k-NN-based classification of sleep apnea types using ECG

Timuş, Oğuz Han; Bolat, Emine Doğru

doi:10.3906/elk-1511-99

Cited by 18 publications

(7 citation statements)

References 27 publications

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“…Apart from SVM, Logistic Regression is widely used in health-related issues and performs better than other regression models for statistical data [18]. KNN also functions well in health-related classification and signal analysis [19].…”

Section: Related Workmentioning

confidence: 99%

A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

Basalamah¹,

Hasan²,

Bhowmik³

et al. 2023

Computer Systems Science and Engineering

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The recognition of pathological voice is considered a difficult task for speech analysis. Moreover, otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%-70%. To enhance detection accuracy and reduce processing speed of dysphonia detection, a novel approach is proposed in this paper. We have leveraged Linear Discriminant Analysis (LDA) to train multiple Machine Learning (ML) models for dysphonia detection. Several ML models are utilized like Support Vector Machine (SVM), Logistic Regression, and K-nearest neighbor (K-NN) to predict the voice pathologies based on features like Mel-Frequency Cepstral Coefficients (MFCC), Fundamental Frequency (F 0 ), Shimmer (%), Jitter (%), and Harmonic to Noise Ratio (HNR). The experiments were performed using Saarbrucken Voice Database (SVD) and a privately collected dataset. The K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML models. According to the experimental results, our proposed approach has a 70% increase in processing speed over Principal Component Analysis (PCA) and performs remarkably well with a recognition accuracy of 95.24% on the SVD dataset surpassing the previous best accuracy of 82.37%. In the case of the private dataset, our proposed method achieved an accuracy rate of 93.37%. It can be an effective non-invasive method to detect dysphonia.

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Section: Related Workmentioning

confidence: 99%

A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

Basalamah¹,

Hasan²,

Bhowmik³

et al. 2023

Computer Systems Science and Engineering

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show abstract

“…It is important to mention, too, that reduction of heart rate is also observed at nighttime while driving during late hours. The second procedure entails detection of heart rate variability, i.e., the time interval between pulses [73], [74]. The previous literature shows that the heart rate variability of an awake person manifests high-frequency signals, whereas sleep-deprived or drowsy persons show lowfrequency heart rate variability [75]- [77].…”

Section: E Electrocardiogram (Ecg)mentioning

confidence: 99%

Drowsiness, Fatigue and Poor Sleep’s Causes and Detection: A Comprehensive Study

2019

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Drowsiness/sleepiness is a serious issue that needs to be addressed for improvement in the safety of road driving. Past statistical data on road accidents has shown enormous increases in car crashes due to drowsy/sleepy feelings. This study comprehensively summarizes all aspects of the drowsy state and its effects during car driving: its symptoms, causes, preventive actions, car accident statistics, sleep stages, and the behavioral, physiological and neural activation changes occurring during wakefulness and in the drowsy state. It considers drivers' behavioral data and corresponding methodologies for its analysis, the biomedical signals of the human body (including neuronal signals in the forms of electrical and hemodynamic responses), and their use for drowsiness detection. All of the existing methodologies, their uses and pros and cons, are comprehensively summarized. A detailed survey of the data published by neuro-imaging methodology-, physiological signal-and behavioral methodology-based studies in addition to studies using electro-mechanical installed sensors are statistically and theoretically summarized. Additionally, the neuronal activity occurring during the drowsy and awake states are analyzed, and the important contributions of fNIRS, fMRI and EEG in this context are discussed in detail. Differing existing drowsinessdetection systems installed in popular car brands also are reviewed. Finally, the remaining challenges and future suggestions for drowsiness-detection systems are summarized as well.

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“…The ML classifier KNN is a simple algorithm with a high accuracy rate for image-based applications [26,27]. As the KNN classifier is well suited for pattern recognition and image analysis [28], it was used in the proposed approach for the image-based classification.…”

Section: Classification Algorithmsmentioning

confidence: 99%

DOP: Discover Objects and Paths, a model for automated navigation and selection in virtual environments

Raees¹,

Ullah²

2019

Turk J Elec Eng & Comp Sci

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Navigation and selection are the two interaction tasks often needed for the manipulation of an object in a synthetic world. An interface that supports automatic navigation and selection may increase the realism of a virtual reality (VR) system. Such an engrossing interface of a VR system is possible by incorporating machine learning (ML) into the realm of the virtual environment (VE). The use of intelligence in VR systems, however, is a milestone yet to be achieved to make seamless realism in a VE possible. To improve the believability of an intelligent virtual agent (IVA), this research work presents DOP ("Discover Objects and Paths"), a novel model for automated navigation and selection. The model, by intermingling ML with the VE, intends to augment the maturity of a virtual agent to the extent of human-level intelligence. Using ML classifiers, an IVA learns objects of interest along with the paths leading to the objects. To access any known object, the IVA then follows a mental map of the scene for self-directed navigation. After reaching a proper location in the designed VE, the required object is selected by using the ML algorithms. Extending ML to VR, the model was implemented in a case-study project called "Learn Objects on a Path" (LOOP). The application, having a maze-like VE, was evaluated in terms of accuracy and applicability by eight users. The results obtained showed that the model can be incorporated into a number of cross-modality applications.

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k-NN-based classification of sleep apnea types using ECG

Cited by 18 publications

References 27 publications

A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

Drowsiness, Fatigue and Poor Sleep’s Causes and Detection: A Comprehensive Study

DOP: Discover Objects and Paths, a model for automated navigation and selection in virtual environments

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