Digital Signal Processing A Computer-based Approach 4th Edition Pdf 599 REPACK

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With respect to the signal processing methods that have been employed, the following techniques are discussed. PSG, and other related signals are transformed into a digital signal to extract the useful information for automatic apneic event detection. The acquired signals are filtered, transformed, normalized, and processed to derive features of interests. The various signal processing approaches used for apneic event detection are listed in Table 3.

Overall, the novelty of the paper is in the review of the literature on different diagnostic tools employed by the researchers for OSA detection and the discussion of signal acquisition techniques. Moreover, the paper also provides an in-depth discussion about the pattern of signal processing methods used for deriving the features of interest and their classification strategies.

Table 4 gives a broad overview of the classification techniques used for apneic event detection. The literature survey has revealed that there are several cases of studies involved in the automatic OSA detection, such as, PSG signals, ECG signals, EEG signals, respiratory signals, and muscle movement signals.

In the last half of the paper, we discuss the tools used by the researchers to record the signals. The table depicted in the following paragraph states the sensors used in recording the signals.

For a review on the physiological signals used for OSA detection, the following signal sources are considered and discussed in the paper. The data are from the European Respiratory Society (ERS) Task Force report on automated detection of OSA [3]. Please refer to Table 2 for details on the physiological signals taken into consideration.

Moreover, the various signal classifiers used are discussed in the following section. A few case studies on the discrimination of OSA and normal cases are also taken into consideration in the paper.

The stochastic gradient descent support vector machine (SGD-SVM) method is proposed to extract the features of EEG signals to be used for sleep apnea detection. The aim was to reduce the computational complexity of the system. This algorithm employs a stochastic process to obtain the required number of training samples for the training data set. Therefore, it is called the sample efficient SVM (SSVM) method [7].

The study investigates the performance of a wearable sensor based on a single light-emitting diode (LED) to detect the apnea events in PSG signals. A machine learning algorithm is used to select the best feature for apnea detection. The study highlights the significance of the features selected by an SVM classification to achieve a reliable decision. The study also provides an insight into the performance of an apnea detector based on the LED sensor [88]. 827ec27edc