A decision tree – based method for the differential diagnosis of Aortic Stenosis from Mitral Regurgitation using heart sounds

Pavlopoulos, S.; Stasis, A.C.; Loukis, Euripidis N.

doi:10.1186/1475-925x-3-21

Cited by 70 publications

(20 citation statements)

References 22 publications

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“…Furthermore, from the results, the decision-tree method appears as one of the best classifiers in many temporally-aware methods. This fact is consistent with other studies where non-speech sounds (Pavlopoulos, Stasis & Loukis, 2004), or more specifically, environmental sounds (Bravo, Berríos & Aide, 2017) are considered.…”

Section: Discussionsupporting

confidence: 92%

Peer Review #2 of "Temporally-aware algorithms for the classification of anuran sounds (v0.1)"

2018

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Several authors have shown that the sounds of anurans can be used as an indicator of climate change. Hence, the recording, storage and further processing of a huge number of anuran sounds, distributed over time and space, are required in order to obtain this indicator. Furthermore, it is desirable to have algorithms and tools for the automatic classification of the different classes of sounds. In this paper, six classification methods are proposed, all based on the data-mining domain, which strive to take advantage of the temporal character of the sounds. The definition and comparison of these classification methods is undertaken using several approaches. The main conclusions of this paper are that: i) the sliding window method attained the best results in the experiments presented, and even outperformed the Hidden Markov Models usually employed in similar applications; ii) noteworthy overall classification performance has been obtained, which is an especially striking result considering that the sounds analysed were affected by a highly noisy background; iii) the instance selection for the determination of the sounds in the training dataset offers better results than cross-validation techniques; and iv) the temporally-aware classifiers have revealed that they can obtain better performance than their non-temporally-aware counterparts.

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Section: Discussionsupporting

confidence: 92%

Peer Review #2 of "Temporally-aware algorithms for the classification of anuran sounds (v0.1)"

2018

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“…Higher order statistics, used in the analysis of Gaussian, stationary and non-linear processes, typically allow to obtained important results. Higher order statistics are calculated upon higher order moments (HOM) such as m 3 and m 4 , i.e., third and fourth order moment are defined as follows:…”

Section: B Feature Extractionmentioning

confidence: 99%

“…The problem of heart sound segmentation in the presence of murmur has been tackled in the last few years using different approaches. A homomorphic filter was introduced in [2], and a decision-tree based segmentation strategy was suggested for the problem in [3]. Simplicity and strength computation was applied in [4].…”

Section: Introductionmentioning

confidence: 99%

Segmentation and Feature Extraction of Heart Murmurs in Newborns

Amiri¹,

Armano

2013

JOLST

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His research interests include pattern recognition, signal processing and intelligent systems, with applications in Biomedical engineering. Giuliano Armano is associate professor of computer engineering at the Dept. of Electrical and Electronic Engineering (DIEE), University of Cagliari, leading also the IASC ("Intelligent Agents and Soft-Computing") group. His educational background ranges over expert systems and machine learning, whereas his current research activity is focused on machine learning and pattern recognition techniques, mainly experimented in the field of biomedical sciences, information retrieval, and text categorization.

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“…Furthermore, Polat et al [20] developed decision tree based models for the classification of CHD, achieving a correct classification score of 82%. Moreover, Pavlopoulos et al [21] used the C4.5 algorithm decision trees to analyze the different heart sound features, which assist clinicians to make a better diagnosis in CHD.…”

Section: Assessment Of the Risk Factors Of Coronary Heart Events Basementioning

confidence: 99%

Assessment of the Risk Factors of Coronary Heart Events Based on Data Mining With Decision Trees

Karaolis¹,

Moutiris²,

Hadjipanayi³

et al. 2010

IEEE Trans. Inform. Technol. Biomed.

169

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Coronary heart disease (CHD) is one of the major causes of disability in adults as well as one of the main causes of death in the developed countries. Although significant progress has been made in the diagnosis and treatment of CHD, further investigation is still needed. The objective of this study was to develop a data-mining system for the assessment of heart event-related risk factors targeting in the reduction of CHD events. The risk factors investigated were: 1) before the event: a) nonmodifiable-age, sex, and family history for premature CHD, b) modifiable-smoking before the event, history of hypertension, and history of diabetes; and 2) after the event: modifiable-smoking after the event, systolic blood pressure, diastolic blood pressure, total cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, and glucose. The events investigated were: myocardial infarction (MI), percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABG). A total of 528 cases were collected from the Paphos district in Cyprus, most of them with more than one event. Data-mining analysis was carried out using the C4.5 decision tree algorithm for the aforementioned three events using five different splitting criteria. The most important risk factors, as extracted from the classification rules analysis were: 1) for MI, age, smoking, and history of hypertension; 2) for PCI, family history, history of hypertension, and history of diabetes; and 3) for CABG, age, history of hypertension, and smoking. Most of these risk factors were also extracted by other investigators. The highest percentages of correct classifications achieved were 66%, 75%, and 75% for the MI, PCI, and CABG models, respectively. It is anticipated that data mining could help in the identification of high and low risk subgroups of subjects, a decisive factor for the selection of therapy, i.e., medical or surgical. However, further investigation with larger datasets is still needed.

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A decision tree – based method for the differential diagnosis of Aortic Stenosis from Mitral Regurgitation using heart sounds

Cited by 70 publications

References 22 publications

Peer Review #2 of "Temporally-aware algorithms for the classification of anuran sounds (v0.1)"

Peer Review #2 of "Temporally-aware algorithms for the classification of anuran sounds (v0.1)"

Segmentation and Feature Extraction of Heart Murmurs in Newborns

Assessment of the Risk Factors of Coronary Heart Events Based on Data Mining With Decision Trees

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