An Enhanced Classification Framework for Limited IoHT Time Series Data Using Ensemble Deep Learning and Image Encoding

Recent studies have illuminated the potential of harnessing the power of Deep Learning (DL) and the Internet of Health Things (IoHT) to detect a variety of disorders, particularly among patients in the middle to later stages of the disease. The utilization of time series data has proven to be a valuable asset in this endeavour. However, the development of effective DL architectures for time series classification with limited data remains a critical gap in the field. Although some studies have explored this area, it is still an understudied and undervalued topic. Thus, there is a crucial need to address this gap and provide insights into designing effective architectures for time series classification with limited data, specifically in the context of healthcare-related time series data for rare diseases. The goal of this study is to investigate the possibility of making accurate predictions with a smaller time series dataset by using an Ensemble DL architecture. This framework is composed of a deep CNN model and transfer learning approaches like ResNet and MobileNet. The ensemble model proposed in this study was supplied with 3D images that were generated from time series data by using Recurrence Plot (RP), Gramian Angular Field (GAF), and Fuzzy Recurrence Plot (FRP) as the transformation techniques. The proposed method has shown promising classification accuracy, even when applied to a small dataset, and surpassed the performance of other state-of-the-art methods when tested on the ECG5000 dataset.Clinical relevance— The proposed deep learning architecture is capable of effectively handling limited clinical time series datasets, enabling the construction of robust models and accurate predictions.