SCNN: Scalogram-based convolutional neural network to detect obstructive sleep apnea using single-lead electrocardiogram signals

Mashrur, Fazla Rabbi; Islam, Md. Saiful; Saha, Dabasish Kumar; Islam, S. M. Riazul; Moni, Mohammad Ali

doi:10.1016/j.compbiomed.2021.104532

Cited by 60 publications

(31 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The type of classification system used in a BCI system is mostly determined by the application's nature and location. With the recent application of deep learning (DL) in different domain (Mashrur et al, 2019 , 2021a ; Nazi et al, 2021 ), Teo et al ( 2017 ) showed the subjects 3D virtual jewelry objects, asked to rate them on a Likert scale, and then categorized EEG signals using deep learning. Again, Aldayel et al ( 2020 ) emphasized the need of spectral valence features to improve prediction accuracy and the merging of classifiers using deep learning to extract features.…”

Section: Introductionmentioning

confidence: 99%

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework

Mashrur

Rahman

Miya

et al. 2022

Front. Hum. Neurosci.

Self Cite

View full text Add to dashboard Cite

Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.

show abstract

Section: Introductionmentioning

confidence: 99%

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework

Mashrur

Rahman

Miya

et al. 2022

Front. Hum. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is divided into a method of diagnosing apnea using electrical biosignals obtained through PSG and a method of diagnosing apnea using ECG obtained via PSG. A scalogram-based convolutional neural network was introduced to detect sleep apnea using single-lead ECG signals [21]. In the work by Shen et al [13], the detection of sleep apnea employs a multiscale, dilated-attention, one-dimensional convolutional neural network and a weighted-loss time-dependent classification model.…”

Section: Related Researchmentioning

confidence: 99%

Classification of Respiratory States Using Spectrogram with Convolutional Neural Network

Park

Lee

2022

Applied Sciences

View full text Add to dashboard Cite

This paper proposes an approach to the classification of respiration states based on a neural network model by visualizing respiratory signals using a spectrogram. The analysis and processing of human biosignals are still considered some of the most crucial and fundamental research areas in both signal processing and medical applications. Recently, learning-based algorithms in signal and image processing for medical applications have shown significant improvement from both quantitative and qualitative perspectives. Human respiration is still considered an important factor for diagnosis, and it plays a key role in preventing fatal diseases in practice. This paper chiefly deals with a contactless-based approach for the acquisition of respiration data using an ultra-wideband (UWB) radar sensor because it is simple and easy for use in an experimental setup and shows high accuracy in distance estimation. This paper proposes the classification of respiratory states by using a feature visualization scheme, a spectrogram, and a neural network model. The proposed method shows competitive and promising results in the classification of respiratory states. The experimental results also show that the method provides better accuracy (precision: 0.86 and specificity: 0.90) than conventional methods that use expensive equipment for respiration measurement.

show abstract

“…In their work, they've applied a windowing strategy, with window sizes of 500, 1000, 1500, 2000 and 2500 for validation of their model, which achieved 93.77% accuracy for window size of 500 on the PhysioNet Apnea-ECG database [12]. Mashrur et al [28] have proposed a novel Scalogram-based CNN to detect OSA using ECG signals. In their work, they've obtained hybrid scalograms from the ECG signals using continuous wavelet transform (CWT) and empirical mode decomposition (EMD).…”

Section: Related Workmentioning

confidence: 99%

Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study

Mukherjee

Dhar

Schwenker

et al. 2021

Sensors

View full text Add to dashboard Cite

Sleep Apnea is a breathing disorder occurring during sleep. Older people suffer most from this disease. In-time diagnosis of apnea is needed which can be observed by the application of a proper health monitoring system. In this work, we focus on Obstructive Sleep Apnea (OSA) detection from the Electrocardiogram (ECG) signals obtained through the body sensors. Our work mainly consists of an experimental study of different ensemble techniques applied on three deep learning models—two Convolutional Neural Network (CNN) based models, and a combination of CNN and Long Short-Term Memory (LSTM) models, which were previously proposed in the OSA detection domain. We have chosen four ensemble techniques—majority voting, sum rule and Choquet integral based fuzzy fusion and trainable ensemble using Multi-Layer Perceptron (MLP) for our case study. All the experiments are conducted on the benchmark PhysioNet Apnea-ECG Database. Finally, we have achieved highest OSA detection accuracy of 85.58% using the MLP based ensemble approach. Our best result is also able to surpass many of state-of-the-art methods.

show abstract

SCNN: Scalogram-based convolutional neural network to detect obstructive sleep apnea using single-lead electrocardiogram signals

Cited by 60 publications

References 35 publications

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework

Classification of Respiratory States Using Spectrogram with Convolutional Neural Network

Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study

Contact Info

Product

Resources

About