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research-article

Energy-efficient dynamic sensor time series classification for edge health devices

Published: 01 September 2024 Publication History

Abstract

Background and Objective:

Time series data plays a crucial role in the realm of the Internet of Things Medical (IoMT). Through machine learning (ML) algorithms, online time series classification in IoMT systems enables reliable real-time disease detection. Deploying ML algorithms on edge health devices can reduce latency and safeguard patients’ privacy. However, the limited computational resources of these devices underscore the need for more energy-efficient algorithms. Furthermore, online time series classification inevitably faces the challenges of concept drift (CD) and catastrophic forgetting (CF). To address these challenges, this study proposes an energy-efficient Online Time series classification algorithm that can solve CF and CD for health devices, called OTCD.

Methods:

OTCD first detects the appearance of concept drift and performs prototype updates to mitigate its impact. Afterward, it standardizes the potential space distribution and selectively preserves key training parameters to address CF. This approach reduces the required memory and enhances energy efficiency. To evaluate the performance of the proposed model in real-time health monitoring tasks, we utilize electrocardiogram (ECG) and photoplethysmogram (PPG) data. By adopting various feature extractors, three arrhythmia classification models are compared. To assess the energy efficiency of OTCD, we conduct runtime tests on each dataset. Additionally, the OTCD is compared with state-of-the-art (SOTA) dynamic time series classification models for performance evaluation.

Results:

The OTCD algorithm outperforms existing SOTA time series classification algorithms in IoMT. In particular, OTCD is on average 2.77% to 14.74% more accurate than other models on the MIT-BIH arrhythmia dataset. Additionally, it consumes low memory (1 KB) and performs computations at a rate of 0.004 GFLOPs per second, leading to energy savings and high time efficiency.

Conclusion:

Our proposed algorithm, OTCD, enables efficient real-time classification of medical time series on edge health devices. Experimental results demonstrate its significant competitiveness, offering promising prospects for safe and reliable healthcare.

Highlights

Real-time disease detection via sensor time series reduces patient mortality.
OTCD: Energy-efficient online time series classification for edge health devices.
Addressing concept drift and catastrofic forgetting in IoMT online continue learning.
The prototype specification item ensures high accuracy with minimal memory use.
Experiments show a 20% accuracy increase over baseline methods.

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Published In

cover image Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine  Volume 254, Issue C
Sep 2024
544 pages

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 01 September 2024

Author Tags

  1. Sensor time series classification
  2. Edge health devices
  3. Smart healthcare
  4. Concept drift
  5. Catastrophic forgetting

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