Performance Comparison Of Ann Classifiers For Sleep Apnea Detection Based On Ecg Signal Analysis Using Hilbert Transform

Bali, Jyoti; Nandi, Ashis Kumar; Hiremath, P. S.

doi:10.24297/ijct.v17i2.7616

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…The Pan-Tompkins (PT) algorithm was utilized to process the ECG signals and detect the QRS complexes [25,26]. In this algorithm, to reduce the influence of electromyogram (EMG) noise, powerline noise, baseline wander, and T-wave interference, a Butterworth bandpass filter was applied with a passband frequency of 5–15 Hz.…”

Section: Methodsmentioning

confidence: 99%

Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices

Ross

Sarkar

Rodenburg

et al. 2019

Sensors

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Simulation-based training has been proven to be a highly effective pedagogical strategy. However, misalignment between the participant’s level of expertise and the difficulty of the simulation has been shown to have significant negative impact on learning outcomes. To ensure that learning outcomes are achieved, we propose a novel framework for adaptive simulation with the goal of identifying the level of expertise of the learner, and dynamically modulating the simulation complexity to match the learner’s capability. To facilitate the development of this framework, we investigate the classification of expertise using biological signals monitored through wearable sensors. Trauma simulations were developed in which electrocardiogram (ECG) and galvanic skin response (GSR) signals of both novice and expert trauma responders were collected. These signals were then utilized to classify the responders’ expertise, successive to feature extraction and selection, using a number of machine learning methods. The results show the feasibility of utilizing these bio-signals for multimodal expertise classification to be used in adaptive simulation applications.

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Section: Methodsmentioning

confidence: 99%

Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices

Ross

Sarkar

Rodenburg

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…The Pan-Tompkins algorithm was utilized to filter the raw ECG signals [60], [61]. The Pan-Tompkins algorithm first uses a Butterworth bandpass filter with a passband frequency of 5-15 Hz to reduce the EMG noise, powerline noise, baseline wander and T-wave interference.…”

Section: A Pre-processingmentioning

confidence: 99%

“…In order to extract features from the ECG signals, the Pan-Tompkins algorithm was used to detect the QRS complexes of the ECG waves [60], [61]. This is accomplished by differentiating the filtered ECG signal using a 5-point derivative transfer function in order to gain the QRS slope information.…”

Section: E Manual Feature Extraction and Selectionmentioning

confidence: 99%

Unsupervised Multi-Modal Representation Learning for Affective Computing with Multi-Corpus Wearable Data

Ross¹,

Hungler²,

Etemad³

2020

Preprint

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With recent developments in smart technologies, there has been a growing focus on the use of artificial intelligence and machine learning for affective computing to further enhance the user experience through emotion recognition. Typically, machine learning models used for affective computing are trained using manually extracted features from biological signals. Such features may not generalize well for large datasets and may be sub-optimal in capturing the information from the raw input data. One approach to address this issue is to use fully supervised deep learning methods to learn latent representations of the biosignals. However, this method requires human supervision to label the data, which may be unavailable or difficult to obtain. In this work we propose an unsupervised framework reduce the reliance on human supervision. The proposed framework utilizes two stacked convolutional autoencoders to learn latent representations from wearable electrocardiogram (ECG) and electrodermal activity (EDA) signals. These representations are utilized within a random forest model for binary arousal classification. This approach reduces human supervision and enables the aggregation of datasets allowing for higher generalizability. To validate this framework, an aggregated dataset comprised of the AMIGOS, ASCERTAIN, CLEAS, and MAHNOB-HCI datasets is created. The results of our proposed method are compared with using convolutional neural networks, as well as methods that employ manual extraction of hand-crafted features. The methodology used for fusing the two modalities is also investigated. Lastly, we show that our method outperforms current state-ofthe-art results that have performed arousal detection on the same datasets using ECG and EDA biosignals. The results show the wide-spread applicability for stacked convolutional autoencoders to be used with machine learning for affective computing. I. INTRODUCTIONS MART technologies are quickly becoming ubiquitous in our everyday lives. These technologies aim to understand, analyze, and interact with users seamlessly, providing a user-centered experience. Affective computing is one area of research aiming to create smart technologies that can better understand and ultimately react to their user. Affective computing is the notion of detecting, modeling, and reacting to the user's affective states by a computer [1]. The integration of affective computing into devices can make users perceive the computers as more intelligent, effectively making the smart technology "smarter" [2].

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Efficient ANN Algorithms for Sleep Apnea Detection Using Transform Methods

Bali

Nandi

Hiremath

2019

Algorithms for Intelligent Systems

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Performance Comparison Of Ann Classifiers For Sleep Apnea Detection Based On Ecg Signal Analysis Using Hilbert Transform

Cited by 8 publications

References 21 publications

Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices

Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices

Unsupervised Multi-Modal Representation Learning for Affective Computing with Multi-Corpus Wearable Data

Efficient ANN Algorithms for Sleep Apnea Detection Using Transform Methods

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