Nonlinear Heart Rate Variability based artificial intelligence in lung cancer prediction

Time-domain heart rate dynamics in the prognosis of progressive atherosclerosis

BACKGROUND AND AIM

The regular uptake of a high-fat diet (HFD) with changing lifestyle causes atherosclerosis leading to cardiovascular diseases and autonomic dysfunction. Therefore, the current study aimed to investigate the correlation of autonomic activity to lipid and atherosclerosis markers. Further, the study proposes a support vector machine (SVM) based model in the prediction of atherosclerosis severity.

METHODS AND RESULTS

The Lead-II electrocardiogram and blood markers were measured from both the control and the experiment subjects each week for nine consecutive weeks. The time-domain heart rate variability (HRV) parameters were derived, and the significance level was tested using a one-way Analysis of Variance. The correlation analysis was performed to determine the relation between autonomic parameters and lipid and atherosclerosis markers. The statistically significant time-domain values were used as features of the SVM. The observed results demonstrated the reduced time domain HRV parameters with the increase in lipid and atherosclerosis index markers with the progressive atherosclerosis severity. The correlation analysis revealed a negative association between time-domain HRV parameters with lipid and atherosclerosis parameters. The percentage accuracy increases from 86.58% to 98.71% with the increase in atherosclerosis severity with regular consumption of HFD.

CONCLUSIONS

Atherosclerosis causes autonomic dysfunction with reduced HRV. The negative correlation between autonomic parameters and lipid profile and atherosclerosis indexes marker revealed the potential role of vagal activity in the prognosis of atherosclerosis progression. The support vector machine presented a respectable accuracy in the prediction of atherosclerosis severity from the control group.

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review

Background

The impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs.

Methods

PubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study.

Results

ML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy.

Conclusion

Overall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial designs

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308345, identifier CRD42022308345.

Heart rate dynamics in the prediction of coronary artery disease and myocardial infarction using artificial neural network and support vector machine

BACKGROUND

Atherosclerosis leads to coronary artery disease (CAD) and myocardial infarction (MI), a major cause of morbidity and mortality worldwide. The computer-aided prognosis of atherosclerotic events with the electrocardiogram (ECG) derived heart rate variability (HRV) can be a robust method in the prognosis of atherosclerosis events.

METHODS

A total of 70 male subjects aged 55 ± 5 years participated in the study. The lead-II ECG was recorded and sampled at 200 Hz. The tachogram was obtained from the ECG signal and used to extract twenty-five HRV features. The one-way Analysis of variance (ANOVA) test was performed to find the significant differences between the CAD, MI, and control subjects. Features were used in the training and testing of a two-class artificial neural network (ANN) and support vector machine (SVM).

RESULTS

The obtained results revealed depressed HRV under atherosclerosis. Accuracy of 100% was obtained in classifying CAD and MI subjects from the controls using ANN. Accuracy was 99.6% with SVM, and in the classification of CAD from MI subjects using SVM and ANN, 99.3% and 99.0% accuracy was obtained respectively.

CONCLUSIONS

Depressed HRV has been suggested to be a marker in the identification of atherosclerotic events. The good accuracy observed in classification between control, CAD, and MI subjects, revealed it to be a non-invasive cost-effective approach in the prognosis of atherosclerotic events.

Cancer classification using machine learning and HRV analysis: preliminary evidence from a pilot study

Most cancer patients exhibit autonomic dysfunction with attenuated heart rate variability (HRV) levels compared to healthy controls. This research aimed to create and evaluate a machine learning (ML) model enabling discrimination between cancer patients and healthy controls based on 5-min-ECG recordings. We selected 12 HRV features based on previous research and compared the results between cancer patients and healthy individuals using Wilcoxon sum-rank test. Recursive Feature Elimination (RFE) identified the top five features, averaged over 5 min and employed them as input to three different ML. Next, we created an ensemble model based on a stacking method that aggregated the predictions from all three base classifiers. All HRV features were significantly different between the two groups. SDNN, RMSSD, pNN50%, HRV triangular index, and SD1 were selected by RFE and used as an input to three different ML. All three base-classifiers performed above chance level, RF being the most efficient with a testing accuracy of 83%. The ensemble model showed a classification accuracy of 86% and an AUC of 0.95. The results obtained by ML algorithms suggest HRV parameters could be a reliable input for differentiating between cancer patients and healthy controls. Results should be interpreted in light of some limitations that call for replication studies with larger sample sizes.

Heart rate variability time domain features in automated prediction of diabetes in rat

Diabetes is a very common occurring disease, diagnosed by hyperglycemia. The established mode of diagnosis is the analysis of blood glucose level with the help of a hand-held glucometer. Nowadays, it is also known for affecting multi-organ functions, particularly the microvasculature of the cardiovascular system. In this work, an alternative diagnostic system based on the heart rate variability (HRV) analysis and artificial neural network (ANN) and support vector machine (SVM) have been proposed. The experiment and data recording has been performed on male Wister rats of 10-12 week of age and 200 ± 20 gm of weight. The digital lead-I electrocardiogram (ECG) data are recorded from control (n = 5) and Streptozotocin-induced diabetic rats (n = 5). Nine time-domain linear HRV parameters are computed from 60 s of ECG data epochs and used for the training and testing of backpropagation ANN and SVM. Total 526 (334 Control and 192 diabetics) such datasets are computed for the testing of ANN for the identification of the diabetic conditions. The ANN has been optimized for architecture 9:5:1 (Input: hidden: output neurons, respectively) with the optimized learning rate parameter at 0.02. With this network, a very good classification accuracy of 96.2% is achieved. While similar accuracy of 95.2% is attained using SVM. Owing to the successful implementation of HRV parameters based automated classifiers for diabetic conditions, a non-invasive, ECG based online prognostic system can be developed for accurate and non-invasive prediction of the diabetic condition.

The application of artificial intelligence and radiomics in lung cancer

Lung cancer is one of the most leading causes of death throughout the world, and there is an urgent requirement for the precision medical management of it. Artificial intelligence (AI) consisting of numerous advanced techniques has been widely applied in the field of medical care. Meanwhile, radiomics based on traditional machine learning also does a great job in mining information through medical images. With the integration of AI and radiomics, great progress has been made in the early diagnosis, specific characterization, and prognosis of lung cancer, which has aroused attention all over the world. In this study, we give a brief review of the current application of AI and radiomics for precision medical management in lung cancer.

NONLINEAR HEART RATE VARIABILITY-BASED ANALYSIS AND PREDICTION OF PERFORMANCE STATUS IN PULMONARY METASTASES PATIENTS

Cancer causes chronic stress and is associated with impaired autonomic nervous system (ANS). Heart rate variability (HRV) has been suggested to be an important tool in the identification and prediction of performance status (PS) in cancer. Lead II surface electrocardiogram (ECG) was recorded from 24 pulmonary metastases (PM) subjects and 30 healthy controls for nonlinear HRV analysis. Artificial neural network (ANN) and support vector machine (SVM) were applied for the prediction analysis. Analysis of variance (ANOVA) along with post-hoc Tukey’s HSD test was conducted using statistical R, 64-bit, v.3.3.2, at [Formula: see text]. The obtained results suggested lower HRV that increases with cancer severity from the Eastern Cooperative Oncology Group (ECOG)1 PS to ECOG4 PS. ANOVA results stated that approximate entropy (ApEn) ([Formula: see text]-[Formula: see text], [Formula: see text]), detrended fluctuation analysis (DFA) [Formula: see text] ([Formula: see text]-[Formula: see text], [Formula: see text]) and correlation dimension (CD) ([Formula: see text]-[Formula: see text], [Formula: see text]) were significant. The 13 nonlinear features were fed to ANN and SVM to obtain 82.25% and 100% accuracies, respectively. Nonlinear HRV analysis has given promising results in the prediction of diagnosis of PS in PM patients. These inputs would be very useful for clinicians to diagnose PS in their cancer patients and improve their quality of living.