Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines

Kher, Rahul; Pawar, Tanmay; Thakar, Vishvjit K.; Shah, Hemanshi

doi:10.3109/03091902.2014.998372

Cited by 16 publications

(3 citation statements)

References 35 publications

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“…The results of the proposed rule-based feature extraction and SVM classification method were compared to those of the previous works. The SVM classification method has been widely used in the detection of myocardial infarction, arrhythmia, and physical activities recognition from ambulatory ECG signals [ 56 , 57 ]. Although it is a blind classification method, diagnosis methods based on SVM often achieve high sensitivity and accuracy.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection

Tseng

Lin

Jaw

2016

Computational and Mathematical Methods in Medicine

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An automatic method is presented for detecting myocardial ischemia, which can be considered as the early symptom of acute coronary events. Myocardial ischemia commonly manifests as ST- and T-wave changes on ECG signals. The methods in this study are proposed to detect abnormal ECG beats using knowledge-based features and classification methods. A novel classification method, sparse representation-based classification (SRC), is involved to improve the performance of the existing algorithms. A comparison was made between two classification methods, SRC and support-vector machine (SVM), using rule-based vectors as input feature space. The two methods are proposed with quantitative evaluation to validate their performances. The results of SRC method encompassed with rule-based features demonstrate higher sensitivity than that of SVM. However, the specificity and precision are a trade-off. Moreover, SRC method is less dependent on the selection of rule-based features and can achieve high performance using fewer features. The overall performances of the two methods proposed in this study are better than the previous methods.

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Section: Discussionmentioning

confidence: 99%

Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection

Tseng

Lin

Jaw

2016

Computational and Mathematical Methods in Medicine

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“…In the literature, the methodologies that have been proposed span from signal processing (ECG [63][64][65], gait analysis [66], food ingestion analysis [67], smoking habit monitoring [68]) to methods based on machine learning techniques (for activity detection [69][70][71][72][73][74][75], falls detection [76], vital signs [77], cigarette smoking [78], position recognition [79,80], social influence [82]), with particular relevance of artificial neural networks (ANNs) [82][83][84][85] and hidden Markov models (HMMs) [86][87][88]. Interestingly, few of the proposed methods have already been integrated in the devices [65,67,83,89]; they rather work offline.…”

Section: Wearable Technologymentioning

confidence: 99%

Progress in Biomedical Knowledge Discovery: A 25-year Retrospective

Sacchi¹,

Holmes

2016

Yearb Med Inform

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Summary Objectives: We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning. Methods: We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: Knowledge Discovery OR Data Mining OR Pattern Discovery OR Pattern Recognition, Automated. Results: A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992-2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992. Conclusions: Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains.Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25 years will bring with regard to the availability of even more complex data and to the methods that could be, and are being now developed for the discovery of new knowledge in biomedical data.

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“…Unlike the standard ECG, the ambulatory ECG records the signal continuously over a long period out-of-hospital environment using the conventional Holter monitor [2] or the trendy wearable devices [3]. This allows the analysis of ambulatory cardiac signals that can assist in various medical applications [4][5][6][7][8] including the diagnosis of cardiac arrhythmias that can lead to sudden death or heart failure among patients [9,10].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Effects of Noisy Electrocardiogram Signal on Heartbeat Detection Performance

et al. 2020

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Heartbeat detection for ambulatory cardiac monitoring is more challenging as the level of noise and artefacts induced by daily-life activities are considerably higher than monitoring in a hospital setting. It is valuable to understand the relationship between the characteristics of electrocardiogram (ECG) noises and the beat detection performance in the cardiac monitoring system. For this purpose, three well-known algorithms for the beat detection process were re-implemented. The beat detection algorithms were validated using two types of ambulatory datasets, which were the ECG signal from the MIT-BIH Arrhythmia Database and the simulated noise-contaminated ECG signal with different intensities of baseline wander (BW), muscle artefact (MA) and electrode motion (EM) artefact from the MIT-BIH Noise Stress Test Database. The findings showed that signals contaminated with noise and artefacts decreased the potential of beat detection in ambulatory signal with the poorest performance noted for ECG signal affected by the EM artefacts. In conclusion, none of the algorithms was able to detect all QRS complexes without any false detection at the highest level of noise. The EM noise influenced the beat detection performance the most in comparison to the MA and BW noises that resulted in the highest number of misdetections and false detections.

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Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines

Cited by 16 publications

References 35 publications

Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection

Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection

Progress in Biomedical Knowledge Discovery: A 25-year Retrospective

An Analysis of the Effects of Noisy Electrocardiogram Signal on Heartbeat Detection Performance

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