A Novel Technique for Data Quality Improvement in Human Health PHM

Abstract

Human healthcare data cover different signals related to the functioning of the human body such as blood pressure, blood glucose, heart rate, among others. This kind of data plays an important role on ill patients in the intensive care unit. Unfortunately, the recorded data may include connection & human errors, measurement errors due to the movement of patients, among others. These errors are better known as Artifacts and they should be removed in case the data needs to be used for clinical purposes. Different methods have been proposed for artifact detection; however, the existing methods only analyze one signal at a time or rely on feature engineering. In this study, we present an alternative solution for artifact detection that integrates signals such as Intracranial Pressure (ICP), Electrocardiogram (ECG), and Arterial Blood Pressure (ABP). Time raw domain data, Fourier transform, and the combination of both were studied as the input of neural network models. Four deep learning algorithms were employed: convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and transformer neural network. By performing a cross-validation ensemble using a dataset of 39 patients with noisy signals, the Fourier transform and CNN (FT-CNN) model outperformed the other deep learning models in terms of accuracy and computational time. This study shows that deep learning, Fourier transform, and cross-validation ensemble combined have a great potential in data quality improvement in healthcare.