Arrhythmia detection and classification using ECG and PPG techniques: a review.

Electrocardiogram (ECG) and photoplethysmograph (PPG) are non-invasive techniques that provide electrical and hemodynamic information of the heart, respectively. This information is advantageous in the diagnosis of various cardiac abnormalities. Arrhythmia is the most common cardiovascular disease, manifested as single or multiple irregular heartbeats. However, due to the continuous manual observation, it becomes troublesome for experts sometimes to identify the paroxysmal nature of arrhythmia correctly. Moreover, due to advancements in technology, there is an inclination towards wearable sensors which monitor such patients continuously. Thus, there is a need for automatic detection techniques for the identification of arrhythmia. In the presented work, ECG and PPG-based state-of-the-art methods have been described, including preprocessing, feature extraction, and classification techniques for the detection of various arrhythmias. Additionally, this review exhibits various wearable sensors used in the literature and public databases available for the evaluation of results. The study also highlights the limitations of the current techniques and pragmatic solutions to improvise the ongoing effort.

AutoIHC-scoring: a machine learning framework for automated Allred scoring of molecular expression in ER- and PR-stained breast cancer tissue.

In prognostic evaluation of breast cancer Immunohistochemical (IHC) markers namely, oestrogen receptor (ER) and progesterone receptor (PR) are widely used. The expert pathologist investigates qualitatively the stained tissue slide under microscope to provide the Allred score; which is clinically used for therapeutic decision making. Such qualitative judgment is time-consuming, tedious and more often suffers from interobserver variability. As a result, it leads to imprecise IHC score for ER and PR. To overcome this, there is an urgent need of developing a reliable and efficient IHC quantifier for high throughput decision making. In view of this, our study aims at developing an automated IHC profiler for quantitative assessment of ER and PR molecular expression from stained tissue images. We propose here to use CMYK colour space for positively and negatively stained cell extraction for proportion score. Also colour features are used for quantitative assessment of intensity scoring among the positively stained cells. Five different machine learning models namely artificial neural network, Naïve Bayes, K-nearest neighbours, decision tree and random forest are considered for learning the colour features using average red, green and blue pixel values of positively stained cell patches. Fifty cases of ER- and PR-stained tissues have been evaluated for validation with the expert pathologist's score. All five models perform adequately where random forest shows the best correlation with the expert's score (Pearson's correlation coefficient = 0.9192). In the proposed approach the average variation of diaminobenzidine (DAB) to nuclear area from the expert's score is found to be 7.58%, as compared to 27.83% for state-of-the-art ImmunoRatio software.

MRF-ANN: a machine learning approach for automated ER scoring of breast cancer immunohistochemical images.

Molecular pathology, especially immunohistochemistry, plays an important role in evaluating hormone receptor status along with diagnosis of breast cancer. Time-consumption and inter-/intraobserver variability are major hindrances for evaluating the receptor score. In view of this, the paper proposes an automated Allred Scoring methodology for estrogen receptor (ER). White balancing is used to normalize the colour image taking into consideration colour variation during staining in different labs. Markov random field model with expectation-maximization optimization is employed to segment the ER cells. The proposed segmentation methodology is found to have F-measure 0.95. Artificial neural network is subsequently used to obtain intensity-based score for ER cells, from pixel colour intensity features. Simultaneously, proportion score - percentage of ER positive cells is computed via cell counting. The final ER score is computed by adding intensity and proportion scores - a standard Allred scoring system followed by pathologists. The classification accuracy for classification of cells by classifier in terms of F-measure is 0.9626. The problem of subjective interobserver ability is addressed by quantifying ER score from two expert pathologist and proposed methodology. The intraclass correlation achieved is greater than 0.90. The study has potential advantage of assisting pathologist in decision making over manual procedure and could evolve as a part of automated decision support system with other receptor scoring/analysis procedure.

Attenuation of the effect of progesterone and 4'-chlordiazepam on stress-induced immune responses by bicuculline.

The present study investigates the effect of progesterone, a pregnane precursor of neurosteroids, and 4'-chlordiazepam (4'-CD), a specific ligand for mitochondrial diazepam binding inhibitor receptor (MDR) involved in neurosteroidogenesis, on restraint stress (RS)-induced modulation of humoral and cell-mediated immune responses. RS produced a significant reduction in anti-sheep red blood cells (SRBC) antibody titre, a measure of humoral immune response, and % leucocyte migration inhibition (LMI) and foot-pad thickness test, measures of cell-mediated immune responses. These effects of RS on immune responses were effectively blocked by pretreating the animals with progesterone (10 mg/kg, sc) or 4'-CD (0.5 mg/kg, sc) administered just before subjecting the animal to RS. The effect of both progesterone and 4'-CD on RS-induced immune modulation was significantly attenuated by bicuculline (2 mg/kg, ip) but not by flumazenil (10 mg/kg, ip). Unlike its effect on RS-induced immune responsiveness, progesterone (5, 10 mg/kg, sc) when administered to non-stressed animals produced a significant suppression of both humoral and cell-mediated immune responses which was not reversed by bicuculline. However, 4'-CD failed to modulate immune response in naive non-stressed animals. These results suggest that progesterone and 4'-CD affect stress-induced immune responses by modulating GABA-ergic mechanism. However, GABA-A receptor system does not appear to be involved in progesterone-induced immunosuppression in nonstressed animals.