and synthetic minority over-sampling technique-Tomek link (SMOTE-Tomek) algorithm were used to preprocess the original sleep EEG signals. Secondly, one-dimensional sleep EEG signals were used as the input of the model, and WKCNN was used to extract frequency-domain features and suppress high-frequency noise. Then, the LSTM layer was used to learn the time-domain features. Finally, normalized exponential function was used on the full connection layer to realize sleep state. The experimental results showed that the classification accuracy of the one-dimensional WKCNN-LSTM model was 91.80% in this paper, which was better than that of similar studies in recent years, and the model had good generalization ability. This study improved classification accuracy of single-channel sleep EEG signals that can be easily utilized in portable sleep monitoring devices.

Research progress of epileptic seizure predictions based on electroencephalogram signals / 生物医学工程学杂志

As a common disease in nervous system, epilepsy is possessed of characteristics of high incidence, suddenness and recurrent seizures. Timely prediction with corresponding rescues and treatments can be regarded as effective countermeasure to epilepsy emergencies, while most accidental injuries can thus be avoided. Currently, how to use electroencephalogram (EEG) signals to predict seizure is becoming a highlight topic in epilepsy researches. In spite of significant progress that made, more efforts are still to be made before clinical applications. This paper reviews past epilepsy studies, including research records and critical technologies. Contributions of machine learning (ML) and deep learning (DL) on seizure predictions have been emphasized. Since feature selection and model generalization limit prediction ratings of conventional ML measures, DL based seizure predictions predominate future epilepsy studies. Consequently, more exploration may be vitally important for promoting clinical applications of epileptic seizure prediction.

A fetal electrocardiogram signal extraction method based on long short term memory network optimized by genetic algorithm / 生物医学工程学杂志

Fetal electrocardiogram signal extraction is of great significance for perinatal fetal monitoring. In order to improve the prediction accuracy of fetal electrocardiogram signal, this paper proposes a fetal electrocardiogram signal extraction method (GA-LSTM) based on genetic algorithm (GA) optimization with long and short term memory (LSTM) network. Firstly, according to the characteristics of the mixed electrocardiogram signal of the maternal abdominal wall, the global search ability of the GA is used to optimize the number of hidden layer neurons, learning rate and training times of the LSTM network, and the optimal combination of parameters is calculated to make the network topology and the mother body match the characteristics of the mixed signals of the abdominal wall. Then, the LSTM network model is constructed using the optimal network parameters obtained by the GA, and the nonlinear transformation of the maternal chest electrocardiogram signals to the abdominal wall is estimated by the GA-LSTM network. Finally, using the non-linear transformation obtained from the maternal chest electrocardiogram signal and the GA-LSTM network model, the maternal electrocardiogram signal contained in the abdominal wall signal is estimated, and the estimated maternal electrocardiogram signal is subtracted from the mixed abdominal wall signal to obtain a pure fetal electrocardiogram signal. This article uses clinical electrocardiogram signals from two databases for experimental analysis. The final results show that compared with the traditional normalized minimum mean square error (NLMS), genetic algorithm-support vector machine method (GA-SVM) and LSTM network methods, the method proposed in this paper can extract a clearer fetal electrocardiogram signal, and its accuracy, sensitivity, accuracy and overall probability have been better improved. Therefore, the method could extract relatively pure fetal electrocardiogram signals, which has certain application value for perinatal fetal health monitoring.

Prediction of drug-induced cell viability by SAE-XGBoost algorithm based on LINCS-L1000 perturbation signal / 生物工程学报

Different cell lines have different perturbation signals in response to specific compounds, and it is important to predict cell viability based on these perturbation signals and to uncover the drug sensitivity hidden underneath the phenotype. We developed an SAE-XGBoost cell viability prediction algorithm based on the LINCS-L1000 perturbation signal. By matching and screening three major dataset, LINCS-L1000, CTRP and Achilles, a stacked autoencoder deep neural network was used to extract the gene information. These information were combined with the RW-XGBoost algorithm to predict the cell viability under drug induction, and then to complete drug sensitivity inference on the NCI60 and CCLE datasets. The model achieved good results compared to other methods with a Pearson correlation coefficient of 0.85. It was further validated on an independent dataset, corresponding to a Pearson correlation coefficient of 0.68. The results indicate that the proposed method can help discover novel and effective anti-cancer drugs for precision medicine.

Audio-visual Emotion Regulation System Based on TGAM Module / 中国医疗器械杂志

OBJECTIVE@#Based on the TGAM PCB module, a system of emotion control using audio-visual feedback is designed.@*METHODS@#TGAM collects EEG information through the electrode in contact with the forehead skin. The system analyzes the user's emotion through the STM32F103ZET6 of the main control chip, and finally controls the control end of the system to regulate the user's emotion.@*RESULTS@#It can be seen from the test results that the system can precisely recognize the user's emotions, and at the same time effectively adjust the user's emotions from both audio-visual aspects.@*CONCLUSIONS@#The system has high recognition accuracy and good adjustment effect.

Development of Respiratory Signal Extraction System Based on Photoplethysmography / 中国医疗器械杂志

Oxygen saturation and respiratory signals are important physiological signals of human body, respiratory monitoring plays an important role in clinical and daily life. A system was established to extract respiratory signals from photoplethysmography in this study. Including the collection of pulse wave signal, the extraction of respiratory signal, and the calculation of respiratory rate and pulse rate transmitted from the slave computer to the host computer in real time.