A noble double dictionary based ECG Compression Technique for IoTH

Jia Qian, Prayag Tiwari, Sarada Prasad Gochhayat, Hari Mohan Pandey

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Internet-of-things (IoT) health-care system monitors a patients’ condition and takes preventive measures in case of an emergency. Electrocardiogram (ECG) that measures the electrical activity of the heart is one of the important health indicators. Thanks to the wearable technology, nowadays, we can even measure the ECG using smart portable devices and send via a wireless channel. However, this wireless transmission has to minimize both energy and memory consumption. In this paper, we propose CULT -an ECG compression technique using unsupervised dictionary learning. Our method achieves a high compression rate due to the essence of dictionary learning and is immune to the noise by integrating Discrete Cosine Transformation. Moreover, it continuously expands the dictionary when the unseen pattern occurs and refines the dictionary when new input arrives, by imposing the double dictionary scheme. We show that our method has a better performance by comparing it with the other existing approaches.
Original languageEnglish
JournalIEEE Internet of Things Journal
Issue number10
Pages (from-to)10160 - 10170
Publication statusPublished - 2020


  • ECG
  • Compression
  • Vector Quantization
  • Dictionary Learning
  • IoT healthcare


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