A Robust Martingale Approach for Detecting Abnormalities in Human Heartbeat Rhythm

Jonathan Etumusei; Jorge Martinez Carracedo; Sally I McClean

A Robust Martingale Approach for Detecting Abnormalities in Human Heartbeat Rhythm

Jonathan Etumusei, Jorge Martinez Carracedo, Sally I McClean

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The analysis of electrocardiogram data is vital to the healthcare system to improve and monitor health conditions. Existing algorithms are effective in discovering abnormalities in electrocardiogram data streams but most of these approaches do not focus on the intensity and duration of these anomalies. In this paper, we propose a new method called alignment of the martingale sequence (AMS) that improves previous approaches using dynamic time warping and particle swarm optimisation to obtain the optimal parameter that maximises F1. Our proposed method can also estimate the severity and extent of an abnormal heartbeat rate. Experimental results show that the proposed approach makes some improvements over the traditional method.

This paper won the Best Paper Award

Original language	English
Title of host publication	7th Collaborative European Research Conference
Subtitle of host publication	CERC2021
Publisher	CEUR Workshop Proceedings
Publication status	Accepted/In press - 23 Aug 2021
Event	7th Collaborative European Research Conference (CERC 2021) - Cork, Cork, Ireland Duration: 9 Sept 2021 → 10 Sept 2021 https://www.cerc-conf.eu/

Conference

Conference	7th Collaborative European Research Conference (CERC 2021)
Country/Territory	Ireland
City	Cork
Period	9/09/21 → 10/09/21
Internet address	https://www.cerc-conf.eu/

Keywords

heart rate
Dynamic time warping
ECG sequence
martingales

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

@inproceedings{b57e2e9f5b3d4756872ea57415d676b1,

title = "A Robust Martingale Approach for Detecting Abnormalities in Human Heartbeat Rhythm",

abstract = "The analysis of electrocardiogram data is vital to the healthcare system to improve and monitor health conditions. Existing algorithms are effective in discovering abnormalities in electrocardiogram data streams but most of these approaches do not focus on the intensity and duration of these anomalies. In this paper, we propose a new method called alignment of the martingale sequence (AMS) that improves previous approaches using dynamic time warping and particle swarm optimisation to obtain the optimal parameter that maximises F1. Our proposed method can also estimate the severity and extent of an abnormal heartbeat rate. Experimental results show that the proposed approach makes some improvements over the traditional method.This paper won the Best Paper Award",

keywords = "heart rate, Dynamic time warping, ECG sequence, martingales",

author = "Jonathan Etumusei and {Martinez Carracedo}, Jorge and McClean, {Sally I}",

year = "2021",

month = aug,

day = "23",

language = "English",

booktitle = "7th Collaborative European Research Conference",

publisher = "CEUR Workshop Proceedings",

note = "7th Collaborative European Research Conference (CERC 2021) ; Conference date: 09-09-2021 Through 10-09-2021",

url = "https://www.cerc-conf.eu/",

}

TY - GEN

T1 - A Robust Martingale Approach for Detecting Abnormalities in Human Heartbeat Rhythm

AU - Etumusei, Jonathan

AU - Martinez Carracedo, Jorge

AU - McClean, Sally I

PY - 2021/8/23

Y1 - 2021/8/23

N2 - The analysis of electrocardiogram data is vital to the healthcare system to improve and monitor health conditions. Existing algorithms are effective in discovering abnormalities in electrocardiogram data streams but most of these approaches do not focus on the intensity and duration of these anomalies. In this paper, we propose a new method called alignment of the martingale sequence (AMS) that improves previous approaches using dynamic time warping and particle swarm optimisation to obtain the optimal parameter that maximises F1. Our proposed method can also estimate the severity and extent of an abnormal heartbeat rate. Experimental results show that the proposed approach makes some improvements over the traditional method.This paper won the Best Paper Award

AB - The analysis of electrocardiogram data is vital to the healthcare system to improve and monitor health conditions. Existing algorithms are effective in discovering abnormalities in electrocardiogram data streams but most of these approaches do not focus on the intensity and duration of these anomalies. In this paper, we propose a new method called alignment of the martingale sequence (AMS) that improves previous approaches using dynamic time warping and particle swarm optimisation to obtain the optimal parameter that maximises F1. Our proposed method can also estimate the severity and extent of an abnormal heartbeat rate. Experimental results show that the proposed approach makes some improvements over the traditional method.This paper won the Best Paper Award

KW - heart rate

KW - Dynamic time warping

KW - ECG sequence

KW - martingales

UR - https://check.cerc-conf.eu/2021/

UR - https://proceedings.cerc-conf.eu/?language=ENGLISH

M3 - Conference contribution

BT - 7th Collaborative European Research Conference

PB - CEUR Workshop Proceedings

T2 - 7th Collaborative European Research Conference (CERC 2021)

Y2 - 9 September 2021 through 10 September 2021

ER -

A Robust Martingale Approach for Detecting Abnormalities in Human Heartbeat Rhythm

Abstract

Conference

Keywords

UN SDGs

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