ACLT-Based QRS Detection and ECG Compression Architecture

Temesghen Tekeste Habte, Hani Saleh, Baker Mohammad, Mohammed Ismail

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations


In this chapter, a QRS detection architecture based on absolute value curve length transform is presented. Ultra-low power and optimized architectures are crucial for IoT devices. Moreover, optimized ECG processing architectures with an adequate level of accuracy is a necessity for IoT medical wearable devices. This chapter presents a real-time QRS detector and ECG compression architecture for energy constrained IoT healthcare wearable devices. The implementation of the proposed architectures requires adders, shifters, and comparators only, and removes the need for any multipliers. QRS detections are accomplished by using adaptive thresholds in the ACLT-transformed ECG-signal. The proposed QRS detector achieved a sensitivity of 99.37% and a predictivity of 99.38% when validated using databases acquired from Physionet. Furthermore, a lossless compression technique was incorporated into the proposed architecture that uses the ECG signal first derivative and variable-bit-length encoding. An average compression ratio of 2.05 was achieved when evaluated using the MIT-BIH database. The proposed QRS architecture was implemented using a 65 nm GF low-power process, it consumed an ultra-low power of 6.5 nW when operated at a supply of 1 V and at a frequency of 250 Hz.

Original languageBritish English
Title of host publicationAnalog Circuits and Signal Processing
Number of pages19
StatePublished - 2019

Publication series

NameAnalog Circuits and Signal Processing
ISSN (Print)1872-082X
ISSN (Electronic)2197-1854


  • Average Compression Ratio
  • Compression Architecture
  • Lossless Compression Techniques
  • PhysioNet
  • Ultra-low Power


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