Optimized porous carbon-fibre microelectrode for multiplexed, highly reproducible and repeatable detection of heavy metals in real water samples.

This work demonstrates the simultaneous identification of four hazardous heavy metals in water samples, namely copper, lead, cadmium, and mercury. A simple yet selective electrode with the simplest fabrication procedure was used. The modified porous carbon threads coated with gold nanoparticles (AuNPs) was employed as a working electrode. The surface chemistry and morphology of the AuNPs deposited porous carbon thread surface were examined. The electrocatalytic activity of the metals on the Au-modified thread surface was observed using the differential pulse voltammetry (DPV) technique. Furthermore, all four metal ions were detected simultaneously, and no interference was observed. Individual and simultaneous experiments to test the impact of concentration revealed that the limit of detection (LoD) was observed to be 1.126 µM, 1.419 µM, 0.966 µM, 0.736 µM for the Cd2+, Pb2+, Cu2+, and Hg2+ metal ions respectively in a linear concentration range of 10-110 µM of each. Subsequently, the study of pH, interference with coexisting metal ions, repeatability study, and stability analysis was also performed. A real sample analysis utilising three different lake water samples is also carried out to further understand the application of the proposed sensor. A good recovery rate is achieved, and the results are reported. This work paves way for the on-field applicability of the present heavy metal detection platform.

Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals.

Heart valve diseases (HVDs) are a group of cardiovascular abnormalities, and the causes of HVDs are blood clots, congestive heart failure, stroke, and sudden cardiac death, if not treated timely. Hence, the detection of HVDs at the initial stage is very important in cardiovascular engineering to reduce the mortality rate. In this article, we propose a new approach for the detection of HVDs using phonocardiogram (PCG) signals. The approach uses the Chirplet transform (CT) for the time-frequency (TF) based analysis of the PCG signal. The local energy (LEN) and local entropy (LENT) features are evaluated from the TF matrix of the PCG signal. The multiclass composite classifier formulated based on the sparse representation of the test PCG instance for each class and the distances from the nearest neighbor PCG instances are used for the classification of HVDs such as mitral regurgitation (MR), mitral stenosis (MS), aortic stenosis (AS), and healthy classes (HC). The experimental results show that the proposed approach has sensitivity values of 99.44%, 98.66%, and 96.22% respectively for AS, MS and MR classes. The classification results of the proposed CT based features are compared with existing approaches for the automated classification of HVDs. The proposed approach has obtained the highest overall accuracy as compared to existing methods using the same database. The approach can be considered for the automated detection of HVDs with the Internet of Medical Things (IOMT) applications.

Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time-Frequency Representation of PCG Recordings.

The heart valve ailments (HVAs) are due to the defects in the valves of the heart and if untreated may cause heart failure, clots, and even sudden cardiac death. Automated early detection of HVAs is necessary in the hospitals for proper diagnosis of pathological cases, to provide timely treatment, and to reduce the mortality rate. The heart valve abnormalities will alter the heart sound and murmurs which can be faithfully captured by phonocardiogram (PCG) recordings. In this paper, a time-frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals. Spline kernel-based Chirplet transform (SCT) is used to evaluate the time-frequency representation of PCG recording, and the features like L1-norm (LN), sample entropy (SEN), and permutation entropy (PEN) are extracted from the different frequency components of the time-frequency representation of PCG recording. The DLKSRN formulated using the hidden layers of extreme learning machine- (ELM-) autoencoders and kernel sparse representation (KSR) is used for the classification of PCG recordings as normal, and pathology cases such as mitral valve prolapse (MVP), mitral regurgitation (MR), aortic stenosis (AS), and mitral stenosis (MS). The proposed approach has been evaluated using PCG recordings from both public and private databases, and the results demonstrated that an average sensitivity of 100%, 97.51%, 99.00%, 98.72%, and 99.13% are obtained for normal, MVP, MR, AS, and MS cases using the hold-out cross-validation (CV) method. The proposed approach is applicable for the Internet of Things- (IoT-) driven smart healthcare system for the accurate detection of HVAs.