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Studies have suggested that the saturated oxygen level within one's arterial blood can provide crucial information about the status of one's cardiopulmonary system. Currently, a popular and convenient approach is to obtain this vital physiological sign through non-invasive measurement on suitable peripheral sites such as a finger, toe or ear lobe. This measurement is known as the SpO2 parameter. It has been increasingly adopted in not only clinical settings but also remote monitoring purposes. In order to measure this optical based parameter, light sources of both the red and infrared wavelengths are required. The most recognized waveform feature is the peripheral pulse or its AC component which is synchronized to each heartbeat. The AC component is superimposed on a constant DC baseline attributed to breathing efforts, sympathetic nervous system activities and thermoregulation. The popularity of the SpO2 parameter may be due to its viable cost, simplicity to build and portability. Moreover, the basic building blocks of a SpO2 based measurement consists of control, filtering and amplification functions that can easily be incorporated with an embedded system. In this review, a brief description of the SpO2 measurement, its normative values and technical issues in its application as a clinical monitor are discussed.

The occlusion of the coronary arteries commonly known as coronary artery disease (CAD) restricts the normal blood circulation required to the heart muscles, thus results in an irreversible myocardial damage or death (myocardial infarction). Clinically, electrocardiogram (ECG) is performed as a primary diagnostic tool to capture these cardiac activities and detect the presence of CAD. However, the use of computer-aided techniques can reduce the visual burden and manual time required for the analysis of complex ECG signals in order to identify the CAD affected subjects from normal ones. Therefore, in this study, a novel computer-aided technique is proposed using 2[Formula: see text]s of 12 lead ECG signals for the identification of CAD affected patients. Each of the 2[Formula: see text]s 12 lead ECG signal beats (3791 normal and 12308 CAD ECG signal beats) are implemented with four levels of wavelet packet decomposition (WPD) to obtain various coefficients. Using the fourth-level coefficients obtained for each lead ECG signal beat, new 2[Formula: see text]s. ECG signal beats are reconstructed. Later, the reconstructed signals are split into two-fold data sets, in which one set is used for acquiring common spatial pattern (CSP) filter and the other for obtaining features vector (vice versa). The obtained features are one by one fed into k-nearest neighbors (KNN) classifier for automated classification. The proposed system yielded maximum average classification results of 99.65% accuracy, 99.64% sensitivity and 99.7% specificity using 10 features. Our proposed algorithm is highly efficient and can be used by the clinicians as an aiding system in their CAD diagnosis, thus, assisting in faster treatment and avoiding the progression of CAD condition.