Coronary heart disease diagnosis by artificial neural networks including aortic pulse wave velocity index and clinical parameters

Vallée, Alexandre; Cinaud, Alexandre; Blachier, Vincent; Lelong, Hélène; Safar, Michel E.; Blacher, Jacques

doi:10.1097/hjh.0000000000002075

Cited by 24 publications

(21 citation statements)

References 45 publications

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“…A nomogram of aortic PWV was constructed, by a linear regression analysis, and previously described; according to the determinants of the PWV, to determine theoretical aortic PWV values based on age, gender (male = 1 and female = 0), mean BP and HR.…”

Section: Methodsmentioning

confidence: 99%

“…Parameters influencing PWV measurement at baseline and during follow-up can be evaluated independently of sex, age, mean BP, and HR. 10 A nomogram of aortic PWV was constructed, by a linear regression analysis, and previously described 10,19,20 ; according to the determinants of the PWV, 21 to determine theoretical aortic PWV values based on age, gender (male = 1 and female = 0), mean BP and HR.…”

Section: Determination Of Pwv Indexmentioning

confidence: 99%

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Application of a decision tree to establish factors associated with a nomogram of aortic stiffness

Vallée¹,

Safar²,

Blacher³

2019

J of Clinical Hypertension

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Aortic stiffness is a marker of vascular aging and may reflect occurrence of cardiovascular (CV) diseases. Aortic pulse wave velocity (PWV), a marker of aortic stiffness, can be measured by applanation tonometry. A nomogram of aortic stiffness was evaluated by the calculation of PWV index. Theoretical PWV can be calculated according to age, gender, mean blood pressure, and heart rate, allowing to form an individual PWV index [(measured PWV – theoretical PWV)/theoretical PWV]. The purpose of the present cross‐sectional study was to investigate the determinants of the PWV index, by applying a decision tree. A cross‐sectional study was conducted from 2012 to 2017, and 597 individuals were included. A training decision tree was constructed based on seventy percent of these subjects (N = 428). The remaining 30% (N = 169) were used as the testing dataset to evaluate the performance of the decision trees. The input variables for the models were clinical and biochemical parameters. The different input variables remained in the model were diabetes, tobacco status, carotid plaque, albuminuria, C‐reactive protein, total cholesterol, BMI, and previous CV diseases. For the validation decision model, the sensitivity, specificity, and accuracy values for identifying the related risk factors of PWV index were 70%, 78%, and 0.73. Since determinants of PWV index were all well‐accepted CV risk factors, a nomogram of aortic stiffness could be considered as an integrator of CV risk factors on their duration of exposure and could be utilized to develop future programs for CV risk assessment and reduction strategies.

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Section: Methodsmentioning

confidence: 99%

Section: Determination Of Pwv Indexmentioning

confidence: 99%

Application of a decision tree to establish factors associated with a nomogram of aortic stiffness

Vallée¹,

Safar²,

Blacher³

2019

J of Clinical Hypertension

Self Cite

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“…In contrast to the majority of current 'black-box' machine learning methods that are based on artificial neural networks [29,37,38] and whose output decision typically cannot be explained, our method is based on modeling a clear probabilisitic decision process, that can be tracked back and used to justifiy the decision -we believe that this is a critical aspect when using machine learning for supporting clinical decisions. Our HCM-AF-Risk Model addresses data imbalance, and utilizes a set of 18 clinical variables to identify AF cases, and clinical features associated with higher/lower risk for AF in HCM patients.…”

Section: Discussionmentioning

confidence: 99%

HCM-AF-Risk Model to Identify Cases and Predictors of Atrial Fibrillation in Hypertrophic Cardiomyopathy

Bhattacharya

Greenland

et al. 2020

Preprint

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Background AF in HCM is associated with high stroke risk despite low CHA2DS2-VASc scores. Hence, there is need to understand AF pathophysiology and predict AF in HCM. We develop/apply a data-driven, machine learning-based method to identify AF cases and clinical features associated with AF in HCM, using electronic health record (EHR) data. Methods Patients with documented paroxysmal/ persistent/permanent AF (n=191) were considered AF cases, and the remaining patients in sinus rhythm (n=640) were tagged as No-AF. We evaluated 93 clinical variables; the most informative variables useful for distinguishing AF from No-AF cases were selected based on the 2-sample t-test and information gain criterion. Results We identified 18 highly informative clinical variables: 11 are positively associated (e.g. LA-diameter, LV-diastolic dysfunction, LV-LGE), and 7 are negatively correlated (e.g. several exercise parameters) with AF in HCM. Next, patient records were represented via these 18 variables. Data imbalance resulting from the relatively low number of AF cases was addressed via a combination of over-and under-sampling strategies. We trained and tested multiple classifiers under this sampling approach, showing effective classification. Specifically, an ensemble of logistic regression and naïve Bayes classifiers, trained based on the 18 variables and corrected for data imbalance, proved most effective for separating AF from No-AF cases (sensitivity=0.74, specificity=0.72, C-index=0.80). Conclusions Our model (HCM-AF-Risk Model), the first machine learning-based method for identification of AF cases in HCM, demonstrates good performance, and suggests that AF is associated with a more severe cardiac HCM-phenotype.

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“…PPG provides the most basic physiological signal of the human body, including hemodynamics [9] and circulation information of the autonomic nervous system [10], which is comprehensively displayed in pulse waves, amplitudes, wave speed, and rhythm. These parameters are an important basis for evaluating human physiological state and clinical diagnosis [11] in medicine. The detection technology of physiological signals has developed from contact to noncontact processes, as shown in Figure 1.…”

Section: Related Workmentioning

confidence: 99%

Combining Chrominance Features and Fast ICA for Noncontact Imaging Photoplethysmography

Cai

Yue

et al. 2020

IEEE Access

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Video-based noncontact detection of heart rate has a wide range of applications in the field of medicine and health. However, this method is susceptible to noise interference, making it difficult to effectively extract blood volume pulse (BVP) signals. To overcome this problem, a new method of noncontact heart rate estimation that can suppress noise interference is proposed in this paper. First, the established data acquisition system conducts video collection, and the captured videos are divided into multiple small regions. Subsequently, the initial signals of BVP are extracted in accordance with the chrominance features extracted through multi-channel data fusion. The BVP signals are separated using the FastICA algorithm. The kurtosis value and signal-to-noise ratios of the power spectrum of the separated signals are analyzed to determine the effective separation component. Results show that this method can extract and process pulse signals, effectively suppressing non-periodic interference. The experiment also proves that the method has good consistency with the measurement of pulse oximeter and has good stability and accuracy in the detection of heart rate of the human body. INDEX TERMS Chrominance features, kurtosis, photoplethysmography(PPG), data harvesting fusion.

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Coronary heart disease diagnosis by artificial neural networks including aortic pulse wave velocity index and clinical parameters

Cited by 24 publications

References 45 publications

Application of a decision tree to establish factors associated with a nomogram of aortic stiffness

Application of a decision tree to establish factors associated with a nomogram of aortic stiffness

HCM-AF-Risk Model to Identify Cases and Predictors of Atrial Fibrillation in Hypertrophic Cardiomyopathy

Combining Chrominance Features and Fast ICA for Noncontact Imaging Photoplethysmography

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