Noise detection on ECG based on agglomerative clustering of morphological features

Rodrigues, João; Belo, David; Gamboa, Hugo

doi:10.1016/j.compbiomed.2017.06.009

Cited by 54 publications

(41 citation statements)

References 23 publications

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“…Noise and artefact in ECG recording are something that almost cannot be avoided. The patient movement, local muscle contraction and electrical interference are some causes of noise and artefact in the recording process (Rodrigues, Belo, and Gamboa 2017). In automatic ECG signal labeling depends mainly on the algorithm and the quality of the recording (Imtiaz et al 2016).…”

Section: B Discussionmentioning

confidence: 99%

Neural Network based - Arrhythmia Monitoring Device: A Pivotal Clinical Trial

2020

IJITEE

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Arrhythmia or irregular heart beat had wide range of clinical manifestations, from benign arrhythmia that not need any medication to life threatening condition. It can occur permanently or intermittently. Intermittent arrhythmia needs specific diagnostic tools that can record the electrocardiogram continuously. This research was sought to analysed the sensitivity, specificity, Positive Predictive Value, and Negative Predictive Value of arrhythmia monitoring device that based on neural network based artificial intelligent. The pivotal clinical trial was involved a total 103 people (health and stable arrhythmia patients). This research used a diagnostic test by comparing the electrocardiography (ECG) from prototype with standard ECG for diagnose arrhythmia. The Arrhythmia Monitoring System that we developed has three hardware components; smartphones, server for arrhythmia detection and patchable ECG recorder. All three components are connected with internet of things (IoT) technology. The architecture of Arrhythmia software monitoring included ECG signals pre-processing, beats detection, features extraction for detecting VT/VF, and classification for detecting VT/VF. Features extraction such as heart rate variability (HRV) and T wave alternans. We compared the ECG of arrhythmia prototype monitoring device with standard Holter monitoring. We enrolled 103 patients. There was no significant difference of heart rate between arrhythmia prototype monitoring device and standard Holter (87.26 ± 11.2 vs 86.07±9.15, P=0.43) . There was significant different of maximum and minimum heart rate between arrhythmia prototype monitoring device and standard holter monitoring (121.3±31.7 vs 131.0±10.8, p= 0.000, and 65.1±13.5 vs 73.07±10.02, p=0.000) . This device has low sensitivity 80% (95% CI 75% -82%) and high specificity 91.8% (95% CI 85% -92%) for detecting the abnormal ECG. The Positive Predictive Value (PPV) was 63.2% (95% CI 58.8% -67.52%) and Negative Predictive Value (NVP) was 96.3% (95% CI 94.7% -98.3%) 71.4% (95% CI 66.4% -76.4%) and 75% (95% CI 72%-78%), Specificity 97.8% (95% CI 95.8-99.8%) and 91.7% (95% CI 83.4%-99.7%, respectively). The PPV of this device to detect PVC and PAC was 71.4% (95% CI 66.4%-76.4%) and 72.7%. (95% CI 68.7%-76.7%) The NPV of this device to detect PVC and PAC was 97. 8% (95% CI 95.8%-99.85) and 98.9% (95% CI 98.1%-99.7%), respectively. This study found that the device to be a valuable diagnostic tool that has relatively low sensitivity but high specificity for diagnosing Abnormal ECG, PVC and PAC. According to the results of our study, we found that the device to be a valuable diagnostic tool that has relatively high sensitivity and specificity for diagnosing Abnormal ECG, PVC and PAC. . This device demonstrates an ability to detect PVC and PAC (Sensitivity

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Section: B Discussionmentioning

confidence: 99%

Neural Network based - Arrhythmia Monitoring Device: A Pivotal Clinical Trial

2020

IJITEE

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“…Differently from our present research, these database recordings are not continuously labeled in terms of their signal quality. In addition, these studies seldom describe a detailed noise scale classification, but only the usual BW, muscle artifact, and PLI noise powers [ 35 , 36 , 37 , 38 ], and controlled noise with different SNR values is often added to the ECG signal [ 39 , 40 , 41 ] or is assigned in global terms of acceptable or unacceptable signals for the ECG quality assessment [ 29 , 42 , 43 ].…”

Section: Discussionmentioning

confidence: 99%

Noise Maps for Quantitative and Clinical Severity Towards Long-Term ECG Monitoring

Everss

Melgarejo-Meseguer

Blanco–Velasco

et al. 2017

Sensors

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Noise and artifacts are inherent contaminating components and are particularly present in Holter electrocardiogram (ECG) monitoring. The presence of noise is even more significant in long-term monitoring (LTM) recordings, as these are collected for several days in patients following their daily activities; hence, strong artifact components can temporarily impair the clinical measurements from the LTM recordings. Traditionally, the noise presence has been dealt with as a problem of non-desirable component removal by means of several quantitative signal metrics such as the signal-to-noise ratio (SNR), but current systems do not provide any information about the true impact of noise on the ECG clinical evaluation. As a first step towards an alternative to classical approaches, this work assesses the ECG quality under the assumption that an ECG has good quality when it is clinically interpretable. Therefore, our hypotheses are that it is possible (a) to create a clinical severity score for the effect of the noise on the ECG, (b) to characterize its consistency in terms of its temporal and statistical distribution, and (c) to use it for signal quality evaluation in LTM scenarios. For this purpose, a database of external event recorder (EER) signals is assembled and labeled from a clinical point of view for its use as the gold standard of noise severity categorization. These devices are assumed to capture those signal segments more prone to be corrupted with noise during long-term periods. Then, the ECG noise is characterized through the comparison of these clinical severity criteria with conventional quantitative metrics taken from traditional noise-removal approaches, and noise maps are proposed as a novel representation tool to achieve this comparison. Our results showed that neither of the benchmarked quantitative noise measurement criteria represent an accurate enough estimation of the clinical severity of the noise. A case study of long-term ECG is reported, showing the statistical and temporal correspondences and properties with respect to EER signals used to create the gold standard for clinical noise. The proposed noise maps, together with the statistical consistency of the characterization of the noise clinical severity, paves the way towards forthcoming systems providing us with noise maps of the noise clinical severity, allowing the user to process different ECG segments with different techniques and in terms of different measured clinical parameters.

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“…After this process, a sliding window model was used for extracting samples of a set of EMG and HRV parameters over time, in order to explore trends. e window of dimension WS z slides over each sample of the time series, taking into consideration a defined overlapping factor, dependent of the chosen time-step TS y , between consecutive windows [32]. Table 1 lists the parameters that were extracted from the EMG signal and HRV data.…”

Section: Processing Stage -Features and Sliding Windowmentioning

confidence: 99%

Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor

Ramos

Vaz

Mendonça

et al. 2020

Journal of Healthcare Engineering

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Research in physiology and sports science has shown that fatigue, a complex psychophysiological phenomenon, has a relevant impact in performance and in the correct functioning of our motricity system, potentially being a cause of damage to the human organism. Fatigue can be seen as a subjective or objective phenomenon. Subjective fatigue corresponds to a mental and cognitive event, while fatigue referred as objective is a physical phenomenon. Despite the fact that subjective fatigue is often undervalued, only a physically and mentally healthy athlete is able to achieve top performance in a discipline. Therefore, we argue that physical training programs should address the preventive assessment of both subjective and objective fatigue mechanisms in order to minimize the risk of injuries. In this context, our paper presents a machine-learning system capable of extracting individual fatigue descriptors (IFDs) from electromyographic (EMG) and heart rate variability (HRV) measurements. Our novel approach, using two types of biosignals so that a global (mental and physical) fatigue assessment is taken into account, reflects the onset of fatigue by implementing a combination of a dimensionless (0-1) global fatigue descriptor (GFD) and a support vector machine (SVM) classifier. The system, based on 9 main combined features, achieves fatigue regime classification performances of 0.82±0.24, ensuring a successful preventive assessment when dangerous fatigue levels are reached. Training data were acquired in a constant work rate test (executed by 14 subjects using a cycloergometry device), where the variable under study (fatigue) gradually increased until the volunteer reached an objective exhaustion state.

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Noise detection on ECG based on agglomerative clustering of morphological features

Cited by 54 publications

References 23 publications

Neural Network based - Arrhythmia Monitoring Device: A Pivotal Clinical Trial

Neural Network based - Arrhythmia Monitoring Device: A Pivotal Clinical Trial

Noise Maps for Quantitative and Clinical Severity Towards Long-Term ECG Monitoring

Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor

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