Figure 1 - uploaded by Marimuthu Palaniswami
Content may be subject to copyright.
![Flow chart of sleep apnea detection and classification. The first two stages – Event Detection and Apnea-Hypopnea Classification has been reported in literature [11]. The third stage of Apnea Type Detection is the focus of this work.](profile/Marimuthu-Palaniswami/publication/51905969/figure/fig1/AS:277278140452865@1443119668972/Flow-chart-of-sleep-apnea-detection-and-classification-The-first-two-stages-Event.png)
Flow chart of sleep apnea detection and classification. The first two stages – Event Detection and Apnea-Hypopnea Classification has been reported in literature [11]. The third stage of Apnea Type Detection is the focus of this work.
Source publication
Obstructive sleep apnea (OSA) causes a pause in airflow with reduced breathing effort. In contrast, central sleep apnea (CSA) event is not accompanied with breathing effort. The aim of this study is to differentiate CSA and OSA events using wavelet packet analysis and support vector machines of ECG signals over 5 s period.
Eight level wavelet packe...
Similar publications
Bruxism is a medical condition in which a person’s teeth grind or clench. If you have bruxism, you may have unconsciously clenched or ground your teeth while awake (awake bruxism) or while sleeping (sleep bruxism). Polysomnography, Overnight sleep studies at a sleep clinic are the most definitive approach to identify sleep bruxism. Polysomnography...
Citations
... However, based on statistics related to sleep apnea, about 93% of middle-aged females and 82% of middle-aged males with moderate to severe sleep apnea symptoms have not yet been diagnosed (Young et al., 1997). Sleep apnea is primarily categorized into three distinct types: Obstructive Sleep Apnea (OSAS), which results from dysfunction in the upper airway; Central Sleep Apnea (CSAS), which arises due to neurological abnormalities where the brain fails to generate or convey signals to the respiratory muscles; and Sleep Apnea Hypoventilation Syndrome (SAHS), attributed to diminished air circulation (Gubbi et al., 2012). SAS can occur multiple times during the night and its physiological symptoms include snoring, sleep gasping, waking up with a dry mouth, and poor sleep quality, which can lead to poor concentration, insomnia, cognitive decline, memory loss, and depression (Vanek et al., 2020). ...
Introduction
Sleep apnoea syndrome (SAS) is a serious sleep disorder and early detection of sleep apnoea not only reduces treatment costs but also saves lives. Conventional polysomnography (PSG) is widely regarded as the gold standard diagnostic tool for sleep apnoea. However, this method is expensive, time-consuming and inherently disruptive to sleep. Recent studies have pointed out that ECG analysis is a simple and effective diagnostic method for sleep apnea, which can effectively provide physicians with an aid to diagnosis and reduce patients’ suffering.
Methods
To this end, in this paper proposes a LightGBM hybrid model based on ECG signals for efficient detection of sleep apnea. Firstly, the improved Isolated Forest algorithm is introduced to remove abnormal data and solve the data sample imbalance problem. Secondly, the parameters of LightGBM algorithm are optimised by the improved TPE (Tree-structured Parzen Estimator) algorithm to determine the best parameter configuration of the model. Finally, the fusion model TPE_OptGBM is used to detect sleep apnoea. In the experimental phase, we validated the model based on the sleep apnoea ECG database provided by Phillips-University of Marburg, Germany.
Results
The experimental results show that the model proposed in this paper achieves an accuracy of 95.08%, a precision of 94.80%, a recall of 97.51%, and an F1 value of 96.14%.
Discussion
All of these evaluation indicators are better than the current mainstream models, which is expected to assist the doctor’s diagnostic process and provide a better medical experience for patients.
... Sleep was found to be correlated with electrical cardiac activity and breathing alterations. ECG signal was first reported in Moody et al. (1985) and many other studies such as (Ameh Joseph et al. 2022;Olubusoye et al. 2021;Rehmat et al. 2022;Ng et al. 2008;Vimala et al. 2019;Xie and Minn 2012;Yüzer et al. 2020;Almazaydeh et al. 2012;Zarei et al. 2022;Gubbi et al. 2012;Zhao et al. 2021;Sugi et al. 2009; The ECGid database 2011; Shen et al. 2021;Guijarro-Berdiñas et al. 2012;Almuhammadi et al. 2015;Chen et al. 2020;Nguyen et al. 2014;Alhamad et al. 2021). The detecting process employs a variety of ML approaches to OSA (The ECG-id database 2011). ...
... Te authors reported a maximum accuracy of 83.77%. In [123], the authors used an eight-level wavelet packet analysis method on a short-duration (5 s) ECG signal to diferentiate between central sleep apnea (CSA) and obstructive sleep apnea (OSA). CSA occurs when the brain is unable to send proper signals to the muscles associated with breathing. ...
The joint time-frequency analysis method represents a signal in both time and frequency. Thus, it provides more information compared to other one-dimensional methods. Several researchers recently used time-frequency methods such as the wavelet transform, short-time Fourier transform, empirical mode decomposition and reported impressive results in various electrophysiological studies. The current review provides comprehensive knowledge about different time-frequency methods and their applications in various ECG-based analyses. Typical applications include ECG signal denoising, arrhythmia detection, sleep apnea detection, biometric identification, emotion detection, and driver drowsiness detection. The paper also discusses the limitations of these methods. The review will form a reference for future researchers willing to conduct research in the same field.
... As a classical signal processing algorithm, the wavelet packet transform is widely used in many areas of pattern recognition, such as disease diagnosis [17][18][19], industrial equipment fault diagnosis [20][21][22], voice recognition [23][24][25], and ECG signal classification [26,27], etc. ...
Frequency modulation continuous wave (FMCW) radio fuze is widely used in military equipment, due to its excellent range and anti-jamming ability. However, the widespread use of radio fuze jammers on modern battlefields poses a serious threat to fuzes. In this study, a classification method of targeting and sweeping frequency jamming signals of FMCW radio fuze based on wavelet packet transform features is proposed, which improves the anti-jamming ability of fuze. The wavelet packet transform of the output signal of the radio fuze detector is used to form a feature vector, which is fed into a support vector machine for targeting and jamming signal classification. The experimental results of the measured data show that the proposed method can achieve a high accuracy rate of classification and identification of FMCW radio fuze targets and frequency sweeping jamming signals. The highest recognition accuracy reached is 98.81% ± 0.0037. The lowest false alarm probability is 0.57% ± 0.0043, which indicates its potential application values in the near future.
... В останні роки багато досліджень було присвячено розробці комп'ютеризованих методів виявлення епізодів апное сну [4][5][6][7]. Такі підходи можуть базуватися на спектральних особливостях досліджуваних сигналів [8][9][10], вейвлет-аналізі [11], характеристиках, заснованих на статистичних моментах [8], аналізі головних компонент [12]. Аналіз полісомнограм передбачає реєстрацію великої кількості біомедичних сигналів під час сну, включаючи електричну активність мозку та серця, рівень кисню та вуглекислого газу в крові, реєстрацію рухів очей та кінцівок, скорочення м'язів, частоту серцевих скорочень, частоту дихання, виникнення хропіння, потоки повітря крізь ніс і рот [3]. ...
... Дослідники застосовують різні методи виділення ознак у часовій та частотній області, а саме перетворення Фур'є, вейвлет аналіз, розпізнавання форми хвиль біомедичних сигналів [6,8,11]. Замість використання 12 фізіологічних сигналів полісомнограми з метою зменшення обчислювальних витрат для виявлення апное сну також використовується аналіз лише одного з сигналів. ...
... Замість використання 12 фізіологічних сигналів полісомнограми з метою зменшення обчислювальних витрат для виявлення апное сну також використовується аналіз лише одного з сигналів. Дослідження довели можливість виявлення апное сну за допомогою ознак, отриманих на основі ЕКГ сигналу, а саме показників варіабельності ритму серця (ВРС) [7][8][9][10][11][12][13][14]. Також ряд наукових робіт присвячено дослідженню можливості виявлення апное на основі аналізу ЕЕГ сигналів [15][16][17][18]. ...
The article is devoted to the application of machine learning methods for computerized detection of sleep apnea episodes based on the analysis of single-channel signals of the electrocardiogram (ECG) and electroencephalogram (EEG). To study the possibilities of machine learning to detect apnea based on ECG and EEG analysis, we used Apnea-ECG database and MIT-BIH polysomnographic database from PhysioNet, which contain annotations to each minute of records indicating the presence or absence of apnea/hypopnea at the current time. In order to apply machine learning methods to the problem of automated detection of sleep apnea/hypopnea episodes in ECG and EEG signals, long-term polysomnograms available in MIT-BIH polysomnographic database were segmented according to annotations into shorter sections lasting 30 seconds each. The study used 267 segments lasting 30 seconds for the class "norm", 258 segments for the class "apnea" and 273 segments for the class "hypopnea", a total of 798 simultaneous ECG and EEG recordings. The aim of this work is to identify and compare informative signs of sleep apnea episodes in terms of heart rate variability (HRV) and brain electrical activity, as well as the choice of classification methods that provide the highest accuracy for this task. Features of cardiorhythmograms in time and frequency domains, spectral-temporal and wavelet characteristics, as well as parameters of EEG signals based on energy ratio of EEG rhythms, Hearst index, Higuchi fractal dimension and sample entropy for EEG signals are considered. Using different sets of features, the accuracy of classifiers based on decision trees, discriminant analysis, support vector machines, k-nearest neighbor method, and ensemble training was determined. Based on this, combination of features and classifiers is proposed, which provides the highest accuracy of recognition of sleep apnea episodes according to single-channel ECG and EEG signals, taken separately and in the case of a combination of their features. The best results of classification of signals "norm", "apnea" and "hypopnea" were obtained for the model trained using weighted method k nearest neighbors with 25 features of HRV: the total percentage of correctly identified cases for three classes was 99.9% (797 correctly identified cases of 798). By reducing the number of HRV parameters to 9, the best machine learning result was achieved using the bagging ensemble algorithm with 30 decision trees: the total percentage of correctly identified cases for all three classes was 99.4% (793 correctly identified cases from 798: for "norm" - 265 cases from 267, for "apnea" - 257 cases from 258, for "hypopnea" - 271 cases from 273). The use of EEG parameters as features for apnea/hypopnea recognition showed worse results compared to HRV parameters. In this case, the best result of machine learning was achieved using support vector machines with quadratic kernel function: the total percentage of correctly identified cases for three classes was 91.9% and the signals corresponding to norm were most badly recognized (27 cases were classified as hypopnea, and in 9 cases - as sleep apnea). The combination of HRV and EEG parameters gave the best accuracy of 99.1%, but the results are comparable to using only HRV parameters. The obtained results indicate that HRV parameters allow recognizing sleep apnea and hypopnea with higher accuracy than EEG parameters, but EEG signal undoubtedly reflects signs of sleep apnea/hypopnea and also can be used for apnea recognition.
... ECG and EEG signals are widely used in the detection of sleep apnea [4,5,6,7]. Electrical heart activity along with respiratory changes was found to correlate with sleep apnea as it was first reported in [8], [9]. Different machine learning techniques are used in the detection of sleep apnea [10], [11]. ...
Sleep apnea is a sleep disorder that can cause serious health problems. An Artificial Neural Network classifier to detect sleep apnea has been presented in this paper by utilizing the ECG signals. Moreover, the discrete wavelet transform is used to decompose the ECG signal and use the first decomposition for feature extraction; the extracted features were used to train the Artificial Neural Network for pattern detection using MATLAB tools. Also, the data-sets used contains both Apnea pat1ients and healthy volunteers' ECG signals. The results achieve 92.3% accuracy in the testing records.
... The regular wavelet decomposition method may not always yield the best results in recognizing patterns [95]. Therefore, a WPT decomposition can be used. ...
Drowsiness is not only a core challenge to safe driving in traditional driving conditions but also a serious obstacle for the wide acceptance of added services of self-driving cars (because drowsiness is, in fact, one of the most representative early-stage symptoms of self-driving carsick-ness). In view of the importance of detecting drivers' drowsiness, this paper reviews the algorithms of electroencephalogram (EEG)-based drivers' drowsiness detection (DDD). To facilitate the review, the EEG-based DDD approaches are organized into a tree structure taxonomy, having two main categories, namely "detection only (open-loop)" and "management (closed-loop)", both aimed at designing better DDD systems that ensure early detection, reliability and practical utility. To achieve this goal, we addressed seven questions, the answers of which helped in developing an EEG-based DDD system that is superior to the existing ones. A basic assumption in this review article is that although driver drowsiness and carsickness-induced drowsiness are caused by different factors, the brain network that regulates drowsiness is the same.
... Using WPT for feature extraction is common practice in EEG signals. For example, Gubbi et al. used wavelet packet to classify sleep apnea types [9]. Huptych et al. used WPT to classify normal (N) and ventricular (V) beats classification [10]. ...
... From that indices set, selected nodes can be found. The selected nodes are-y(1, 1), y(1, 2), y(1, 3), y(1, 5), y (1,9), y(1, 10), y (1,12), y(1, 15), y(1, 16), y(2, 1), y(2, 4), y(2, 5), y(2, 7), y(2, 8), y(2, 10), y(2, 11), y(2, 16), y(3, 2), y(3, 6), y (3,8), y (3,12), y (3,13), y (3,14), y (3,16), y(4, 2), y(4, 3), y(4, 6), y(4, 7), y (4,11), y(4, 12), y (4,14), y(4, 15), y(5, 2), y(5, 3), y(5, 6), y(5, 7), y (5,9), y(5, 10), y(5, 13), y (5,14), y(5, 15), y(5, 16), y(6, 1), y(6, 3), y(6, 4), y(6, 6), y (6,8), y(6, 13), y (6,15). ...
... From that indices set, selected nodes can be found. The selected nodes are-y(1, 1), y(1, 2), y(1, 3), y(1, 5), y (1,9), y(1, 10), y (1,12), y(1, 15), y(1, 16), y(2, 1), y(2, 4), y(2, 5), y(2, 7), y(2, 8), y(2, 10), y(2, 11), y(2, 16), y(3, 2), y(3, 6), y (3,8), y (3,12), y (3,13), y (3,14), y (3,16), y(4, 2), y(4, 3), y(4, 6), y(4, 7), y (4,11), y(4, 12), y (4,14), y(4, 15), y(5, 2), y(5, 3), y(5, 6), y(5, 7), y (5,9), y(5, 10), y(5, 13), y (5,14), y(5, 15), y(5, 16), y(6, 1), y(6, 3), y(6, 4), y(6, 6), y (6,8), y(6, 13), y (6,15). ...
Activity recognition from human action data is quite a challenging task in the biomedical data science community. The main challenge in dealing with human activity recognition (HAR) datasets is their high cardinality. Therefore, reducing cardinality is a cardinal area of research in the HAR field. In this research, reducing the data dimensionality by utilizing future selection methods has been used. This research work has extracted features using wavelet packet transform (WPT) and the cardinality of the feature set has been reduced by using the Genetic Algorithm (GA) technique. The selected features also have been ranked according to their importance based on their SHAP values. In the venture, an interesting inspection has been found. That is in HAR datasets, signal values lay into lower frequency regions mostly. The highest accuracy and f1-score which have been got are 94.74%, 94.73%, and 89.98%, 89.67% for the feature extracted and feature selected dataset respectively.
... Some other approaches aimed to reduce the complexity of the PSG system, making use of ECG signals to extract vital parameters as HR and BR [47] and then differentiate between obstructive and central apnea events [48,49]. Some others also integrated accelerometer signals besides ECG to seek better accuracy [50,51]. ...
The present paper proposes the design of a sleep monitoring platform. It consists of an entire sleep monitoring system based on a smart glove sensor called UpNEA worn during the night for signals acquisition, a mobile application, and a remote server called AeneA for cloud computing. UpNEA acquires a 3-axis accelerometer signal, a photoplethysmography (PPG), and a peripheral oxygen saturation (SpO2) signal from the index finger. Overnight recordings are sent from the hardware to a mobile application and then transferred to AeneA. After cloud computing, the results are shown in a web application, accessible for the user and the clinician. The AeneA sleep monitoring activity performs different tasks: sleep stages classification and oxygen desaturation assessment; heart rate and respiration rate estimation; tachycardia, bradycardia, atrial fibrillation, and premature ventricular contraction detection; and apnea and hypopnea identification and classification. The PPG breathing rate estimation algorithm showed an absolute median error of 0.5 breaths per minute for the 32 s window and 0.2 for the 64 s window. The apnea and hypopnea detection algorithm showed an accuracy (Acc) of 75.1%, by windowing the PPG in one-minute segments. The classification task revealed 92.6% Acc in separating central from obstructive apnea, 83.7% in separating central apnea from central hypopnea and 82.7% in separating obstructive apnea from obstructive hypopnea. The novelty of the integrated algorithms and the top-notch cloud computing products deployed, encourage the production of the proposed solution for home sleep monitoring.
... In addition, this detection process can easily cause discomfort to patients and may also lead to subjective errors [8]. Therefore, many researchers are committed to obtaining SA information from a single or a small amount of physiological signals for simple and effective automatic SA classification, such as photoplethysmography (PPG) [9], SpO2 [10,11], ECG [12,13], thoracoabdominal signal [14], EMG, ECG and EEG [15]. ...
Sleep apnea is a kind of sleep disorder with a high prevalence rate. It is manifested as the abnormal stop of breathing during sleep and is highly dangerous to human health. The purpose of this research is to find a simple, and effective feature extraction method that can able to distinguish obstructive apnea events, central apnea events, and normal breathing events. Unlike conventional methods, the method illustrated in this study used the Infinite Impulse Response Butterworth Band pass filter to divide the Electroencephalogram (EEG) signal into 5, 7, 9 or 11 frequency sub-bands and then used the Welch method to extract the power features of these frequency sub-band signals, which were subsequently used as classifier input. Random forest, K-nearest neighbors and bagging classifiers were investigated.
The results showed that in several different frequency sub-band division methods of EEG signals, the features extracted from the EEG signal that was divided into 11 frequency sub-bands were more conducive to the classification of sleep apnea events. The random forest classifier achieved the highest average accuracy, macro F1 and kappa coefficient in three types of events, which were 90.43%, 90.38% and 0.88, respectively. Compared with existing methods, the method used in the present study has higher classification performance.
