Noninvasive Blood Pressure Classification Based on Photoplethysmography Using K-Nearest Neighbors Algorithm: A Feasibility Study

Tjahjadi, Hendrana; Ramli, Kalamullah

doi:10.3390/info11020093

Cited by 43 publications

(21 citation statements)

References 36 publications

(68 reference statements)

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“…Another work conducted three types of SVM classifiers for predicting the Coronary artery disease [13]. An automated diagnosis system was suggested for the identification of heart valve diseases based on the Support Vector Machines (SVM) classification of heart sounds [14].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.

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Section: Introductionmentioning

confidence: 99%

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

et al. 2020

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“…In ref. [12], Tjahjadi and Ramli propose a method to classify BP by means of the K-nearest neighbors (KNN) algorithm based on PPG. The data are related to 121 subjects, who are divided into normotensive, pre-hypertensive, and hypertensive.…”

Section: Related Workmentioning

confidence: 99%

“…This means we consider the discrimination ability provided by an algorithm at different levels of granularity into which the data set can be divided, as reported in, e.g., refs. [4,11,12]. This analysis allows us to ascertain which is the best machine learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Risk Stratification of Hypertension: A Systematic Comparison of Machine Learning Algorithms

Sannino

Falco

Pietro

2020

JSAN

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One of the most important physiological parameters of the cardiovascular circulatory system is Blood Pressure. Several diseases are related to long-term abnormal blood pressure, i.e., hypertension; therefore, the early detection and assessment of this condition are crucial. The identification of hypertension, and, even more the evaluation of its risk stratification, by using wearable monitoring devices are now more realistic thanks to the advancements in Internet of Things, the improvements of digital sensors that are becoming more and more miniaturized, and the development of new signal processing and machine learning algorithms. In this scenario, a suitable biomedical signal is represented by the PhotoPlethysmoGraphy (PPG) signal. It can be acquired by using a simple, cheap, and wearable device, and can be used to evaluate several aspects of the cardiovascular system, e.g., the detection of abnormal heart rate, respiration rate, blood pressure, oxygen saturation, and so on. In this paper, we take into account the Cuff-Less Blood Pressure Estimation Data Set that contains, among others, PPG signals coming from a set of subjects, as well as the Blood Pressure values of the latter that is the hypertension level. Our aim is to investigate whether or not machine learning methods applied to these PPG signals can provide better results for the non-invasive classification and evaluation of subjects’ hypertension levels. To this aim, we have availed ourselves of a wide set of machine learning algorithms, based on different learning mechanisms, and have compared their results in terms of the effectiveness of the classification obtained.

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“…O estudo de Lima et al (12) dos métodos utilizados por Anggoro e Kurnia (14) Tjahjadi e Raml (15) utilizaram KNN, dado que o mesmo apresenta excelente desempenho em aplicações de classificação, como também é destacado por Tchuente F, Baddour N, Lemaire (13) que o método de classificação KNN, pode fornecer exatidão, sensibilidade, especificidade, precisão, pontuação, bastante relevantes.…”

Section: Introductionunclassified

Aplicação Do Classificador K-Nearest Neighbors (Knn) Na Área Da Saúde: Relação Cintura-Quadril E Pressão Arterial

Passos¹,

Silió²,

Lima³

et al. 2021

CPAQV

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Um dos pontos críticos nos algoritmos de Inteligência Artificial é a eficiência do classificador utilizado nos diferentes cenários de uma rede neural recursiva. O KNN (K-Nearest Neighbors) é um classificador simples capaz de rotular cada elemento de um conjunto de dados a partir da distância destes em relação aos seus vizinhos mais próximos em um conjunto de dados semelhante treinado previamente. Na área da saúde é amplamente conhecido que a Relação Cintura Quadril (RCQ) e a Pressão Arterial (PA) estão fortemente correlacionadas. Objetivo: averiguar o comportamento da acurácia, sensibilidade e precisão do KNN para diferentes valores de k (3, 5 e 7) em relação à identificação de hipertensos em uma conjunto de dados de RCQ. Métodos: A amostra é composta por 499 mulheres, usuários do Programa de Saúde da Família (PSF) na cidade de Poços de Caldas, Minas Gerais.Todas as participantes assinaram o Termo de Consentimento Livre e Esclarecido (TCLE).Todas as participantes assinaram o Termo de Consentimento Livre e Esclarecido (TCLE). Para aferição da PA foi utilizado um esfigmomanômetro de coluna de mercúrio (0-300 mmHg) da marca Biotec ®, a medida das circunferências da cintura e do quadril foram realizadas com uma fita antropométrica, com escala em milímetros da marca Sanny®. O algoritmo desenvolvido na linguagem Python 3.0 e bibliotecas específicas do mesmo foram utilizadas. Foram utilizados 80% dos dados para o treinamento do algoritmo e 20% para a fase de testagem do mesmo. Resultados: os valores da acurácia, sensibilidade e precisão variaram para os diferentes valores de K testados na KNN. Para K=3 foram obtidos os melhores valores para acurácia (0,89) sensibilidade (0,89) e precisão (0,89) para este classificador de mulheres hipertensas a partir do RCQ das mesmas. Conclusão: o método KNN é um eficiente classificador em redes neurais recursivas aplicadas na área da saúde.

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Noninvasive Blood Pressure Classification Based on Photoplethysmography Using K-Nearest Neighbors Algorithm: A Feasibility Study

Cited by 43 publications

References 36 publications

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Non-Invasive Risk Stratification of Hypertension: A Systematic Comparison of Machine Learning Algorithms

Aplicação Do Classificador K-Nearest Neighbors (Knn) Na Área Da Saúde: Relação Cintura-Quadril E Pressão Arterial

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