Empirical mode decomposition (EMD) and Bivariate are adopted to evaluate the output of EEG signals. EMD performance improved significantly when the number of samples decreased [89]. The segmental error analyzed in event-related potential (ERP) indicated the occurrence of apnea. The delta energy associated with the immune system and the regulation of homeostasis is due to the depletion of oxygen in the event of apnea. Using the Hilbert Huang Transform, there is a wave of force in low waves when apnea occurs. This can be linked to delta energy related to the immune system and the regulation of homeostasis. The EMD and Bivariate methods were compared to reflect the key features associated with apnea for analytical purposes [31]. In a study on nonlinear feature extraction methods, three parameters are extracted: (i) the length of the longest diagonal, (ii) recurrent points needed to construct the diagonal lines, and (iii) percentage of recurrent lines to shape the vertical lines [8]. Multidomain feature extraction utilizes a combination of feature extraction methods and signal processing algorithms. Comparing the feature extraction methods and the features extracted, RR series signal and HRV data are extracted from PSG signal and ECG waveforms, Oxygen-related features from SpO2 and SaO2 signals and the rest of the features are extracted using the abovementioned feature extraction strategies.
digital signal processing a computer-based approach 4th edition pdf 599
From the literature analysis, it is noted that the so-called CAD approaches are more focused on two significant aspects. One trend is aimed at reducing the time required in monitoring multiple parameters as in the case of PSG studies, which is performed overnight in an attended hospital environment [5]. On the other hand, the emergence of sophisticated signal processing algorithms and predictive classifiers has paved way for a break-through for automatic detection of OSA. Although reducing the number of signals to be monitored has a significance in terms of reduced costs, increased patient comfort, and reduced waiting lists, yet, manual analysis of still images as in the case of PSG is more complex and time consuming. For this purpose, automation of the screening process is more preferred.
All new technologies emerging today have their pros and cons. When compared with sleep apnea monitors, overnight acquisition of signals is a tedious task and also considered as a challenging one. The major advantage of using nocturnal polysomnography is it helps in the monitoring of the heart, lungs, brain, breathing pattern, leg, and arm movements. The disadvantage is that the patient gets hooked up with wires and sensors, which would cause lot of discomfort to the individuals, and most of the patients have to repeat the test again on account of improper sleep. In the feature extraction stage, RR interval signals are extracted mostly from ECG recordings. Not all the researchers preferred PSG recordings; a variety of signals are also used for the sleep apnea detection. When blood oxygen saturation or oxygen saturation signals are considered for the analysis, it yields an effective outcome, while the EEG recordings depicted artefacts; hence, a separate preprocessing unit needs to be installed for better acquisition of signals. During the recording of EEG, the signal gets disrupted if the patient suffered suffocation, muscle pains, or limb twitches and hence requires a surrogate technique to monitor each parameter separately [69, 122].
Deep learning is part of a wider family of artificial neural networks (ANN)-based machine learning approaches with representation learning. Deep learning provides a computational architecture by combining several processing layers, such as input, hidden, and output layers, to learn from data [41]. The main advantage of deep learning over traditional machine learning methods is its better performance in several cases, particularly learning from large datasets [105, 129]. Figure 9 shows a general performance of deep learning over machine learning considering the increasing amount of data. However, it may vary depending on the data characteristics and experimental set up.
This study is aimed at covering the diagnostic approaches in the field of OSA, which are to some extent supported by automated computer-assisted diagnostic procedures. With this objective, the paper addresses the following in detail:(i)Physiological signals associated with OSA detection(ii)Signal processing algorithms used for extraction of features that aid in apneic event detection(iii)Hardware devices that source the biological signals used for OSA detection(iv)Classifiers used for discrimination of normal and abnormal signals
The fundamental physiological signals that are employed in various approaches, for the screening of OSA, are discussed in the following session. Based on the inference from the survey, there have been few studies on novel OSA detection techniques, over the past two decades, with a limited set of signals analyzed during PSG procedure. Thus, ECG, PPG, SpO2, and audio signals have been used to assist in the diagnosis of sleep apnea.
For the measurement of respiratory and hemodynamic function, the best opted method is photoplethysmography (PPG). As an alternative to traditional arterial oxygen saturation, pulse photoplethysmography (PPG) signal has been proposed which indicates vasoconstriction changes. In the study by Grote and Zou, the suitability of this signal in monitoring the amplitude changes in airflow and oxygen saturation is tested and compared with other acquisition methods as well as the default apneic event detector. Peripheral arterial tone (PAT) technique is widely used in monitoring the pulsatile changes in the arterial volume. Using PAT technology, a combined algorithm has been proposed by Varon et al. [14] to identify OSA, where the algorithm included the data from pulse wave attenuations, heart rate (HR) responses, and SpO2 levels [15]. By carefully analyzing the PPG signal and identifying the morphological parameters from the shape of the waveform assist in extracting the details such as the duration of the apneic period, the number of recurrences, the variation in heart rate, and breathing rate. Basically, a PPG signal is obtained by illuminating the skin of either the thumb or fore finger and thereby measuring the changes in light absorption. The signal obtained is based on the transmission, absorption, and dispersion of light as it passes through the skin, the underlying tissues, and the blood. The PPG is obtained from a device which utilizes small light emitting diodes (red and near infrared band) transmitting light through the finger to a photodiode. The characteristic parameters extracted from the PPG signal are pulse wave amplitude in both systolic and diastolic, the pulse propagation time, and peak waveform in systolic and diastolic pulse wave. The absorption of light depends purely on the heartbeat, as the blood capillary in the finger contracts and expands with each pulsation of the heart. The signal traces the minimum absorption and the peak absorption of the light intensity, and it is clearly proportional to the cardiac pulse [16]. Most of the previously developed approaches using pulse oximetry signal for screening of OSA are based on percentage oxygen saturation measurement and have been proved to be less accurate as measurement of SpO2 is less accurate at low values [17].
Different case study approaches have been taken on board by the researchers. The diagnostic strategies used for all the acquired signals are relevant to PSG, nasal sounds, breathing movements, abdominal wall movements, chest wall movements, tracheal sounds, and diaphragm movements. These signals are recorded using various devices viz., a microphone, Home Sleep Apnea Testing (HSAT) machine, WatchPat system, and by using sensors for measuring the breathing pattern and accelerometer for recording the breathing movement. The subjects are categorized into several groups based on the BMI (body mass index), age, sex, pre-OSA conditions, sleep disorders, and psychological conditions, including narcolepsy. Table 1 summarizes the different sleep study approaches followed by various researchers. PSG signals contain ECG, EEG, EMG, nasal sounds, and breathing patterns [6, 21, 35]. Figure 2 portrays the distribution of the signal source used in the literature.
Filtering, windowing, and sampling are the most widely used preprocessing techniques. The above section has highlighted different signals that are used for the analysis of sleep apnea. Various filtering methods have been used for effective utilization of the signals for the later stages of signal processing and thereby improve the efficiency of diagnosis. This section spotlights the preprocessing techniques used in the study.
Several other signal processing techniques such as discrete wavelet transform (DWT), empirical mode decomposition (EMD) [78], variational mode decomposition (VMD), and empirical wavelet transform (EWT) for signal denoising and feature extraction have been considered. Of all the comparative methods used for filtering and windowing, simple bandpass filters are more preferred due to its efficiency in preserving information related to OSA, and WT gave a prominent result not only for denoising but also for the extraction of features [67]. 2ff7e9595c
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