4 research outputs found
Un enfoque de aprendizaje profundo para estimar la frecuencia respiratoria del fotopletismograma
This article describes the methodology used to train and test a Deep Neural Network (DNN) with Photoplethysmography (PPG) data performing a regression task to estimate the Respiratory Rate (RR). The DNN architecture is based on a model used to infer the heart rate (HR) from noisy PPG signals, which is optimized to the RR problem using genetic optimization. Two open-access datasets were used in the tests, the BIDMC and the CapnoBase. With the CapnoBase dataset, the DNN achieved a median error of 1.16 breaths/min, which is comparable with analytical methods in the literature, in which the best error found is 1.1 breaths/min (excluding the 8 % noisiest data). The BIDMC dataset seems to be more challenging, as the minimum median error of the literature’s methods is 2.3 breaths/min (excluding 6 % of the noisiest data), and the DNN based approach achieved a median error of 1.52 breaths/min with the whole dataset.Este trabajo presenta una metodologÃa para entrenar y probar una red neuronal profunda (Deep Neural Network – DNN) con datos de fotopletismografÃas (Photoplethysmography – PPG), con la finalidad de llevar a cabo una tarea de regresión para estimar la frecuencia respiratoria (Respiratory Rate – RR). La arquitectura de la DNN está basada en un modelo utilizado para inferir la frecuencia cardÃaca (FC) a partir de señales PPG ruidosas. Dicho modelo se ha optimizado a través de algoritmos genéticos. En las pruebas realizadas se usaron BIDMC y CapnoBase, dos conjuntos de datos de acceso abierto. Con CapnoBase, la DNN logró un error de la mediana de 1,16 respiraciones/min, que es comparable con los métodos analÃticos reportados en la literatura, donde el mejor error es 1,1 respiraciones/min (excluyendo el 8 % de datos más ruidosos). Por otro lado, el conjunto de datos BIDMC aparenta ser más desafiante, ya que el error mÃnimo de la mediana de los métodos reportados en la literatura es de 2,3 respiraciones/min (excluyendo el 6 % de datos más ruidosos). Para este conjunto de datos la DNN logra un error de mediana de 1,52 respiraciones/min
Un enfoque de aprendizaje profundo para estimar la frecuencia respiratoria del fotopletismograma
Este trabajo presenta una metodologÃa para entrenar
y probar una red neuronal profunda (Deep Neural
Network – DNN) con datos de fotopletismografÃas
(Photoplethysmography – PPG), con la finalidad de
llevar a cabo una tarea de regresión para estimar la
frecuencia respiratoria (Respiratory Rate – RR). La
arquitectura de la DNN está basada en un modelo
utilizado para inferir la frecuencia cardÃaca (FC) a
partir de señales PPG ruidosas. Dicho modelo se ha
optimizado a través de algoritmos genéticos. En las
pruebas realizadas se usaron BIDMC y CapnoBase,
dos conjuntos de datos de acceso abierto. Con CapnoBase,
la DNN logró un error de la mediana de 1,16
respiraciones/min, que es comparable con los métodos
analÃticos reportados en la literatura, donde el
mejor error es 1,1 respiraciones/min (excluyendo el
8 % de datos más ruidosos). Por otro lado, el conjunto
de datos BIDMC aparenta ser más desafiante,
ya que el error mÃnimo de la mediana de los métodos
reportados en la literatura es de 2,3 respiraciones/min
(excluyendo el 6 % de datos más ruidosos). Para este
conjunto de datos la DNN logra un error de mediana
de 1,52 respiraciones/min.//This article describes the methodology used to train
and test a Deep Neural Network (DNN) with Photoplethysmography
(PPG) data performing a regression
task to estimate the Respiratory Rate (RR). The
DNN architecture is based on a model used to infer
the heart rate (HR) from noisy PPG signals, which
is optimized to the RR problem using genetic optimization.
Two open-access datasets were used in
the tests, the BIDMC and the CapnoBase. With the
CapnoBase dataset, the DNN achieved a median error
of 1.16 breaths/min, which is comparable with
analytical methods in the literature, in which the best
error found is 1.1 breaths/min (excluding the 8 %
noisiest data). The BIDMC dataset seems to be more
challenging, as the minimum median error of the literature’s
methods is 2.3 breaths/min (excluding 6 %
of the noisiest data), and the DNN based approach
achieved a median error of 1.52 breaths/min with the
whole dataset
Triagem Automática em Centros de Saúde: Avaliação de Técnicas de Aprendizado de Máquina Baseadas no Protocolo de Manchester para um Dispositivo Multimodal
<p>A classificação de risco é fundamental para priorizar o atendimento em setores de emergência em centros de saúde, assegurando que pacientes com condições mais graves recebam tratamento imediato. O Sistema de Triagem de Manchester (STM) foi desenvolvido para avaliar a gravidade dos pacientes que chegam em centros de saúde. No entanto, a presença de vieses, decorrentes de fatores como falta de treinamento adequado e influência de preferências pessoais dos profissionais de saúde, pode comprometer o tempo de espera do atendimento. Neste estudo foi realizada uma avaliação de técnicas de Aprendizado de Máquina (em inglês, Machine Learning – ML) utilizando as informações relevantes de pacientes de uma base de dados de STM. O objetivo deste estudo é aplicar essas técnicas em um sistema de triagem automática, utilizando um dispositivo multimodal, para mitigar os vieses na classificação de risco do STM. Os resultados para a tarefa de classificação de risco foram promissores em relação à acurácia (aproximadamente 98,00%) e ao F1-score (aproximadamente 1,00). Esses resultados sugerem que o sistema de triagem automática proposto pode ser uma alternativa eficaz para melhorar a classificação de risco no STM, podendo reduzir a influência de vieses e otimizar o atendimento em centros de saúde. </p>
Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms
COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, which are released when someone sneezes, coughs, or talks. The gold-standard exam to detect the virus is Real-Time Polymerase Chain Reaction (RT-PCR); however, this is an expensive test and may require up to 3 days after infection for a reliable result, and if there is high demand, the labs could be overwhelmed, which can cause significant delays in providing results. Biomedical data (oxygen saturation level—SpO2, body temperature, heart rate, and cough) are acquired from individuals and are used to help infer infection by COVID-19, using machine learning algorithms. The goal of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal piece of equipment that can collect biomedical data, such as oxygen saturation level, body temperature, heart rate, and cough sound, and helps infer the diagnosis of COVID-19 through machine learning algorithms. The IPMA has the capacity to store the biomedical data for continuous studies and can be used to infer other respiratory diseases. Quadratic kernel-free non-linear Support Vector Machine (QSVM) and Decision Tree (DT) were applied on three datasets with data of cough, speech, body temperature, heart rate, and SpO2, obtaining an Accuracy rate (ACC) and Area Under the Curve (AUC) of approximately up to 88.0% and 0.85, respectively, as well as an ACC up to 99% and AUC = 0.94, respectively, for COVID-19 infection inference. When applied to the data acquired with the IMPA, these algorithms achieved 100% accuracy. Regarding the easiness of using the equipment, 36 volunteers reported that the IPMA has a high usability, according to results from two metrics used for evaluation: System Usability Scale (SUS) and Post Study System Usability Questionnaire (PSSUQ), with scores of 85.5 and 1.41, respectively. In light of the worldwide needs for smart equipment to help fight the COVID-19 pandemic, this new equipment may help with the screening of COVID-19 through data collected from biomedical signals and cough sounds, as well as the use of machine learning algorithms