Remote Health Monitoring of Heart Failure With Data Mining via CART Method on HRV Features

Pecchia, Leandro; Melillo, Paolo; Bracale, M.

doi:10.1109/tbme.2010.2092776

Cited by 102 publications

(50 citation statements)

References 25 publications

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“…In fact, ECG monitoring is beneficial for several cardiovascular diseases, and the application of ECG monitoring during real-life activities are under investigation for several purposes and particularly because of its effectiveness as early detector of cardiovascular diseases worsening [5,11,12]. Accordingly, most of the wearable and ambient sensing technologies aiming to monitor older subjects in real life include ECG or HRV monitoring.…”

Section: Introductionmentioning

confidence: 99%

Fall Prediction in Hypertensive Patients via Short-Term HRV Analysis

Castaldo

Melillo

Izzo

et al. 2017

IEEE J. Biomed. Health Inform.

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A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription.For more information, please contact the WRAP Team at: publications@warwick.ac.ukAbstract-Falls are a major problem of later life having severe consequences on quality of life and a significant burden in occidental countries. Many technological solutions have been proposed to assess the risk or to predict falls and the majority is based on accelerometers and gyroscopes. However, very little was done for identifying first time fallers, which are very difficult to recognise. This paper presents a meta-model predicting falls using short term Heart Rate Variability (HRV) analysis acquired at the baseline. 170 hypertensive patients (age: 72 ± 8 years, 56 female) were investigated, of which 34 fell once in the 3 months after the baseline assessment. This study is focused on hypertensive patients, which were considered as convenient pragmatic sample, as they undergo regular outpatient visits, during which short term ECG can be easily recorded without significant increase of healthcare costs. For each subject, 11 consecutive excerpts of 5 minutes each (55 min) were extracted from ECGs recorded between 10:30 and 12:30 and analysed. Linear and nonlinear HRV features were extracted and averaged among the 11 excerpts, which were, then, considered for the statistical and data mining analysis. The best predictive meta-model was based on Multinomial Naïve Bayes, which enabled to predict first-time fallers with sensitivity, specificity and accuracy rates of 72%, 61%, 68% respectively.

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Section: Introductionmentioning

confidence: 99%

Fall Prediction in Hypertensive Patients via Short-Term HRV Analysis

Castaldo

Melillo

Izzo

et al. 2017

IEEE J. Biomed. Health Inform.

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“…CART was applied to HRV measures for other investigations, such as for the diagnosis of Obstructive Sleep Apnea Syndrome [31], and for the analysis of the relationship between HRV and the menstrual cycle in healthy young women [34]. We have adopted CART in previous studies, to investigate discrimination power of short-term HRV features [28][29] in distinguishing CHF patients from normal subjects and in assessing CHF severity. To the best of the authors' knowledge, CART has not yet been applied to long-term HRV analysis for CHF diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Discrimination power of long-term heart rate variability measures for chronic heart failure detection

Melillo

Fusco

Sansone

et al. 2011

Med Biol Eng Comput

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“…On the other hand, the indirect method corresponds to the use of various signal processing and machine learning techniques for detection and localization of cardiovascular diseases. There are number of signal processing techniques like heart rate variability analysis [21][22][23][24][25], discrete and continuous wavelet transform based analysis [26,27], auto-regressive model coefficients [28], discrete cosine transform coefficients [29], principal component analysis [30,31], linear discriminant analysis [32], independent component analysis [33], polynomial regression coefficients [34] etc. are reported in literature for extracting clinical diagnostic features from ECG for arrhythmia detection and classification.…”

Section: Ecg Feature Extraction For Disease Detection: a Reviewmentioning

confidence: 99%

Quantification of Diagnostic Information from Electrocardiogram Signal: A Review

Dandapat

Sharma

Tripathy

2015

Lecture Notes in Electrical Engineering

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Electrocardiogram (ECG) contains the information about the contraction and relaxation of heart chambers. This diagnostic information will change due to various cardiovascular diseases. This information is used by a cardiologist for accurate detection of various life-threatening cardiac disorders. ECG signals are subjected to number of processing, for computer aided detection and localization of cardiovascular diseases. These processing schemes are categorized as filtering, synthesis, compression and transmission. Quantifying diagnostic information from an ECG signal in an efficient way, is always a challenging task in the area of signal processing. This paper presents a review on state-of-art diagnostic information extraction approaches and their applications in various ECG signal processing schemes such as quality assessment and cardiac disease detection. Then, a new diagnostic measure for multilead ECG (MECG) is proposed. The proposed diagnostic measure (MSD) is defined as the difference between multivariate sample entropy values for original and processed MECG signals. The MSD measure is evaluated over MECG compression framework. Experiments are conducted over both normal and pathological MECG from PTB database. The results demonstrate that the proposed MSD measure is effective in quantifying diagnostic information in MECG. The MSD measure is also compare with other measures such as WEDD, PRD and RMSE.

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Remote Health Monitoring of Heart Failure With Data Mining via CART Method on HRV Features

Cited by 102 publications

References 25 publications

Fall Prediction in Hypertensive Patients via Short-Term HRV Analysis

Fall Prediction in Hypertensive Patients via Short-Term HRV Analysis

Discrimination power of long-term heart rate variability measures for chronic heart failure detection

Quantification of Diagnostic Information from Electrocardiogram Signal: A Review

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