Hypertension is an antecedent to cardiac disorders. According to the World Health Organization (WHO), the number of people affected with hypertension will reach around 1.56 billion by 2025. Early detection of hypertension is imperative to prevent the complications caused by cardiac abnormalities. Hypertension usually possesses no apparent detectable symptoms; hence, the control rate is significantly low. Computer-aided diagnosis based on machine learning and signal analysis has recently been applied to identify biomarkers for the accurate prediction of hypertension. This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension. The PuPG signal data set, including rich information of cardiac activity, was acquired from healthy and hypertensive subjects. The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components. A combination of multi-domain features was extracted from the preprocessed PuPG signal. The features exhibiting high discriminative characteristics were selected and reduced through a proposed hybrid feature selection and reduction (HFSR) scheme. Selected features were subjected to various classification methods in a comparative fashion in which the best performance of 99.4% accuracy, 99.6% sensitivity, and 99.2% specificity was achieved through weighted k-nearest neighbor (KNN-W). The performance of the proposed EHDS was thoroughly assessed by tenfold cross-validation. The proposed EHDS achieved better detection performance in comparison to other electrocardiogram (ECG) and photoplethysmograph (PPG)-based methods.

Electrocardiogram (ECG) is the measure of the electrical activity of heart which provides a powerful authentication system for person recognition due to the variation in ECG signatures. In spite of fingerprint biometrics and face detection in past years, biometric system based on ECG is more in pace. In this article a simple method is proposed for person recognition consisting of first denoising the signal by using wavelet decomposition. The denoised signal is then passed through the process of EMD. First two IMFs are selected and from the resulting signal a combination of time domain and frequency domain features are extracted and cubic SVM is trained to get a highest accuracy of 98.4\% with 35 subjects.

Acute Coronary Syndrome (ACS) is one the major reason of increasing mortality rate. Despite medical advances, there isn’t any efficient method that can control this proliferating mortality rate. The major aim of this study is detection of ACS using Pulse Plethysmograph (PuPG) signal analysis. 348 samples of PuPG signal were acquired by fastening PuPG sensor to subject’s index finger. Signal data is preprocessed through Empirical Mode Decomposition (EMD) to remove any possible noise and other artifacts. Extensive experimental analysis was performed to select features having maximum intraclass distance and discriminative power to classify ACS and Normal signals through Support Vector Machines (SVM) classifier. 5-fold cross validation was used to perform training and testing of proposed model using self-collected dataset of PuPG signals. Average accuracy of 99.42%, sensitivity of 99.43% and specificity of 99.41% is obtained through SVM with cubic kernel proving proposed model as a best possible methodology in-terms of cost and efficiency as compared to existing solutions.