A Novel Heart Disease Classification Algorithm Based on Fourier Transform and Persistent Homology

Ni, Yin; Sun, Fupeng; Luo, Yihao; Xiang, Zhengrui; Sun, Hongwei

doi:10.1109/eebda53927.2022.9744978

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…In this study, the single channel basis with the most significant correlation features screened by PCA in the previous section was selected, and wavelet decomposition was performed on this channel. The cerebral blood oxygenation signal is a nonstationary signal, which cannot be analyzed by the conventional Fourier Transform [ 35 ]. The Short Time Fourier Transform [ 36 ], on the other hand, has a single resolution due to its fixed window function.…”

Section: Methodsmentioning

confidence: 99%

Study of Motion Sickness Model Based on fNIRS Multiband Features during Car Rides

2023

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Motion sickness is a common physiological discomfort phenomenon during car rides. In this paper, the functional near-infrared spectroscopy (fNIRS) technique was used in real-world vehicle testing. The fNIRS technique was utilized to model the relationship between changes in blood oxygenation levels in the prefrontal cortex of passengers and motion sickness symptoms under different motion conditions. To enhance the accuracy of motion sickness classification, the study utilized principal component analysis (PCA) to extract the most significant features from the test data. Wavelet decomposition was used to extract the power spectrum entropy (PSE) features of five frequency bands highly related to motion sickness. The correlation between motion sickness and cerebral blood oxygen levels was modeled by a 6-point scale calibration for the subjective evaluation of the degree of passenger motion sickness. A support vector machine (SVM) was used to build a motion sickness classification model, achieving an accuracy of 87.3% with the 78 sets of data. However, individual analysis of the 13 subjects showed a varying range of accuracy from 50% to 100%, suggesting the presence of individual differences in the relationship between cerebral blood oxygen levels and motion sickness symptoms. Thus, the results demonstrated that the magnitude of motion sickness during the ride was closely related to the change in the PSE of the five frequency bands of cerebral prefrontal blood oxygen, but further studies are needed to investigate individual variability.

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Section: Methodsmentioning

confidence: 99%

Study of Motion Sickness Model Based on fNIRS Multiband Features during Car Rides

2023

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“…These extracted features often capture the fundamental characteristics and intricate structures inherent in the point cloud, making them directly applicable to machine learning tasks. Therefore, TDA methods are widely used in various application fields, including computer vision [10] , data analysis [11] , medical imaging [12] , and robotics [13,14] . Inspired by these significant works, we present a novel TPA method that applies persistent homology to point cloud processing.…”

Section: Applications Of Persistent Homologymentioning

confidence: 99%

An efficient 3D point cloud classification approach via persistent homology

Zhou,

Pan,

Zhang

et al. 2024

Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024)

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“…Additionally, the Mapper algorithm has been applied to predict the presence and severity of heart disease [2]. Computer-aided ECG rhythm classification methods which utilize TDA include neural networks with topological-based features [16,53], fractal dimension in tandem with neural networks [55], mapping ECG signals to a higher dimensional space prior to computing topological features [26,27,34,36,41], and utilizing a sliding window and Fast Fourier Transform to process the ECG signal prior to computing topological features [43]. These approaches construct topological predictor variables utilizing information directly derived from the birth and death radii statistics along with extra information such as heart rate, fractal dimension statistics, and persistent entropy.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram arrhythmia detection with novel signal processing and persistent homology-derived predictors

Dlugas

2024

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Many approaches to computer-aided electrocardiogram (ECG) arrhythmia detection have been performed, several of which combine persistent homology and machine learning. We present a novel ECG signal processing pipeline and method of constructing predictor variables for use in statistical models. Specifically, we introduce an isoelectric baseline to yield non-trivial topological features corresponding to the P, Q, S, and T-waves (if they exist) and utilize the N-most persistent 1-dimensional homological features and their corresponding area-minimal cycle representatives to construct predictor variables derived from the persistent homology of the ECG signal for some choice of N. The binary classification of (1) Atrial Fibrillation vs. Non-Atrial Fibrillation, (2) Arrhythmia vs. Normal Sinus Rhythm, and (3) Arrhythmias with Morphological Changes vs. Sinus Rhythm with Bradycardia and Tachycardia Treated as Non-Arrhythmia was performed using Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Naive Bayes, Random Forest, Gradient Boosted Decision Tree, K-Nearest Neighbors, and Support Vector Machine with a linear, radial, and polynomial kernel Models with stratified 5-fold cross validation. The Gradient Boosted Decision Tree Model attained the best results with a mean F1-score and mean Accuracy of ( 0.967 , 0.946 ), ( 0.839 , 0.946 ), and ( 0.943 , 0.921 ) across the five folds for binary classifications of (1), (2), and (3), respectively.

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A Novel Heart Disease Classification Algorithm Based on Fourier Transform and Persistent Homology

Cited by 8 publications

References 26 publications

Study of Motion Sickness Model Based on fNIRS Multiband Features during Car Rides

Study of Motion Sickness Model Based on fNIRS Multiband Features during Car Rides

An efficient 3D point cloud classification approach via persistent homology

Electrocardiogram arrhythmia detection with novel signal processing and persistent homology-derived predictors

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