Algorithm for Mobile Platform-Based Real-Time QRS Detection

Luca Neri, Matt T. Oberdier, Antonio Augello, Masahito Suzuki, Ethan Tumarkin, Sujai Jaipalli, Gian Angelo Geminiani, Henry R. Halperin, Claudio Borghi

Research output: Contribution to journalArticlepeer-review


Recent advancements in smart, wearable technologies have allowed the detection of various medical conditions. In particular, continuous collection and real-time analysis of electrocardiogram data have enabled the early identification of pathologic cardiac rhythms. Various algorithms to assess cardiac rhythms have been developed, but these utilize excessive computational power. Therefore, adoption to mobile platforms requires more computationally efficient algorithms that do not sacrifice correctness. This study presents a modified QRS detection algorithm, the AccYouRate Modified Pan–Tompkins (AMPT), which is a simplified version of the well-established Pan–Tompkins algorithm. Using archived ECG data from a variety of publicly available datasets, relative to the Pan–Tompkins, the AMPT algorithm demonstrated improved computational efficiency by 5–20×, while also universally enhancing correctness, both of which favor translation to a mobile platform for continuous, real-time QRS detection.

Original languageEnglish (US)
Article number1625
Issue number3
StatePublished - Feb 2023


  • QRS complex
  • algorithm
  • electrocardiogram
  • heart rate
  • mobile platform
  • wearables

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering
  • Biochemistry


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