An autonomous medical monitoring system: Validation on arrhythmia detection

Lemkaddem, Alia; Proença, Martin; Delgado-Gonzalo, Ricard; Renevey, Ph.; Oei, I.; Montano, Giuseppe; Martinez-Heras, José A.; Donati, Alessandro; Bertschi, Mattia; Lemay, Mathieu

doi:10.1109/embc.2017.8037869

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Various approaches for the automatic online detection of cardiac arrhythmias have been proposed in the literature. Machine learning approaches have been adopted by numerous authors [21,22], and as have wavelet- [22,23], artificial neural network (ANN)- [24][25][26], and decision tree-based [27] approaches. For a more detailed review, the reader is referred to recent review papers [18,28].…”

Section: Introductionmentioning

confidence: 99%

A Low-Complexity Model-Free Approach for Real-Time Cardiac Anomaly Detection Based on Singular Spectrum Analysis and Nonparametric Control Charts

Lang

2018

Technologies

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While the importance of continuous monitoring of electrocardiographic (ECG) or photoplethysmographic (PPG) signals to detect cardiac anomalies is generally accepted in preventative medicine, there remain numerous challenges to its widespread adoption. Most notably, difficulties arise regarding crucial characteristics such as real-time capability, computational complexity, the amount of required training data, and the avoidance of too-restrictive modeling assumptions. We propose a lightweight and model-free approach for the online detection of cardiac anomalies such as ectopic beats in ECG or PPG signals on the basis of the change detection capabilities of singular spectrum analysis (SSA) and nonparametric rank-based cumulative sum (CUSUM) control charts. The procedure is able to quickly detect anomalies without requiring the identification of fiducial points such as R-peaks, and it is computationally significantly less demanding than previously proposed SSA-based approaches. Therefore, the proposed procedure is equally well suited for standalone use and as an add-on to complement existing (e.g., heart rate (HR) estimation) procedures.

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Section: Introductionmentioning

confidence: 99%

A Low-Complexity Model-Free Approach for Real-Time Cardiac Anomaly Detection Based on Singular Spectrum Analysis and Nonparametric Control Charts

Lang

2018

Technologies

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“…Various approaches for the automatic online detection of cardiac arrhythmias have been proposed in the literature. Machine Learning approaches have been adopted by numerous authors [21,22], as have Wavelet [22,23], Artificial Neural Network (ANN) [24][25][26], and decision tree [27] based approaches. For a more detailed review, the reader is referred to some recent review papers [18,28].…”

Section: Introductionmentioning

confidence: 99%

A Low-Complexity Model-Free Approach for Real-Time Cardiac Anomaly Detection Based on Singular Spectrum Analysis and Nonparametric Control Charts

Lang¹

2017

Preprint

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While the importance of continuous monitoring of electrocardiographic (ECG) or 1 photoplethysmographic (PPG) signals to detect cardiac anomalies is generally accepted in preventative medicine, 2 there remain major barriers to its actual widespread adoption. Most notably, current approaches tend to lack 3 real-time capability, exhibit high computational cost, and be based on restrictive modeling assumptions or 4 require large amounts of training data. We propose a lightweight and model-free approach for the online 5 detection of cardiac anomalies such as ectopic beats in ECG or PPG signals based on the change detection 6 capabilities of Singular Spectrum Analysis (SSA) and nonparametric rank-based cumulative sum (CUSUM) 7 control charts. The procedure is able to quickly detect anomalies without requiring the identification of fiducial 8 points such as R-peaks and is computationally significantly less demanding than previously proposed SSA-based 9 approaches. Therefore, the proposed procedure is equally well suited for standalone use and as an add-on to 10 complement existing (e.g. heart rate (HR) estimation) procedures.

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Using casual reasoning for anomaly detection among ECG live data streams in ubiquitous healthcare monitoring systems

Qidwai

Chaudhry

Jabbar

et al. 2018

J Ambient Intell Human Comput

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An autonomous medical monitoring system: Validation on arrhythmia detection

Cited by 4 publications

References 13 publications

A Low-Complexity Model-Free Approach for Real-Time Cardiac Anomaly Detection Based on Singular Spectrum Analysis and Nonparametric Control Charts

A Low-Complexity Model-Free Approach for Real-Time Cardiac Anomaly Detection Based on Singular Spectrum Analysis and Nonparametric Control Charts

A Low-Complexity Model-Free Approach for Real-Time Cardiac Anomaly Detection Based on Singular Spectrum Analysis and Nonparametric Control Charts

Using casual reasoning for anomaly detection among ECG live data streams in ubiquitous healthcare monitoring systems

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