Comparison of SFS and mRMR for oximetry feature selection in obstructive sleep apnea detection

Mostafa, Sheikh Shanawaz; Morgado‐Dias, Fernando; Ravelo-García, Antonio G.

doi:10.1007/s00521-018-3455-8

“…Similar to the PhysioNet Apnea-ECG dataset, the dataset was divided into two parts, one for training and the other for verification. It is noteworthy that the original UCD dataset is continuously annotated based on the occurrence of events, which is different from the PhysioNet Apnea-ECG dataset, and we followed (Mostafa, Morgado-Dias & Ravelo-García, 2018; Xie & Minn, 2012) in converting them to 1-minute interval annotations. Table 5 shows the performance of our modified LeNet-5 and traditional machine learning methods in per-segment SA detection and per-recording classification.…”

Section: Resultsmentioning

confidence: 99%

“…According to the definition of apnea, an event should last at least 10s. However, an apnea event lasting 10s may be separated over two adjacent minutes, each having a smaller amount of apnea event time (Mostafa, Morgado-Dias & Ravelo-García, 2018; Xie & Minn, 2012). In the case of apnea or hypopnea lasting 5 or more consecutive seconds, the minute is considered to be an apnea.…”

Section: Methodsmentioning

confidence: 99%

Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network

Wang

¹

,

Lu

²

,

Shen

³

et al. 2019

PeerJ

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Sleep apnea (SA) is the most common respiratory sleep disorder, leading to some serious neurological and cardiovascular diseases if left untreated. The diagnosis of SA is traditionally made using Polysomnography (PSG). However, this method requires many electrodes and wires, as well as an expert to monitor the test. Several researchers have proposed instead using a single channel signal for SA diagnosis. Among these options, the ECG signal is one of the most physiologically relevant signals of SA occurrence, and one that can be easily recorded using a wearable device. However, existing ECG signal-based methods mainly use features (i.e. frequency domain, time domain, and other nonlinear features) acquired from ECG and its derived signals in order to construct the model. This requires researchers to have rich experience in ECG, which is not common. A convolutional neural network (CNN) is a kind of deep neural network that can automatically learn effective feature representation from training data and has been successfully applied in many fields. Meanwhile, most studies have not considered the impact of adjacent segments on SA detection. Therefore, in this study, we propose a modified LeNet-5 convolutional neural network with adjacent segments for SA detection. Our experimental results show that our proposed method is useful for SA detection, and achieves better or comparable results when compared with traditional machine learning methods.

show abstract

“…According to the definition of apnea, an event should last at least 10s. However, an apnea event lasting 10s may be separated over two adjacent minutes, each having a smaller amount of apnea event time (Mostafa et al 2018;Xie & Minn 2012). In the case of apnea or hypopnea lasting 5 or more consecutive seconds, the minute is considered to be an apnea.…”

Section: Ucd Datasetmentioning

confidence: 99%

“…Similar to the PhysioNet Apnea-ECG dataset, the dataset was divided into two parts, one for training and the other for verification. It is noteworthy that the original UCD dataset is continuously annotated based on the occurrence of events, which is different from the PhysioNet Apnea-ECG dataset, and we followed (Mostafa et al 2018;Xie & Minn 2012) in converting them to 1-minute interval annotations. Table 5 shows the performance of our modified LeNet-5 and traditional machine learning methods in per-segment SA detection and per-recording classification.…”

Section: Validation On Ucd Databasementioning

confidence: 99%

Peer Review #2 of "Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network (v0.1)"

Vaquerizo-Villar¹

2019

0

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Sleep apnea (SA) is the most common respiratory sleep disorder, leading to some serious neurological and cardiovascular diseases if left untreated. The diagnosis of SA is traditionally made using Polysomnography (PSG). However, this method requires many electrodes and wires, as well as an expert to monitor the test. Several researchers have proposed instead using a single channel signal for SA diagnosis. Among these options, the ECG signal is one of the most physiologically relevant signals of SA occurrence, and one that can be easily recorded using a wearable device. However, existing ECG signal-based methods mainly use features (i.e. frequency domain, time domain, and other nonlinear features) acquired from ECG and its derived signals in order to construct the model. This requires researchers to have rich experience in ECG, which is not common. A convolutional neural network (CNN) is a kind of deep neural network that can automatically learn effective feature representation from training data and has been successfully applied in many fields. Meanwhile, most studies have not considered the impact of adjacent segments on SA detection. Therefore, in this study, we propose a modified LeNet-5 convolutional neural network with adjacent segments for SA detection. Our experimental results show that our proposed method is useful for SA detection, and achieves better or comparable results when compared with traditional machine learning methods.

show abstract

“…Therefore, a large number of features needed to be sorted according to relevance to increase the accuracy. Various techniques have been employed to address this problem such as minimum Redundancy Maximum Relevance (mRMR), Sequential Forward Search (SFS) [25] and Genetic Algorithms (GA) [26]. However, these techniques are either slow or most of the time it does not guarantee that the best features were chosen.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Hyperparameter Optimization of Convolutional Neural Network for Obstructive Sleep Apnea Detection

Mostafa

¹

,

Mendonça

²

,

Ravelo-García

³

et al. 2020

Self Cite

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Obstructive sleep apnea (OSA) is a common sleep disorder characterized by interrupted breathing during sleep. Because of the cost, complexity, and accessibility issue related to polysomnography, the gold standard test for apnea detection, automation of the diagnostic test based on a simpler method is desired. Several signals can be used for apnea detection, such as airflow and electrocardiogram. However, the reduction of airflow normally leads to a decrease in the blood oxygen saturation level (SpO2). This signal is usually measured by a pulse oximeter, a sensor that is cheap, portable, and easy to assemble. Therefore, the SpO2 was chosen as the reference signal. Feature based classifiers with shallow neural networks have been developed to provide apnea detection using SpO2. However, two main issues arise, the need for feature creation and the selection of the more relevant features. Deep neural networks can solve these issues by employing featureless methods. Among multiple deep classifiers that have been developed, convolution neural networks (CNN) are gaining popularity. However, the selection of the CNN structure and hyperparameters are typically done by experts, where prior knowledge is essential. With these problems in mind, an algorithm for automatic structure selection and hyper parameterization of a one dimension CNN was developed to detect OSA events using only the SpO2 signal. Three different input sizes and databases were tested, and the best model achieved an average accuracy, sensitivity, and specificity of 94%, 92%, and 96%, respectively.

show abstract

Comparison of SFS and mRMR for oximetry feature selection in obstructive sleep apnea detection

Cited by 15 publications

References 41 publications

Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network

Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network

Peer Review #2 of "Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network (v0.1)"

Multi-Objective Hyperparameter Optimization of Convolutional Neural Network for Obstructive Sleep Apnea Detection

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