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

doi:10.3390/s23031625

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

School of Medicine

Research output: Contribution to journal › Article › peer-review

Abstract

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 language	English (US)
Article number	1625
Journal	Sensors
Volume	23
Issue number	3
DOIs	https://doi.org/10.3390/s23031625
State	Published - Feb 2023

Keywords

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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10.3390/s23031625

Cite this

@article{4a31925ff78841bcafdaea641b21054d,

title = "Algorithm for Mobile Platform-Based Real-Time QRS Detection",

abstract = "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.",

keywords = "QRS complex, algorithm, electrocardiogram, heart rate, mobile platform, wearables",

author = "Luca Neri and Oberdier, {Matt T.} and Antonio Augello and Masahito Suzuki and Ethan Tumarkin and Sujai Jaipalli and Geminiani, {Gian Angelo} and Halperin, {Henry R.} and Claudio Borghi",

note = "Publisher Copyright: {\textcopyright} 2023 by the authors.",

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doi = "10.3390/s23031625",

language = "English (US)",

volume = "23",

journal = "Sensors",

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AU - Neri, Luca

AU - Oberdier, Matt T.

AU - Augello, Antonio

AU - Suzuki, Masahito

AU - Tumarkin, Ethan

AU - Jaipalli, Sujai

AU - Geminiani, Gian Angelo

AU - Halperin, Henry R.

AU - Borghi, Claudio

PY - 2023/2

Y1 - 2023/2

N2 - 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.

AB - 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.

KW - QRS complex

KW - algorithm

KW - electrocardiogram

KW - heart rate

KW - mobile platform

KW - wearables

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JO - Sensors

JF - Sensors

IS - 3

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ER -

Algorithm for Mobile Platform-Based Real-Time QRS Detection

Abstract

Keywords

ASJC Scopus subject areas

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