Recognizing cardiovascular risk from photoplethysmogram signals using ELM

Shobitha, S; Sandhya, R.; B, Niranjana Krupa; Ali, Mohd

doi:10.1109/ccip.2016.7802864

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Hosseini et al [ 48 ] utilized finger PPG, a noninvasive optical signal collected before and after reactive hyperemia, to distinguish between people with various CVDs, with a maximum accuracy of 81.5% for the KNN classifier. Shobitha et al [ 49 ] used the extreme learning machine (ELM), a supervised learning algorithm, to classify PPG signals as normal or affected by cardiovascular illness and compared its performance with backpropagation and support vector machine (SVM) techniques. These algorithms were validated by testing healthy and pathological signals from each of the 30 patients.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Techniques for the Performance Enhancement of Multiple Classifiers in the Detection of Cardiovascular Disease from PPG Signals

Palanisamy,

Rajaguru

2023

Bioengineering

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Photoplethysmography (PPG) signals are widely used in clinical practice as a diagnostic tool since PPG is noninvasive and inexpensive. In this article, machine learning techniques were used to improve the performance of classifiers for the detection of cardiovascular disease (CVD) from PPG signals. PPG signals occupy a large amount of memory and, hence, the signals were dimensionally reduced in the initial stage. A total of 41 subjects from the Capno database were analyzed in this study, including 20 CVD cases and 21 normal subjects. PPG signals are sampled at 200 samples per second. Therefore, 144,000 samples per patient are available. Now, a one-second-long PPG signal is considered a segment. There are 720 PPG segments per patient. For a total of 41 subjects, 29,520 segments of PPG signals are analyzed in this study. Five dimensionality reduction techniques, such as heuristic- (ABC-PSO, cuckoo clusters, and dragonfly clusters) and transformation-based techniques (Hilbert transform and nonlinear regression) were used in this research. Twelve different classifiers, such as PCA, EM, logistic regression, GMM, BLDC, firefly clusters, harmonic search, detrend fluctuation analysis, PAC Bayesian learning, KNN-PAC Bayesian, softmax discriminant classifier, and detrend with SDC were utilized to detect CVD from dimensionally reduced PPG signals. The performance of the classifiers was assessed based on their metrics, such as accuracy, performance index, error rate, and a good detection rate. The Hilbert transform techniques with the harmonic search classifier outperformed all other classifiers, with an accuracy of 98.31% and a good detection rate of 96.55%.

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Section: Resultsmentioning

confidence: 99%

Machine Learning Techniques for the Performance Enhancement of Multiple Classifiers in the Detection of Cardiovascular Disease from PPG Signals

Palanisamy,

Rajaguru

2023

Bioengineering

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Metaheuristic-Based Dimensionality Reduction and Classification Analysis of PPG Signals for Interpreting Cardiovascular Disease

Prabhakar

Rajaguru²,

Lee

2019

IEEE Access

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One of the most useful methodologies which provide a specific waveform showing the pulsating peripheral blood flow in a non-invasive manner is Photoplethysmography (PPG). The design, application and implementation of a PPG system are quite inexpensive and have a very easy maintenance. Without having direct contact with the surface of the skin, PPG can easily take the measurements. Therefore, PPG has a good medical competency and due to its widespread availability, it has a lot of advantages. A PPG signal can sometimes be substituted or complemented by an Electrocardiography (ECG) signal as it can provide Heart Rate Variability (HRV) analysis. In this work, an in-depth analysis of classification of Cardiovascular Disease (CVD) is done with the help of Capnobase dataset. Initially, metaheuristic optimization algorithms are utilized as dimensionality reduction techniques and then the dimensionally reduced values are classified with the help of different classifiers for the classification of CVD. The results show that for the PPG normal cases, a high classification accuracy of 99.48% is obtained when Chi square Probability Density Function (PDF) optimized values are classified with Artificial Neural Networks (ANN) and a second highest classification accuracy of 98.96% is obtained when Chicken swarm optimized values are classified with Naïve Bayesian Classifier (NBC). Similarly when the PPG abnormal cases or PPG with CVD cases are concerned, a high classification accuracy of 99.48% is obtained when Chi square PDF optimized values are classified with Logistic Regression and a second highest classification accuracy of 98.96% is obtained when Chi square CDF optimized values are classified with Gaussian Support Vector Machine (SVM) and when Chicken swarm optimized values are classified with NBC.

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