PMID- 38083542
OWN - NLM
STAT- MEDLINE
DCOM- 20231216
LR  - 20231222
IS  - 2694-0604 (Electronic)
IS  - 2375-7477 (Linking)
VI  - 2023
DP  - 2023 Jul
TI  - An Enhanced Classification Framework for Limited IoHT Time Series Data Using 
      Ensemble Deep Learning and Image Encoding.
PG  - 1-4
LID - 10.1109/EMBC40787.2023.10341070 [doi]
AB  - 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.
FAU - Indrasiri, Pubudu L
AU  - Indrasiri PL
FAU - Kashyap, Bipasha
AU  - Kashyap B
FAU - Pathirana, Pubudu N
AU  - Pathirana PN
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Annu Int Conf IEEE Eng Med Biol Soc
JT  - Annual International Conference of the IEEE Engineering in Medicine and Biology 
      Society. IEEE Engineering in Medicine and Biology Society. Annual International 
      Conference
JID - 101763872
SB  - IM
MH  - Humans
MH  - Time Factors
MH  - *Deep Learning
MH  - Rare Diseases
EDAT- 2023/12/12 06:42
MHDA- 2023/12/17 09:42
CRDT- 2023/12/12 01:08
PHST- 2023/12/17 09:42 [medline]
PHST- 2023/12/12 06:42 [pubmed]
PHST- 2023/12/12 01:08 [entrez]
AID - 10.1109/EMBC40787.2023.10341070 [doi]
PST - ppublish
SO  - Annu Int Conf IEEE Eng Med Biol Soc. 2023 Jul;2023:1-4. doi: 
      10.1109/EMBC40787.2023.10341070.