A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG)

Li, Hong Zu; Boulanger, Pierre

doi:10.3390/s20051461

Cited by 65 publications

(52 citation statements)

References 79 publications

(132 reference statements)

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“…Explaining outliers detected in medical data is critical to ensure that practitioners and patients trust the system to make accurate predictions or diagnoses. For example, ECG signals are often mangled by artifacts that have no relation to the heart functions [ 59 ]. The artifacts can be caused by device errors or by motion.…”

Section: Applications Of Outlier Explanationsmentioning

confidence: 99%

A survey on outlier explanations

et al. 2022

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While many techniques for outlier detection have been proposed in the literature, the interpretation of detected outliers is often left to users. As a result, it is difficult for users to promptly take appropriate actions concerning the detected outliers. To lessen this difficulty, when outliers are identified, they should be presented together with their explanations. There are survey papers on outlier detection, but none exists for outlier explanations. To fill this gap, in this paper, we present a survey on outlier explanations in which meaningful knowledge is mined from anomalous data to explain them. We define different types of outlier explanations and discuss the challenges in generating each type. We review the existing outlier explanation techniques and discuss how they address the challenges. We also discuss the applications of outlier explanations and review the existing methods used to evaluate outlier explanations. Furthermore, we discuss possible future research directions.

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Section: Applications Of Outlier Explanationsmentioning

confidence: 99%

A survey on outlier explanations

et al. 2022

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“…Since there exist different kinds of heart diseases [10], an ELM engine is desirable to show reconfigurability for requirements of various ECG anomaly detections. In [9], the ELM engine only supports an input dimension fixed at 128, and thus it cannot process ECG signals with a longer duration.…”

Section: ) Arbitrarily Reconfigurable and End-to-end Elm Designmentioning

confidence: 99%

An Arbitrarily Reconfigurable Extreme Learning Machine Inference Engine for Robust ECG Anomaly Detection

Chuang

Chen

et al. 2021

IEEE Open J. Circuits Syst.

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Extreme learning machine (ELM) has shown to be an effective and low-power approach for real-time electrocardiography (ECG) anomaly detection. However, prior ELM inference chips are noise-prone and lacking in reconfigurability. In this article, we present an arbitrarily reconfigurable ELM inference engine fabricated in 40-nm CMOS technology for robust ECG anomaly detection. By combining Adaptive boosting (Adaboost) and Eigenspace denoising with ELM (AE-ELM), robust classification under noisy conditions is achieved and saves the number of required multiplications by 95.9%. For chip implementation, a reconfigurable VLSI architecture is designed to support arbitrary complexity of AE-ELM, accounting for dynamic change in application requirements. On the other hand, we propose to construct the input weight matrix of ELM as a Bernoulli random matrix, which further reduces the number of multiplications by 55.2%. For real-time detection, parallel computing is exploited to reduce the latency by up to 86.8%. Overall, the 0.21-mm 2 AE-ELM inference engine shows its robustness against noisy signals and achieves 1.83x AEE compared with the state-of-the-art ELM design. INDEX TERMS Adaptive boosting (Adaboost), eigenspace denoising, electrocardiography (ECG) anomaly detection, extreme learning machine (ELM), reconfigurable chip design.

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“…An electrocardiogram (ECG) is simple, quick, safe and painless way recording the electrical activity generated and conducted in the heart. It is an effective non-invasive tool for various biomedical applications such as measuring the heart rate, examining the rhythm of heartbeats, diagnosing heart abnormalities, emotion and physical recognition and biometric identification (Kaplan Berkaya et al 2018;Satija, Ramkumar, and Sabarimalai Manikandan 2018;Li and Boulanger 2020). Different patterns of electrodes' placement over patient's skin provide different observations of heart, called leads.…”

Section: Ecg Analysismentioning

confidence: 99%

“…However, since some arrhythmias are related to a long-time behaviour and/or may occur unpredictably, the records should be extended to several hours, turning the inspect process tedious and human error prone. Moreover, the diagnosis is highly dependent on the analyser experience becoming susceptible to inter observer variabilities whom must be proof of the patients particularities (Hagiwara et al 2018;Borghi, Borges, and Teixeira 2021;Borghi 2020;Li and Boulanger 2020). Due to these challenges and the high amount of data to manually inspect, automated approaches by using signal processing and machine learning techniques emerges as a good alternative, being reported several proposes at this application field in recent years.…”

Section: Introductionmentioning

confidence: 99%

Brief Review on Electrocardiogram Analysis and Classification Techniques with Machine Learning Approaches

Borghi

2021

UPjeng

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Electrocardiogram captures the electrical activity of the heart. The signal obtained can be used for various purposes such as emotion recognition, heart rate measuring and the main one, cardiac disease diagnosis. But ECG analysis and classification require experienced specialists once it presents high variability and suffers interferences from noises and artefacts. With the increase of data amount on long term records, it might lead to long term dependencies and the process become exhaustive and error prone. Automated systems associated with signal processing techniques aim to help on these tasks by improving the quality of data, extracting meaningful features, selecting the most suitable and training machine learning models to capture and generalize its behaviour. This review brings a brief stage sense of how data flows into these approaches and somewhat techniques are most used. It ends by presenting some of the countless applications that can be found in the research community.

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A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG)

Cited by 65 publications

References 79 publications

A survey on outlier explanations

A survey on outlier explanations

An Arbitrarily Reconfigurable Extreme Learning Machine Inference Engine for Robust ECG Anomaly Detection

Brief Review on Electrocardiogram Analysis and Classification Techniques with Machine Learning Approaches

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